CN118786395A - Material handling system and method - Google Patents

Abstract

A material handling system for handling and placing packages onto trays destined for an order store, comprising a storage array, an automated package transport system, an automated palletizer, and a controller operatively connected to the automated palletizer, the controller programmed with a tray load generator having at least one tray-to-order store affinity characteristic, a predetermined method for tray load package delivery at the order store, the tray load generator configured such that the tray load is formed by the automated palletizer disposed in a tray load embodying the at least one tray-to-order store affinity characteristic.

Description

Material handling system and method

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Patent Application No. 63/288,253, filed on December 10, 2021, and is a non-provisional application thereof, the disclosure of which is incorporated herein by reference in its entirety.

Background Art

1. Technical Field

The present disclosure relates generally to material handling systems and, more particularly, to handling freight with a material handling system and placing the freight on pallets.

2. Brief description of relevant developments

A warehouse or distribution center for goods generates pallets of goods for various customers, where such customers include, but are not limited to, retail stores. Each of the various customers orders goods, which orders are fulfilled by the warehouse or distribution center by loading the ordered goods onto one or more pallets. Each of the various customers may have their own preferred way of depalletizing the goods ordered from the warehouse or distribution center to facilitate restocking of those goods on store shelves.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing aspects and other features of the present disclosure are explained in the following description considered in conjunction with the accompanying drawings, in which:

FIG1 is an exemplary schematic illustration of a warehouse or distribution center incorporating aspects of the present disclosure;

FIG2 is an exemplary schematic illustration of pallet load package delivery according to aspects of the present disclosure;

FIG3 is an exemplary schematic illustration of pallet load package delivery according to aspects of the present disclosure;

FIG4 is an exemplary schematic illustration of pallet load package delivery according to aspects of the present disclosure;

FIG5 is an exemplary schematic illustration of an order for pallet planning according to aspects of the present disclosure;

FIG. 6 is an exemplary illustration of a tray-aisle binary matrix according to aspects of the present disclosure;

FIG. 7 is an exemplary method according to aspects of the present disclosure;

FIG8 is an exemplary illustration of a planned order according to aspects of the present disclosure;

FIG. 9 is an exemplary illustration of a pallet to aisle selection process according to aspects of the present disclosure;

FIG. 10 is an exemplary illustration of case unit delivery for a pallet load according to aspects of the present disclosure;

FIG. 11 is an exemplary illustration of case unit delivery for a pallet load according to aspects of the present disclosure;

12A and 12B are diagrams of exemplary methods according to aspects of the present disclosure;

FIG13 is a diagram of an exemplary method according to aspects of the present disclosure;

FIG14 is a diagram of an exemplary method according to aspects of the present disclosure;

FIG15 is a diagram of an exemplary method according to aspects of the present disclosure;

FIG. 16 is a diagram of an exemplary method according to aspects of the present disclosure; and

17 is a chart illustrating the variation in box dimensions within a representative group of boxes.

DETAILED DESCRIPTION

FIG. 1 illustrates an exemplary warehouse or distribution center 199 (generally referred to herein as warehouse 199) according to aspects of the present disclosure. Although aspects of the present disclosure will be described with reference to the accompanying drawings, it should be understood that aspects of the present disclosure may be embodied in a variety of forms. In addition, any suitable size, shape or type of element or material may be used.

Aspects of the present disclosure are generally applied to warehouse systems, where pallet loads (such as those described herein and collectively referred to as (one or more) pallet loads PALO) are built by automated machines, such as robotized palletizers 162, 162', according to a pallet plan generated by a controller. However, aspects of the present disclosure may also be applied to manual pallet construction, where a pallet load generator (such as described herein) outputs an itemization of case units CU to be included on a pallet (according to the present disclosure), where human workers build pallets of case units CU with predetermined details based on warehouse rules and previous work experience. According to the present disclosure, aspects of the present disclosure may also be applied to manual warehouses, where pallet plans are computer-generated and output in a tangible form (e.g., video monitors, graphical user interfaces, smart devices such as phones and tablet devices, paper instructions, etc.) in an advisory role that human workers will follow in order to build the pallets described herein. Here, the goods included in the pallet load PALO are delivered to human workers by conveyors, mobile robots, or other suitable transportation vehicles in a predetermined sequence inferred from the pallet plan.

According to the present disclosure, each pallet load PALO is planned using any suitable calculation method, including but not limited to those described in U.S. Pat. No. 8,965,559 issued on February 24, 2015 and U.S. Pat. No. 9,969,572 issued on May 15, 2018, the disclosures of which are incorporated herein by reference in their entirety. As used herein, a "planned pallet" or "planned pallet load" is a pallet load having an alist of goods (e.g., individual items, boxes, totes, standard packages, etc. as described herein and generally referred to as case units CU), which has coordinates (X, Y, Z - see Figure 1) assigned to the corners of the goods, which have coordinates close to the origin of the pallet coordinate system (X = 0, Y = 0, Z = 0). The orientation of the goods along the X, Y, Z axes has values such as length, width, height, or width, length, height for goods that cannot tilt to one side. Additional values may be provided for cargo that may be placed on the surface on either side of the cargo. These additional values include, for example, length, height, width or width, height, length or height, length, width or height, width, length. A pallet plan is a physically valid plan in which (1) the cargo do not intersect in physical space, (2) each cargo is stably supported by other cargo or the pallet base, (3) no portion of any cargo is located outside the predetermined boundaries of the pallet's outer dimensions Lp, Wp, Hp (or the predetermined volume Vp of the pallet load defined by the outer dimensions Lp, Wp, Hp), and (4) the total weight of the cargo on the pallet does not exceed a predetermined maximum weight Wmax of the pallet load PALO.

Also according to the present disclosure, a "planned order" is a list of planned pallet numbers such that all ordered case units CU belong to some pallet in the list and there are no case units CU that do not belong to any pallet load. Note that consecutive case units CU in the order list do not have to be assigned to the same or consecutive pallet loads. For example, case unit number 1 can be assigned to pallet load number 5, while case unit number 2 can be assigned to pallet load number 3.

It is also noted that the case unit CU may have an integer value of the "product group type" to which the case unit CU belongs within the retail store. For example, retail stores typically assign predetermined relationships between these product group types and the physical locations within the store where the product group types are located (e.g., aisles, departments, sections, etc.). As used herein, the product group types and the corresponding physical locations within the retail store are typically referred to as "aisles." Note that an aisle is an aisle within a retail store and is not to be confused with the (distribution center) storage/picking aisles of the storage array 130 of the (distribution center) material handling system 190. Here, the retail store aisles (of the storage array 130) and the distribution center picking aisles are completely decoupled from each other. It is also noted that the retail store aisles are referred to with numerical labels ranging from 1 to n (e.g., aisle 1, aisle 2, ..., aisle n), where n is an integer value indicating the predetermined highest aisle number for a given store. Although aisles may be numbered, the positions of aisles in a store may not be continuous. According to aspects of the present disclosure, for the pallet load parcel delivery method described herein, box units CU belonging to a common (e.g., same) aisle (e.g., physical location/aisle and/or product group type) are assigned to a common pallet (unless otherwise specified).

In one aspect, closely numbered aisles in a retail store (e.g., such as aisles 34 and 35) can be physically close to each other in space. In this aspect, the present disclosure can optimize the products placed on a given pallet by combining products from physically close aisles (e.g., aisles 34 and 35) on a common pallet, rather than combining products from physically separate aisles (e.g., such as aisles 34 and 73).

In other aspects, the relationship between aisle numbers and the spatial proximity of aisles may be more complex than adjacent aisle numbers that are physically adjacent in space (e.g., aisles 34 and 35). For example, adjacent or close aisle numbers (e.g., aisle 20 and aisle 21) may not mean that the aisles are physically close to each other in space (e.g., aisle 20 may be located at one end of a retail store, while aisle 21 may be located at the opposite end of the retail store). Here, as described herein, a pairwise relationship between two aisles may be provided with respect to the allocation of case units to pallet loads. For example, according to aspects of the present disclosure, a pairwise relationship between two aisles is in the form of coefficients A[i, k] for aisle i and aisle k. This pairwise relationship describes not only the physical proximity between the two aisles, but also the preference of the retail store to keep products from these aisles on one pallet or separate pallets based on the retail store business logic other than, for example, distance-based unloading optimization. An example of such business logic may be the separation of corrosive products (e.g., laundry detergent) and food items (e.g., baby food), which are preferably transported on separate pallet loads.

Aspects of the present disclosure may also be applicable to any suitable volume of product in any given aisle. For example, some aisles may have a total volume of case units that is much larger than the volume of a single pallet (e.g., see volume V2 of aisle 2 in FIG. 5 ). Here, aspects of the present disclosure allocate the volume of the case unit to the entire pallet load until the remaining volume of the case unit does not fill the entire pallet load. Here, the remaining volume of the case unit is allocated to the pallet in accordance with the parcel delivery method described herein. As another example, the volume of the case unit for other aisles may be several case units or even a single case unit, in which case, these case units are allocated to the pallet load in accordance with the parcel delivery method described herein.

Referring also to FIGS. 2-4 , as will be described herein, the material handling system 190 of the warehouse 199 is configured to implement optimization of an automated process for planning and building a mixed product order 299 (see, e.g., FIG. 2 ) to be delivered to, for example, a retail store (or other suitable customer to which the goods are delivered on a pallet). The retail store that places the order is referred to herein as an order store 200 (see, e.g., FIGS. 2-4 ). Each of the one or more pallet load PALOs in the mixed product order 299 is built by the material handling system 190 such that each pallet load PALO is a “store-friendly pallet” or “store-friendly pallet load.” Here, “store-friendly” means that the pallet load PALO is configured for convenient and efficient unloading and delivery to store shelves. For descriptive purposes only, "store-friendly" refers to a store affinity of a pallet load or a pallet load store affinity such that a pallet load configuration (i.e., a pallet load build) includes predetermined characteristics (or factors) of the store affinity that bias or factor the resolution of each pallet load PALO to comply with and provide each resulting pallet load PALO with retail store characteristics that are in accordance with or understand the predetermined characteristics of the retail store, as will be described herein. For example, when the fulfilled mixed product order 299 (see Figures 2-4) (one or more) pallet load PALOs arrive at the order store 200, the pallet load (one or more) PALOs (e.g., pallet load PALOC in Figure 2, PALOA, PALOA' in Figure 3, and PALOC, PALOC' in Figure 4) are quickly unloaded (e.g., such as in accordance with "just in time" inventory practices) and their goods are delivered (e.g., restocked/inventoryed) to the store shelves 233 with minimal disruption to store operations. To facilitate rapid unloading and delivery of goods to store shelves 233, the material handling system 190 is configured to construct (one or more) pallet load PALOs so that the structure of the goods CUs (also referred to herein as packages, products, box units, mixed boxes, boxes, shipping boxes, and shipping units) on (one or more) pallet load PALOs are grouped in a manner similar to the way the goods CUs are delivered to store shelves 233.

Each warehouse customer (e.g., order store 200) of warehouse 199 may have its own preferences with respect to the handling of pallet loads within order store 200. Aspects of the present disclosure provide for the construction of store-friendly pallets that correspond to the different ways that warehouse customers handle pallet loads and deliver products.

Referring to FIG. 2 , an exemplary manner of handling a pallet load PALO may be referred to as a “cluster aisle pallet load parcel delivery method” and includes deconstructing/downstack (one or more) pallet loads PALOC at a loading dock area 222 (or other suitable area) of an order store, and placing goods CU belonging to different sections of the order store 200 on two or more separate secondary pallets PAL21-PAL23 (three secondary pallets are shown in FIG. 2 for exemplary purposes only). These secondary pallets PAL21-PAL23 include goods CUs assigned to predetermined shopping aisles, and are moved to the corresponding predetermined shopping aisles for unloading (see FIG. 2 ). With the secondary pallets PAL21-PAL23 in the corresponding shopping aisles, the goods CUs from the secondary pallets PAL21-PAL23 are delivered to the assigned shelves 233.

Referring to FIG. 3 , another example of handling a pallet load PALO may be referred to as an “adjacent aisle pallet load package delivery method” and includes moving the entire pallet load PALOA, PALOA’ (e.g., without downward stacking of pallets) into a shopping aisle. With the pallet load PALOA, PALOA’ in the shopping aisle, the cargo CU is substantially delivered directly from the (one or more) pallet loads PALOA, PALOA’ to the assigned shelf 233 (see FIG. 3 ). Here, the cargo is arranged on the (one or more) pallet loads PALOA, PALOA’ so as to minimize the travel distance of each pallet load PALOA, PALOA’ within the store and substantially avoid the pallet PALOA, PALOA’ returning to an aisle that the corresponding pallet has previously visited (e.g., the pallet only passes through the aisle once along the predetermined path 301, 302). The cargo CU may be arranged on the pallet loads PALO, PALOA’ according to the travel path 300, 302 of the corresponding pallet load PALOA, PALOA’ through the shopping aisle.

Referring to FIG. 4 , yet another example of handling pallets PALO may be referred to as a “mixed-mode cluster and adjacent-aisle pallet load parcel delivery method” and includes a combination of the above processing methods. Referring to FIG. 4 , pallet loads PALOC, PALOC’ arrive at an order store 200 in a truck (or other suitable means of transport) from a warehouse/distribution center 199. The pallet loads PALOC, PALOC’ are moved (without stacking the pallets down) to a shopping area generally near the shelf to which the goods CU on the pallet loads PALOC, PALOC’ are assigned. As the pallet loads PALOC, PALOC’ are generally located near the assigned shelf, the pallet loads PALOC, PALOC’ are stacked down into the corresponding secondary pallets PALO21, PALO22, PALO23, PALO21’, PALO22’ assigned to the corresponding shopping aisles. Here, the pallet loads PALOC, PALOC' are constructed so that each pallet load PALOC, PALOC' includes goods belonging to/assigned to store aisles that are close to each other (for example, the pallet load PALOC includes goods located in aisle 1, aisle 2 (adjacent to aisle 1), and aisle 4 that is only one aisle away from aisle 2; similarly, the pallet load PALOC' includes goods belonging to/assigned to adjacent aisles 12 and 13). The goods CU can also be arranged in the corresponding pallet loads PALOC, PALOC' so that the pallet structure corresponds to the way the goods are stacked down to the corresponding secondary pallets (for example, such as sequential downward stacking, where, for example, the goods assigned to the secondary pallet PALO21 are at the top of the pallet structure of the pallet load PALOC, the goods assigned to the secondary pallet PALO22 are in the middle of the pallet structure of the pallet load PALOC, and the goods assigned to the secondary pallet PALO23 are at the bottom of the pallet structure of the pallet load PALOC). In this aspect, the aisles to which the goods CU are assigned may not be arranged along corresponding specific paths (see, for example, paths 301, 302 in FIG. 3) for unloading the goods CU of corresponding pallet loads PALO, PALO onto store shelves.

The above examples of pallet handling/down stacking methods in the order store 200 are merely exemplary. Note again that the pallet loads PALOC, PALOC', PALOA, PALOA' used for each pallet handling/down stacking method are generally referred to herein as pallet loads PALO. Note also that the pallet load(s) PALO can be constructed by the material handling system 190 in any suitable manner so that the goods on the pallet load(s) PALO are arranged according to any suitable at least one order pallet to order store affinity characteristics 166, 166' of the pallet load parcel delivery method described herein. Note that the store affinity pallet load resolution (as described herein) is decoupled from the storage array 130 layout and the case CU throughput of the material handling system 190 to the palletizer 162. Here, the output of the case CU from the storage array 130 by the material handling system 190 is selected to comply with or otherwise depend on (based on) the store affinity pallet load resolution. In one or more aspects, the throughput of the box CU output by the material handling system 190 can be achieved in a manner similar to that described in U.S. patent application Ser. No. 17/091,265, filed on November 6, 2020 and entitled “Pallet Building System with Flexible Sequencing,” the disclosure of which is incorporated herein by reference in its entirety. According to aspects of the present disclosure, the arrangement of the box CUs within the storage array 130 can be freely optimized for optimal throughput separately from the parsing and construction of store affinity pallet loads PALO. An example of throughput optimization can be found in U.S. Patent No. 9,733,638, issued on August 15, 2017 and entitled “Automated Storage and Retrieval System and Control System Thereof,” the disclosure of which is incorporated herein by reference in its entirety.

With reference to FIG. 1 , a material handling system 190 may be provided in a retail distribution center or warehouse 199, for example to fulfill orders received from retail stores (e.g., order stores 200—see FIGS. 2-4 ) for replenishing goods shipped in boxes, packages, and/or packaging. The terms box, package, and package are used interchangeably herein, and as noted above, may be any container that may be used for shipping and may be filled with one or more product units by a manufacturer. One or more boxes as used herein means a box, package, or packaging unit that is not stored in a standard package, on a tote, or the like (e.g., not contained). Note that a box unit CU may include a box of items/units (e.g., boxes of soup cans, cereal boxes, etc.) or an individual item/unit suitable for removal from a pallet or placed on a pallet. According to the present disclosure, a box unit (e.g., a carton, barrel, box, crate, can, standard packaging or group of shrink-wrapped goods, or any other suitable device for holding goods) may have a variable size and may be used to hold goods in shipment, and may be configured so that they can be palletized for shipment. A case unit CU may also include one or more totes, boxes, and/or containers of individual shipments that are unpacked/decommissioned from original packaging (commonly referred to as breakpack shipments) and placed into totes, boxes, and/or containers (collectively referred to as totes) at an order filling station along with one or more other individual shipments of a mixed or common type. Note that, for example, when incoming bundles or pallet loads PALN (e.g., from a manufacturer or supplier of case units) arrive at the material handling system 190 to replenish shipments stored within the storage array 130 of the material handling system 190, the contents of each pallet load PALN may be consistent (e.g., each pallet holds a predetermined number of the same items—one pallet holds soup and another pallet holds cereal). As may be appreciated, the boxes of such a pallet load PALN load may be substantially similar, or in other words, homogeneous boxes (e.g., similar size) and may have the same SKU (otherwise, as previously noted, the pallet may be a "rainbow" pallet having layers formed of homogeneous boxes).

When a pallet load PALO leaves the material handling system 190 with cases or totes filled with store replenishment orders, the pallet load PALO may contain any suitable number and combination of different case units (e.g., each pallet may hold a different type of case unit - a pallet holds a combination of canned soup, cereal, beverage packets, cosmetics, and household cleaners). The cases combined onto a single pallet may be of different sizes and/or different SKUs.

The material handling system 190 generally includes a storage array 130 and an automated parcel transport system 195. The storage array 130 includes a storage space 130S for holding case units CU therein. The automated transport system 195 is communicatively connected to the storage array 130 for storing the case units CU in the storage space 130S of the storage array 130 and for retrieving the case units CU from the storage space 130S of the storage array 130.

The automated palletizers 162, 162' include automated parcel picking equipment 162D (e.g., a robotic arm, a gantry picker, etc.) that is capable of moving case units CU from a parcel storage section (such as an out-feed transfer station 160) to a pallet (also referred to herein as a pallet base) to form a pallet load PALO from the case units CU, wherein the pallet load PALO includes more than one composite layer L1-Ln of the case units CU. As described herein, the more than one composite layer L1-Ln of the case units CU are formed by the case units CU arranged in the pallet load PALO, which embodies at least one pallet-to-order store affinity characteristic 166, 166' (see Figures 2-4) of a predetermined method for pallet load parcel delivery at the order store 200. The automated palletizers 162, 162' are communicatively connected to the automated parcel transportation system 195. The automated parcel transport system 195 provides individual case units CU from the storage array 130 to the automated palletizer 162 for forming a pallet load PALO, wherein the pallet load PALO includes more than one composite layer L1-Ln of case units CU. The individual case units CU from the storage array 130 from which the pallet load PALO is constructed have case dimensions (e.g., any one or more of case length, case width, and case height), wherein the (one or more) case dimensions have a substantially Gaussian distribution or substantially random probability, as represented by the normal probability curve illustrated in FIG17. FIG17 is a graph illustrating the variation of case dimensions (e.g., length, height, and width) within a representative population of case CUs such as may be found in the material handling system 190 and used to generate a mixed case pallet load PALO (as described herein) based on a customer replenishment order. As can be appreciated, an order may result in a mixed case pallet load PALO including many cases having sizes from various portions of the size spectrum illustrated in FIG17.

The controller 164, 164' is operably connected to the automated palletizer 164. The controller 164, 164' is programmed with a non-transitory computer program code that defines a pallet load generator 165, 165' having at least one pallet to order store affinity characteristic 166, 166' (as will be described herein) for a predetermined method of pallet load PALO case unit CU delivery at the order store 200. As described herein, the pallet load generator 166, 166' is configured so that a pallet load PALO is formed by the automated palletizer 162 of the case units CU arranged in the pallet load PALO, and the pallet load PALO embodies at least one pallet to order store affinity characteristic 166, 166'.

1, the material handling system 190 can be configured for installation in, for example, an existing warehouse structure or adapted to a new warehouse structure. As previously noted, the material handling system 190 shown in FIG. 1 is representative and can include, for example, infeed and outfeed conveyors terminating at respective infeed and outfeed transfer stations 170, 160 (e.g., transferring case units from and to respective depalletizers 162' and palletizers 162), lift module(s) 150A, 150B, a storage array 130 (e.g., including suitable structures such as display racks, vehicle riding surfaces, storage shelves, etc.), and a plurality of autonomous transport vehicles 110 (also referred to herein as "bots").

Note that the material handling system 190 is formed by at least the storage array 130 and the robot 110. In some aspects, the lift modules 150A, 150B also form part of the material handling system 190; however, in other aspects, the lift modules 150A, 150B may also form a vertical sequencer in addition to the material handling system as described in U.S. patent application Ser. No. 17/091,265, filed on Nov. 6, 2020 and entitled “Pallet Building System with Flexible Sequencing,” the disclosure of which is incorporated herein by reference in its entirety. In alternative aspects, the material handling system 190 may also include a robot or robot transfer station 140 that may provide an interface between the robot 110 and the lift module(s) 150A, 150B.

The storage array 130 includes any suitable structure of a plurality of (stacked) storage levels 130L1-130Ln (see FIG. 1 , generally referred to as storage levels 130L or storage levels 130L, and where n is an integer indicating the upper limit number of storage levels present in the material handling system 190) forming storage display rack modules, wherein each level 130L includes a corresponding picking aisle 130A, a storage space 130S, and a transfer platform (deck) 130B for transferring box units between any storage space 130S and shelves of (one or more) lift modules 150A, 150B of the storage structure 130. The storage spaces 130S are arranged along (or along) one or more sides of each picking aisle 130A, so that the robot 110 traveling along the picking aisle 130A has access to the storage spaces 130S on either side of the picking aisle 130A.

In one aspect, the picking aisle 130A is configured to provide guided travel of the robot 110 (such as along a vehicle riding surface VRSR that includes robot guidance features, such as tracks), while in other aspects, the picking aisle is configured to provide unconstrained travel of the robot 110 (e.g., along a vehicle riding surface VRSU that is open and indeterminate relative to robot 110 guidance/travel). The transfer platform 130B has an open and indeterminate robot-supported travel surface VRS along which the robot 110 travels under guidance and control provided by robot steering (e.g., such steering is achieved by one or more of differential drive wheel steering, steerable wheels, etc.). In one or more aspects, the transfer platform 130B has multiple lanes between which the robot 110 freely transitions to access the picking aisle 130A and/or lift modules 150A, 150B. The picking aisle 130A and the transfer platform 130B also allow the robot 110 to place case units CU into the picking inventory and retrieve the ordered case units CU. In an alternative aspect, each storage level 130L may also include a corresponding robot transfer station 140, which provides a case unit transfer interface between the robot 110 and (one or more) lift modules 150A, 150B.

The robot 110 can be configured to place case units CU such as the above-mentioned retail goods into the picking inventory in one or more levels 130L of the storage array 130 and then selectively retrieve the ordered case units CU for shipping the ordered case units CU to, for example, the order store 200 (see, for example, Figures 2-4) or other suitable locations.

The infeed transfer station 170 and the outfeed transfer station 160 may operate with their respective lift module(s) 150A, 150B for bidirectional transfer of case units CU to and from one or more levels 130L of the storage structure 130. Note that while the lift modules 150A, 150B may be described as dedicated inbound lift modules 150A and outbound lift modules 150B, in alternative aspects, each of the lift modules 150A, 150B may be used for both inbound and outbound transfer of case units from the material handling system 190. Similarly, while the palletizers 162, 162′ may be described as dedicated (inbound) depalletizers 162′ and (outbound) palletizers 162, in alternative aspects, each of the palletizers 162, 162′ may be used for both inbound and outbound transfer of case units from the material handling system 190.

As can be appreciated, the material handling system 190 may include a plurality of infeed and outfeed lift modules 150A, 150B accessible by, for example, a robot 110 of the material handling system 190, such that one or more uncontained (e.g., case unit(s) not held in a standard package) or contained (within a standard package or tote) case units may be transferred from the lift modules 150A, 150B to each storage space 130S on a corresponding level 130L and from each storage space 130S to any one of the lift modules 150A, 150B on a corresponding level 130L. The robot 110 may be configured to transfer case units between a storage space 130S (e.g., located in a picking aisle 130A or other suitable storage space/case unit buffer disposed along a transfer platform 130B) and the lift modules 150A, 150B. Typically, the lift modules 150A, 150B include at least one movable payload support that can move (one or more) box units between the infeed and outfeed transfer stations 160, 170 and the corresponding level 130L of the storage space 130S that stores and retrieves (one or more) box units CU. The (one or more) lift modules can have any suitable configuration, such as, for example, a reciprocating lift or any other suitable configuration. The (one or more) lift modules 150A, 150B include any suitable controller (such as the controller 120 or other suitable controllers coupled to the controller 120, the warehouse management system 2500 and/or the palletizer controller 164, 164'), and can form a sequencer or sorter in a manner similar to that described in U.S. Patent Application No. 16/444,592 filed on June 18, 2019 and entitled "Vertical Sequencer for Product Order Fulfillment" (the disclosure of which is incorporated herein by reference in its entirety).

The material handling system 190 may include a control system including, for example, one or more control servers 120 that are communicatively connected to the infeed and outfeed conveyors and transfer stations 170, 160, lift modules 150A, 150B, and robot 110 via a suitable communication and control network 180. The communication and control network 180 may have any suitable architecture that may incorporate, for example, various programmable logic controllers (PLCs), such as for commanding the operation of the infeed and outfeed conveyors and transfer stations 170, 160, lift modules 150A, 150B, and other suitable system automation. The control servers 120 may include advanced programming that implements a case management system (CMS) that manages the case flow through the material handling system 190.

The network 180 may further include appropriate communications for implementing a two-way interface with the robot 110. For example, the robot 110 may include an onboard processor/controller 1220. The network 180 may include an appropriate two-way communication package that enables the robot controller 1220 to request or receive commands from the control server 120 to implement the desired transportation of the case unit CU (e.g., into or retrieved from a storage location) and to send the desired robot 110 information and data to the control server 120, including the robot 110 ephemeris, status, and other desired data.

1, the control server 120 may be further connected to a warehouse management system 2500 for providing, for example, inventory management and customer order fulfillment information to the CMS 120 level programs. A suitable example of a material handling system arranged to hold and store case units is described in U.S. Pat. No. 9,096,375, issued Aug. 4, 2015, the disclosure of which is incorporated herein by reference in its entirety.

With reference to FIGS. 1-5 , constructing a pallet load PALO based on at least one pallet to order store affinity characteristic will be described in more detail with respect to aspects of the present disclosure. As noted above, the at least one pallet to order store affinity characteristic 166, 166′ is at least one of a clustered aisle pallet load parcel delivery method (see, e.g., FIG. 2 ), an adjacent aisle pallet load parcel delivery method (see, e.g., FIG. 3 ), and a mixed mode clustered and adjacent aisle pallet load parcel method (see, e.g., FIG. 4 ) (e.g., a predetermined customer affinity). The at least one pallet to order store affinity characteristic 166, 166′ may be stored in any suitable memory, such as a memory of the control server 120 and/or the palletizer 162, 162′ as described herein, and used by the control server 120 and/or the palletizer 162, 162′ to generate the pallet load PALO described herein. Unless otherwise noted, the term aisle as used hereinafter refers to the order store aisle to which the pallet load PALO is directed.

Referring to Figures 1 and 5, an exemplary diagram of a sample order for pallet planning (see Figure 5) is illustrated and includes case units from one or more aisles of the order store 200 (Figures 2-4). In the exemplary sample order shown in Figure 5, the aisles are numbered 1-12. The total volume V1-V12 of the case units CU ordered for each corresponding aisle 1-12 is represented by the height of the corresponding bar in the figure (each bar corresponds to the corresponding aisle 1-12). The volume V1-V12 is illustrated as a fractional unit of the expected total volume Vp of the case unit CU relative to a full pallet load (e.g., a full pallet load has the maximum pallet load dimensions in length Lp, width Wp, and height Hp). The volume Vp is the product of the maximum dimensions Lp (length), Wp (width), Hp (height) (e.g., the space allocated for the case unit CU on the pallet load) multiplied by the expected volume efficiency E of the packaged product on the pallet load, where:

Vp＝Lp＊Wp*Hp*E [Equation 1]

Typically, the dimensions of the cargo/box unit CU (e.g., length, width, height) are known, with the box unit CU having a roughly cuboid shape. Here, the known dimensions of the box unit provide for the determination of the total volume Vp of the box unit CU (e.g., the combined volume of the box units CU assigned to any one given pallet load). As an example and depending on the calculation method used to plan individual pallet loads, the average total product volume on the pallet is statistically approximately 0.8, with a standard deviation of 0.03 for the volume of the outer limits of the pallet load having dimensions Lp (length) x Wp (width) x Hp (height) (e.g., approximately 80% of the pallet volume is occupied by cargo, and the remainder is empty space between the cargo). The expected efficiency E depends on the packaging algorithm of the calculation method (such as those described herein) and should generally exceed a value of approximately 0.8 for packaging algorithms of the prior art (such as those of the calculation method described herein) and for mixed products containing boxes of multiple sizes.

Referring generally to FIG. 5 , it can be seen that some of the aisles 1-12 (see, e.g., aisle 2) may have a total volume (such as volume V2 of aisle 2) that exceeds an expected (e.g., maximum) volume Vp of a pallet load PALO. Other aisles 1-12 may have corresponding volumes that are small or smaller (see volume V9 of aisle 9) than the expected total volume Vp. As described herein, according to aspects of the present disclosure, a pallet load generator 165, 165′ (e.g., of a control server 120 and/or a palletizer 162, 162′) is configured to parse the pallet load PALO based on at least one pallet-to-order-store affinity characteristic 166, 166′ such that the pallet load PALO is one or more of the following:

with respect to maximizing at least one of a maximum pallet load volume Vp and a maximum pallet load weight Wmax,

From the smallest number of store aisles with the largest number of packages,

Generate a pallet load with a minimum number of items for each store order,

is generated so that for each pallet load going to the order store 200, the case units CU forming the pallet load represent the minimum quantity for the order store aisle, and

is generated such that for each pallet load going to the order store 200, the parsed pallet load represents the minimum quantity for the order store aisle.

With reference to Figures 1, 2, 5, 6, 7, 8 and 12, the pallet to order store affinity features 166, 166' for the clustered aisle pallet load package delivery method will be described in more detail. The clustered aisle pallet load package delivery method minimizes both (1) the number of pallets created from a given set of products and (2) the average per-aisle pallet ratio RPA. The per-aisle pallet ratio RPA is determined as the total count of product instances from each aisle on each pallet divided by the total number of aisles. The per-aisle pallet ratio RPA can be understood as the number of times a pallet load PALO will be present in any aisle, or the number of aisles in which a pallet load PALO is present to be unloaded. This number is sought to be minimized (e.g., brought to 1).

The per-aisle pallet ratio RPA can be represented by a pallet-aisle binary matrix PA as illustrated in FIG6 . Here, the pallet-aisle binary matrix PA has a number of rows equal to the number of aisles (eight aisles are illustrated for exemplary purposes) and a number of columns equal to the number of pallets planned for a given order (four pallets are illustrated for exemplary purposes). The element PA[i, j] of the pallet-aisle binary matrix at row (i) and column (j) is equal to 1 if a product from aisle (i) is present in pallet (j), otherwise the element PA[i, j] of the pallet-aisle binary matrix at row (i) and column (j) is equal to 0. The per-aisle pallet ratio RPA is determined as the sum of all elements of the pallet-aisle binary matrix PA divided by the number of aisles with products present in the order. Note that the per-aisle pallet ratio RPA is equal to 1 if each aisle is present in only one pallet. For more products from a number of aisles spread across several pallets, the per-aisle pallet ratio RPA is higher. In the case where products from each aisle are present in each pallet, then the pallet ratio per aisle RPA is equal to the number of pallets. In the example illustrated in FIG6 , the pallet ratio per aisle RPA is equal to 11/8 or 1.375. Here, the pallet ratio per aisle RPA is greater than 1 because products from aisle 1 are present in pallets 1 and 3, products from aisle 6 are present in pallets 2 and 4, and products from aisle 8 are present in pallets 1 and 4.

In a clustered aisle pallet load parcel delivery method, all single-aisle pallets are planned for aisles with a volume of case units CU that exceeds an expected pallet volume Vp or a maximum pallet weight Wmax, as will be described in more detail herein. The remaining pallets used to fill store orders are planned based on a combination of aisles, where such a plan is determined using a repeated double loop, such as illustrated in FIG. 12A , where for each aisle combination iteration IAi (e.g., nested within a wider pallet build iteration Pj), the pallets are planned so that the volume Vc(IAi) is maximized relative to the expected pallet volume Vp (e.g., so as to minimize the number of pallets in the order) and the per-aisle pallet ratio RPA is minimized (e.g., so as to approach 1). Here, the repeated double loop determination iterates through the combinations of aisles until the pallet being planned is successfully planned (as described in more detail below), and the pallets are iterated through until the entire store order is consumed (e.g., the order is filled) and there are no more unplanned (i.e., not assigned to the pallet load) case units CU in the store order. Here, the order-store affinity characteristics 166, 166' are informed by a repeated double loop determination, at least one of which relates order-store aisles to one another. At least the other of the repeated double loop determinations determines the available combinations of order-store aisles, thereby resolving the placement of case units or parcels CUs in a given pallet load PALO. The repeated double loop determinations are illustrated, for example, in FIG7 and FIG12B, and will be described below with respect to pallet load construction according to the order-store affinity characteristics for a clustered aisle pallet load parcel delivery method.

The warehouse management server 2500 or the control system 120 (or any other suitable controller of the warehouse 199) receives the store order (FIG. 12B, block 1200). In the case where the warehouse management server 2500 receives the store order, the store order is transmitted to the control server 120 via the network 180 or in any other suitable manner. The control server 120 commands the automated package transport system 195 to retrieve the ordered goods from the storage array 130 for transport to the palletizer 162. For example, the control server 120 commands the robot 110 on one or more predetermined storage levels 130L1-13Ln to retrieve the ordered case unit CU from the predetermined storage space 130S of the corresponding storage level 130L1-130Ln. The robot transports the retrieved case unit CU from the storage space 130S to (one or more) lifts 150B, so that the retrieved case unit CU is output to the palletizer through the outgoing transfer station 160 in a predetermined order. Here, the predetermined order of case unit CU output is at least partially determined by the order-store affinity characteristics 166, 166'.

One or more of the control server 120 and the palletizer 162 are configured to determine the pallet to order store affinity characteristics 166, 166' based on, for example, the order store 200 that placed the order (FIG. 12B, block 1210). For example, one or more of the control servers 120 and the palletizer controllers 164, 164' of the palletizer 162 are configured with a pallet load generator 165, 165'. For example, in one aspect, each respective order store 200 may notify the pallet load generator 165, 165 of the respective pallet to order store affinity characteristics 166, 166' prior to placing an order (such as when the order store opens an account at the warehouse 199, or at any other suitable time, and the pallet to order store affinity characteristics 166, 166' are communicated or input into the warehouse management system). Here, the pallet load generator 166, 166' may include any suitable table that associates each order store 200 with the respective pallet to order store affinity characteristics 166, 166'. In other aspects, the pallet-to-order store affinity characteristics 166, 166' may be communicated to the pallet load generator 165, 165' at the same time the order is placed (such as an entry in an order submission, where the pallet load generator determines the pallet-to-order store affinity characteristics 166, 166' substantially directly from the order, regardless of the identity of the order store 200). As noted above, in this example, the pallet-to-order store affinity characteristics 166, 166' are for a clustered aisle pallet load parcel delivery method.

The pallet load generator 165, 165' determines any aisles having a total volume Vcomb of case units greater than the expected volume Vp of the pallet load PALO (block 700A of FIG. 7 ) (in the example illustrated in FIG. 5 , aisle 2 has a volume V2 greater than the expected volume Vp). Alternatively, the pallet load generator 165, 165' determines any aisles having a total weight Wcomb of case units greater than the expected weight Wmax (e.g., maximum weight) of the pallet load PALO. Based on the existence of any aisles having a total volume Vcomb of case units greater than the expected volume Vp or a total weight Wcomb of case units greater than the expected weight Wmax (collectively referred to herein as "aisles-in-excess"), the pallet load generator 165, 165' plans a pallet load PALO formed entirely with case units ordered for and belonging to the excess aisles (block 710 of FIG. 7 ). In some aspects, there will be some case units CU left over from the surplus aisle (FIG. 7, block 720) that are included in a subsequent pallet load. For example, the pallet load generator 165, 165' forms pallet load 1 (see FIG. 8) with a portion V2A of the case unit volume V2 from FIG. 5, while the remaining portion V2B of the case unit volume V2 from FIG. 5 is included in pallet load 5, as will be described below.

Subsequent pallet loads (or pallet loads where there is no excess aisle) are planned from one store aisle or a combination of more than one store aisle. As described herein, the aisle combinations are created by the pallet load generator 165, 165' by calculation to minimize the per aisle pallet ratio and maximize the case unit volume per pallet load PALO. Here, each available aisle combination of order store aisles is determined based on maximization of pallet loads, or in other aspects as described herein, is determined based on combined maximization of pallet loads and the adjacency or proximity of aisles in the available combinations, where maximization of pallet loads is weighted above the adjacency or proximity of aisles.

Each subsequent pallet load has a total case unit volume Vcomb that is less than the expected product volume Vp of the pallet load PALO, and a total case unit weight Wcomb that is less than the expected weight Wmax of the pallet load PALO. Each combination of aisles can have a different number of aisles, ranging from one aisle to the total number of aisles remaining in the order. The list of allowed aisle combinations ALC (see Figure 1) can be determined by taking a binary representation of an integer iterator (Figure 7, box 730), where the integer iterator has a value k ranging from 1 to 2Na ^-1 , where Na is the number of aisles remaining in the order. Each increment of the integer iterator corresponds to a potential aisle combination as follows: if the mth bit of the least significant bit from the binary representation of the integer iterator is 1, then aisle m from the remaining aisle list is present in the combination, and if the least significant bit is 0, then aisle m is not present in the combination.

As an example using an integer iterator, assume a store order has 5 aisles (there may be more or less than 5 aisles) and the integer iterator is equal to 12, the twelfth iteration (note that eleven of the possible 31 iterations occur before the twelfth iteration (where, for this example, the integer iterator varies from 1 to 31, as determined by k= ^2Na -1= ^25-1 =31 iterators/iteration), and there may be subsequent iterations after the twelfth iteration, such as if the aisle remains in the order). The binary representation of the number 12 (i.e., the integer iterator) is 01100. The numbers of aisles, arranged in order from highest to lowest, can be arranged in a grid relative to the binary representation of the integer iterator (so that the numbers of aisles align with the corresponding numbers in the binary representation of the integer iterator) as follows:

Aisle Number 5 4 3 2 1 integer iterator place value 0 1 1 0 0

As noted above, where the bit of the integer iterator corresponding to an aisle is 1, then the box unit CU from that aisle is present in the combination of the aisle. In the example provided above, the bit of the integer iterator corresponding to aisles 4 and 3 is 1, meaning that the box unit CU from aisles 4 and 3 is included in the 12th iteration combination of the aisle, while aisles 5, 2, and 1 are excluded from the 12th iteration combination of the aisle.

For each value k of the integer iterator, the total volume Vcomb and weight Wcomb of the box units CU in the corresponding aisle (e.g., the corresponding aisle combination for the given value k of the integer iterator) are determined by the pallet load generator 165, 165' and compared with the expected pallet volume Vp and the maximum pallet weight Wmax. If any of the values in Vcomb and Wcomb exceed the values of Vp and Wmax, respectively, the aisle combination having at least one of the Vcomb and Wcomb values exceeding the values of Vp and Wmax is discarded. If both the values of Vcomb and Wcomb are less than the values of Vp and Wmax, respectively, the aisle combination having both the Vcomb and Wcomb values less than the values of Vp and Wmax is added to the list of allowed aisle combinations ALC. Referring to the example above, the combined volumes V3 and V4 of aisles 3 and 4 must be less than or equal to the expected pallet volume Vp, respectively, and the combined weights W3 and W4 of aisles 3 and 4 must be less than or equal to the maximum pallet weight Wmax, respectively, in order to be included in the list of allowed aisle combinations ALC.

The list of allowed aisle combinations ALC may be sorted in any suitable manner, such as in descending order of the total (case unit) volume Vcomb of each aisle combination. Sorting the list of allowed aisle combinations in descending order of total case volume Vcomb may provide a minimum number of pallets to be constructed for a given store order. Here, the list of allowed aisle combinations ALC serves as a list of candidate combinations of products that are selected to plan a pallet load PALO in an output pallet list for a given store order.

An exemplary ranked list of allowed aisle combinations ALC for ten aisles may be presented as follows:

Aisle combination 1 Aisles 2, 3, 8 Vcomb/Vp＝.99 Aisle combination 2 Aisles 1, 4, 6, 9 Vcomb/Vp＝.98 Aisle combination 3 Aisle 3, 7 Vcomb/Vp＝.98 Aisle combination 4 Aisles 4, 5, 10 Vcomb/Vp＝.96

The rightmost column represents the total volume ratio of the case units of the corresponding aisle in the aisle combination (e.g., the combination volume Vcomb) relative to the expected pallet volume Vp.

Note that in aspects where the number of aisles included in a store order is large, each aisle may be subdivided into any suitable number of aisle subsections, where the size of the aisle subsections may depend on the computational resources of the pallet load generators 165, 165'. The size of the aisle subsections may also affect the minimum number of pallets generated/output by the warehouse 199 for a given store order. Aisle subsections may be grouped together with other aisle subsections to form store partitions, where each aisle subsection is treated as an aisle and the list of aisle combinations ALCs is determined for each store partition in the manner described above.

As noted above, the pallet-to-order-store affinity nature of the clustered-aisle pallet load package delivery method is informed by a repeated two-loop DRL determination, at least one of which determines the available combinations of order-store aisles to resolve the placement of packages in the pallet load, and another at least one of which relates the order-store aisles to each other. In the repeated two-loop DRL, the pallet load is planned by employing a list of aisle combinations ALC.

In one cycle of the repeated double-loop DRL, the pallet load generator 165, 165' determines the available aisle combinations, thereby resolving the parcel arrangement in the pallet load (FIG. 12B, box 1230). The entry from the list of aisle combinations ALC with the highest Vcomb/Vp ratio (in the example above, aisle combination 1) is selected (FIG. 7, box 735), and the pallet load PALO is planned with the case units CU corresponding to the aisles in the selected aisle combination (FIG. 7, box 740), thus achieving optimization with respect to the minimum number of pallets. In the case where the pallet plan for the selected aisle combination does not fit all the case units from the aisles in the selected aisle combination in the pallet load PALO (which means that some case units of the corresponding aisles remain unpacked to be included in other pallets, thereby confirming or verifying the optimization of the pallet ratio RPA per aisle - FIG. 7, box 745), the pallet plan is discarded (FIG. 7, box 750). The next entry from the list of aisle combinations ALC with the next highest Vcomb/Vp ratio (e.g., the next aisle combination, in the example above, aisle combination 2) is selected (FIG. 7, block 735), thereby again achieving optimization with respect to the minimum number of pallets. The pallet load PALO is planned with the case units CU corresponding to the aisles in the next aisle combination (FIG. 7, block 740), wherein blocks 740, 745, 750, 735 are repeated (for subsequent aisle combinations, e.g., aisle combination 2, aisle combination 3, aisle combination 4, and so on) until the pallet plan for the selected entry from the list of aisle combinations ALC successfully packs all the case units for the corresponding aisle in the pallet load (e.g., pallet load PALO), thereby again confirming or verifying the optimization of the per-aisle pallet ratio RPA. Here, the aisle combinations are analyzed sequentially by the pallet load generators 165, 165' relative to the pallet plan via repeated double-loop DRL determinations until a planning solution is found that will include all the case units ordered for the aisles in the aisle combination.

As an example of sequential analysis of aisle combinations, using aisle combinations 1-4 above, the pallet load generator 165, 165' first analyzes aisle combination 1 (aisles 2, 3, 8) to determine whether all ordered case units CU for aisles 2, 3, and 8 will fit into one pallet load with a maximum volume Vp and a maximum weight Wmax. For exemplary purposes, it is assumed that not all ordered case units for aisles 2, 3, and 8 will fit into one pallet load, and therefore the next aisle combination in the aisle combination sequence (e.g., aisle combination 2) is analyzed. Here, the pallet load generator 165, 165' analyzes aisle combination 2 (aisles 1, 4, 6, and 9) to determine whether all ordered case units CU for aisles 1, 4, 6, and 9 will fit into one pallet load with a maximum volume Vp and a maximum weight Wmax. For exemplary purposes, it is assumed that all ordered case units for aisles 1, 4, 6, and 9 will fit into one pallet load, and the determination loop of analyzing aisle combinations in this order is stopped and the remaining aisle combinations (e.g., aisle combinations 3 and 4) are not analyzed. As described below, any subsequent pallet loads will be generated with an updated set of aisle combinations (the updated set of aisle combinations is separate and distinct from the previous set of aisle combinations and excludes aisles to which all ordered case units have been assigned to the pallet load).

The successful pallet plan (in the example above, aisle combination 2) forms a planned pallet load PALO and is added to the output list executed by the automated parcel transport system 195 (FIG. 7, block 755), causing the automated parcel transport system 195 to pick and sequence case units CU in the planned pallet load PALO (FIG. 12B, block 1220) for building the planned pallet load at the palletizer 162 (FIG. 12B, block 1250). In some aspects, case unit picking and pallet building for a given store order can occur substantially simultaneously with the planning of subsequent pallet loads in that store order, while in other aspects, case unit picking and pallet building can occur after all pallets have been planned for that store order.

In another cycle of the repeated double-loop DRL, in which the planned pallet load PALO is successfully planned, the pallet load generator 165, 165 determines whether there are any case units CU from any aisle in the store order that have not been included in the (successful) planned pallet load PALO (FIG. 7, box 760). In the case that there are no more case units CU, the pallet planning is stopped (FIG. 7, box 765), and the case unit CU of the planned pallet load PALO for the store order is retrieved and sorted from storage by the automated parcel transport system 195 (FIG. 12B, box 1220), and the pallet load PALO is built by the palletizer 162 (FIG. 12B, box 1250). In the case that the case unit CU remains, another (e.g., subsequent) pallet is planned to be included in the store order (FIG. 7, box 770). Here, the pallet load generator 165, 165' updates the relationship between the store aisles (Figure 12B, box 1240; Figure 7, box 730) so that all aisle combinations containing any aisle that was fully consumed by a previous pallet (e.g., all ordered case units have been assigned to the aisle of the pallet load) are removed and an updated list of aisle combinations ALC is generated (Figure 7, box 730). The repeated double loop DRL continues until there are no remaining unplanned case units CU for any aisle of the store order (i.e., all ordered case units are assigned to the pallet load). The pallet load generator 165, 165' is configured to relate each store aisle to each other (Figure 12B, box 1240, see also Figure 7, box 730 described herein) to minimize the total number of pallet loads in the order and minimize one or more of the pallet per aisle ratio RPA. Note that as described herein, the term "aisle" as used herein generally designates both a product group and an order store aisle to which an integer value is assigned. As such, order store aisles (eg, physical locations within an order store) are related to one another by at least one of an aisle-to-aisle affinity characteristic and a product group type-to-product group type affinity characteristic.

FIG8 is an exemplary store order 800 planned with the pallet load generator 165, 165', which employs the clustered aisle pallet load parcel delivery method described above. In this exemplary order 800, the volume of the case units in each aisle illustrated in FIG5 is shown as being included in the corresponding pallet load (e.g., pallet 1-pallet 6), where each pallet load is sequentially planned (as described above) so as to have a volume Vcomb that is less than the maximum pallet volume Vp. As can be seen in FIG8, the volume V2 corresponding to the case units CU ordered for aisle 2 is divided between pallet loads 1 and 5 (as described above), so that pallet load 1 is completely consumed by the case units ordered for aisle 2. Note that the last planned pallet load (e.g., pallet load 6) can have a smaller combined volume Vcomb than previously planned pallet loads (e.g., pallet loads 1-5) because the last pallet load includes case units from aisles that were not included in the previous aisle combinations of previously planned pallet loads 1-5 that were optimized for volume or weight, achieved an optimization for the minimum number of pallets and/or an optimization for the pallet per aisle RPA.

According to the cluster aisle pallet load parcel delivery method, (one or more) generated pallet load PALOs are built and shipped (Figure 12B, box 1260) to the order store 200 by the palletizer 162. (One or more) pallet load PALOs arrive at the order store 200 from the warehouse 199. Referring to Figure 2, (one or more) pallet load PALOs are typically received in the loading dock area 222 of the order store 200. Each of (one or more) pallet load PALOs includes products from several physical locations (e.g., aisles, departments, sections, etc.) of the order store 200. For exemplary purposes, these physical locations will be referred to as aisles herein. Note that although aisles 1-4 and aisles 11-14 are illustrated in Figure 2, an order store can have any suitable number of aisles. In the cluster aisle pallet load parcel delivery method, case units CU stored on a pallet load PALO are unloaded from the pallet load PALO (e.g., manually or automatically, such as an automated depalletizer similar to those described herein with respect to palletizers 162, 162') onto separate and different secondary pallet loads PALO21, PALO22, PALO23 (three are shown for exemplary purposes, and it should be understood that there may be more or less than three secondary pallet loads). Here, each of the secondary pallet loads PALO21, PALO22, PALO23 includes case units CU from a separate single aisle. For example, pallet load PALO21 includes only case units CU assigned to aisle 1, pallet load PALO22 includes only case units CU assigned to aisle 3, and pallet load PALO 23 includes only case units CU assigned to aisle 12. The secondary pallets PALO21, PALO22, PALO23 are moved (e.g., manually and/or with an automated transport) from the loading dock area 222 to the corresponding allocated aisles in the shopping area 224 of the order store 200, where the case units CU of the corresponding secondary pallet loads PALO21, PALO22, PALO23 are unloaded and placed on the corresponding store shelves 233 of the corresponding allocated aisles.

In the clustered aisle pallet load parcel delivery method, the pallet load PALO can maintain case units CU assigned to aisles that are spatially located away from each other (e.g., far) in the order store 200. As described above, unloading the case units CU assigned to the corresponding aisles onto the corresponding secondary pallet loads PALO21, PALO22, PALO23 makes it possible to maintain the pallet loads PALO assigned to the case units CU assigned to the aisles that are spatially located away from each other (e.g., far) has essentially little to no impact on the replenishment/inventory of the store shelves 233. Here, in the clustered aisle pallet load parcel delivery method, case units CU from different aisles can be assigned to a common pallet load PALO (regardless of aisle proximity) to maximize the number of full-size pallet loads (e.g., pallet loads with maximum pallet load size and/or weight) and minimize the number of pallet loads PALO on the transport vehicle that moves the pallet loads PALO from the warehouse 199 to the order store 200.

Referring now to Figures 1, 4, 5, 9, 12, and 13, the pallet-to-order-store affinity characteristics for a mixed-mode clustered and adjacent-aisle pallet load parcel delivery method will be described in greater detail. The mixed-mode clustered and adjacent-aisle pallet load parcel delivery method minimizes both (1) the number of pallets created from a given set of products and (2) the average per-aisle pallet ratio RPA, while minimizing the distance between the shelf locations of the case units assigned to each pallet. For purposes of this description, aisle numbers that are numerically close to each other are also spatially close to each other (e.g., aisles 10 and 11 are close to each other, while aisle 60 is far from both aisles 10 and 11). Here, the above-described clustered aisle pallet load parcel delivery method is modified such that pallet loads are planned (e.g., based on the contiguity or proximity of one order store aisle to another order store aisle), wherein, in order store 200, products on a common pallet are unloaded into aisles that are adjacent or neighboring to each other.

In the hybrid mode, cluster and adjacent aisle pallet load parcel delivery method orders are placed by the order store 200 and at least one store order affinity characteristic is determined in the manner described above with respect to blocks 1200 and 1210 of Figure 12B. Blocks 700A, 700B, 710, 720 of Figure 13 are identical to the similarly numbered blocks described above in Figure 7. As such, entire pallets are planned from aisles having case unit volumes greater than the pallet load volume Vp and/or weights greater than the maximum pallet load weight Wmax, with the remaining case units ordered for those aisles included in the aisle combination analysis (Figure 13, blocks 700A, 700B, 710, and 720) in the manner described above. Aisle combinations for the mixed-mode cluster and adjacent-aisle pallet load parcel delivery methods are also determined in the manner described above with respect to block 730 of FIG. 7 (see also FIG. 12B , block 1220); however, the determined aisle combinations are ranked by a score S that takes into account the volume of the case units ordered for a given aisle, the weight of the case units ordered for a given aisle, and the proximity of the aisles included in the planned pallet ( FIG. 13 , block 1330). For example, the score S may be determined by the following equation:

Where minAisle and maxAisle are the minimum and maximum aisle numbers included in a given aisle combination, and d0 is greater than 0 and is a parameter that reflects the relative importance of aisle spread/distance (e.g., store friendliness) relative to the volume of case units in a pallet load. As can be seen from Equation 2, for small d0 values, aisle spread/distance is more important than the volume of case units in a pallet load, and for large d0 values, the case unit volume of case volume in a pallet load is more important than the spread/distance between aisles assigned to that pallet load. The determined aisle combinations (see FIG. 7 , block 730) are weighted or scored with a score S and sorted based on the score S ( FIG. 13 , block 1330). Repeated double loop DRL is performed to plan pallets in the manner described above with respect to blocks 735, 740, 745, 750, 755, 760, 765, 770 of FIG. 7 (see also FIG. 12B, block 1230) to achieve optimization with respect to the minimum number of pallets and verify/confirm optimization of the per-aisle pallet ratio RPA; however, for each subsequent pallet, the updated aisle combination is again scored with the score S and sorted based on the score S. The ordered case units are picked and the planned pallet load PALO is built and shipped to the order store in the manner described above with respect to blocks 1240, 1250 and 1260 of FIG. 12B.

FIG9 is an illustrative example of a planned pallet load (e.g., Pallet 1-Pallet 7) determined using a mixed-mode clustering and adjacent-aisle pallet load parcel delivery method. In this illustrative example, the planned pallet loads are determined based on an order having the aisles and corresponding case unit volumes illustrated in FIG2 . As can be seen in FIG9 , the first pallet load is planned from a portion of the case unit volume V2A from aisle 2 alone, and all other planned pallet loads in the store order have a volume Vcomb that is less than the expected volume Vp of the pallet load (as described above). Planned pallet load 1 includes only the volume of case unit V1 allocated to aisle 1 according to the mixed-mode clustering and adjacent-aisle pallet load parcel delivery method. Planned pallet load 2 includes the volumes of case units V2B and V3 allocated to aisles 2 and 3. Planned pallet load 4 includes the volume of case units V4 and V7 allocated to aisles 4 and 7, note that planned pallet load 4 creates a break in the sequence of aisles, but the break is not a large break because aisle 4 is only 3 aisles away from aisle 7, which complies with the purpose of mixed mode clustering and adjacent aisle pallet load parcel delivery methods. Planned pallet load 5 includes the volume of case units V5 and V6 allocated to aisles 5 and 6. Planned pallet load 6 includes the volume of case units V8-V11 allocated to aisles 8-11. Planned pallet load 7 includes the volume of case unit V12 allocated to aisle 12.

14, in some aspects of the mixed-mode cluster and adjacent-aisle pallet load package delivery method, a maximum (or average) distance MDmax between aisles of the ordered case units CU assigned to any given pallet, generally expressed in terms of the difference between aisle numbers, may be specified by the order store 200. This aspect of the mixed-mode cluster and adjacent-aisle pallet load package delivery method is the same as described above; however, before sorting the list of aisle combinations, aisle combinations that include aisles with distances between aisles greater than the maximum distance MDmax are excluded/discarded (see FIG. 14, block 1430).

As described herein with respect to FIG. 15 , in other aspects of the mixed-mode clustering and adjacent aisle pallet load parcel delivery method, the pairwise relationship between aisles p and q can be specified by the order store 200. The relationship between aisles p and q can be expressed as an aisle affinity matrix A[p, q], where p and q belong to the set of all aisles that exist in order. The aisle affinity matrix A[p, q] is diagonally symmetric, so that A[p, q] is equal to A[q, p]. For "store-friendly" aisles, the values of the aisle affinity matrix A[p, q] should be substantially equal to or close to 1, so that the case units CU of these aisles should be on the same (e.g., single) pallet load. For "unfriendly" aisles, the values of the aisle affinity matrix A[p, q] should be substantially equal to or close to 0, where the case units CU of the aisles should be kept separate in different pallet loads (such as the separation of corrosive products (e.g., laundry detergent) and food items (e.g., baby food) as mentioned above). The diagonal elements of the aisle affinity matrix A[p,q] should be equal to 1, for example, for each p, A[p,p]=1, which has the implication that any aisle is friendly to itself.

With the pairwise relationship between aisles, the mixed mode clustering and adjacent aisle pallet load parcel delivery method remains as described above; however, the score S is modified as shown in the following equation:

for all {p, q} belonging to a given combination of aisles. In Equation 3, the expression p is greater than or equal to 0 and is a multiplier that shows the relative importance of pallet volume (e.g., minimization of the total number of pallets) and friendliness between aisles included in a given combination of aisles. For smaller values of p, aisle friendliness is less important than minimizing the total number of pallets; while for larger values of p, friendliness is more important than minimizing the total number of pallets. In the manner described above (see Figure 13), the determined aisle combinations are scored and sorted in descending order according to the scores, and starting with the first aisle combination in the sorted list of aisle combinations, pallet loads are planned for each sequential aisle combination until a successful pallet load is planned, the minimum number of pallets is optimized again and the optimization of the pallet ratio RPA for each aisle is verified/confirmed.

With reference to Figures 1, 3, 5, 10, 11, 12 and 15, the pallet-to-order-store affinity characteristics for the adjacent-aisle pallet load parcel delivery method will be described in more detail. The adjacent-aisle pallet load parcel delivery method of pallet planning can be used for warehouse customers, which transport ordered pallets to store aisles so that case units can be unloaded directly from the ordered pallets to store shelves. Here, as can be seen in Figure 3, each ordered pallet load PALOA, POLOA' is transported from one aisle to another along a corresponding transportation path 300, 302 for unloading case units. The transportation paths 300, 302 traverse the aisles in an adjacent aisle sequence (e.g., pallet load PALOA traverses adjacent aisles 1-3, and pallet load POLOA' traverses adjacent aisles 11-13).

In the adjacent aisle pallet load parcel delivery method, when planning a pallet load, the selection of adjacent or adjacent aisles is prioritized, while the total number of pallets planned for any given order is minimized and excessive splitting of aisles between pallets is substantially avoided. In the event that an aisle is split between two pallets, no more than one aisle is split between the two pallets. An exemplary illustration of a pallet load planned with a "pure" adjacent aisle pallet load parcel delivery method is shown in FIG. 10 . As with other pallet load parcel delivery methods, an aisle having a volume greater than a predetermined volume Vp of the pallet load (or a weight greater than the maximum weight Wmax of the pallet load) is selected and allocated to the full/entire pallet (see FIG. 5 , where aisle 2 has a volume V2 greater than the volume Vp of the pallet load) until the volume or weight remaining in the corresponding aisle is less than the volume Vp or weight Wmax. As can be seen in FIG. 10 , pallet load 1 is completely consumed by a portion V2A of the volume V2 of aisle 2. As described herein, in the case of a full pallet load planned from a surplus aisle, all remaining volumes and weights of the aisles in the order (e.g., the volume and weight of the case units ordered for each corresponding aisle) are less than the volume Vp and weight Wmax of the pallet load. As such, each aisle has an amount of case units that are expected to fit into a single pallet load and, in many cases, combined with other case units from other aisles in a single pallet load, achieve minimization of the number of pallets and pallet per aisle ratio RPA.

In the adjacent aisle pallet load parcel delivery method, the total number of pallet loads in an order and the per aisle pallet RPA are minimized, but to a lesser degree than assigning case units CU to pallets in a sequence of adjacent/adjacent aisles (e.g., each available combination of order-store-aisle is determined more based on the contiguity or adjacency of the order-store-aisles in the available combinations and less based on maximizing the pallet loads (volume or weight). When planning pallet loads according to the adjacent aisle pallet load parcel delivery method, some aisles may be split between pallets, but only when it is avoided that the split generates additional pallets, thereby increasing the overall number of pallets planned for any given order.

If aisle splitting between pallet loads is not allowed, the total number of pallets may increase. For example, FIG. 11 illustrates a store order planned with an adjacent aisle pallet load parcel delivery method (e.g., such as illustrated in FIG. 2 ) without splitting case units from the aisles between pallet loads (except in the case of any excess aisles, such as Aisle 2, where a portion of the case units of each excess aisle are consumed by the entire pallet load and the remainder of the case units are delivered between the remaining pallet loads according to the adjacent aisle pallet load parcel delivery method). In FIG. 11 , the resulting order plan includes seven pallet loads, which is the same number of pallet loads as the mixed-mode cluster and adjacent aisle pallet load parcel delivery methods, but one more pallet load than the cluster aisle pallet load parcel delivery method (note that the example of this delivery method is based on the aisle case unit order shown in FIG. 5 ). It is also noted that, as can be seen in Figure 11, without splitting the aisles between pallet loads, more pallets have case unit volumes below the maximum volume Vp of the corresponding pallet load, while in both the mixed-mode cluster and adjacent aisle pallet load parcel delivery method and the cluster aisle pallet load parcel delivery method (except for the last planned pallet load), have case unit volumes closer to the volume Vp allowed for the pallet load.

In order to increase the average pallet volume and reduce/minimize the number of planned pallets, while prioritizing adjacent/adjacent aisle planning (e.g., store friendliness), a split of case units CU from some aisles is performed in the pallet planning. Here, the adjacent aisle pallet load parcel delivery method can be "modified" to adopt thresholds Vp0 and Vp1, where:

Vp0<Vp1<Vp [Equation 4]

The values of Vp0 and Vp1 optimize the combination of pallet volume and number of split aisles (and minimize the number of pallets). The values of Vp0 and Vp1 should be reasonably close to Vp, for example:

Vp0 = .95*Vp [Equation 5]

and

Vp1 = .98*Vp [Equation 6]

The values of Vp0 and Vp1 are generally kept constant (e.g., unchanged during the pallet planning iteration loop described herein), but may be adjusted for a particular order profile. For example, very large case units may warrant a reduction in Vp0 and Vp1 because it is more likely that some case units will not fit on a given pallet load, while small cases may warrant an increase in Vp0 and Vp1 because it is more likely that a case unit will fit on a given pallet load.

In an adjacent aisle, a pallet load parcel delivery method order is placed by the order store 200, and at least one store order affinity characteristic is determined in the manner described above with respect to blocks 1200 and 1210 of FIG. 12B. As described above, an aisle having a volume Vp greater than a predetermined volume of the pallet load (or a weight greater than a maximum weight Wmax of the pallet load) is selected and assigned to a full/entire pallet. The number of pallets Np0 for the order is determined by the pallet load generator 165, 165' based on the expected product volume Vp and maximum weight Wmax in one pallet load and the remaining case unit volume Vrem and weight Wrem according to the following equation (FIG. 15, block 1500):

The pallet load generator 165, 165' relates the aisles to each other (FIG. 12B, block 1220, in this example a sequential aisle relationship) and to the determined aisle combination that resolves the case unit placement in the pallet load (FIG. 12B, block 1230). For example, the pallet load generator 165, 165' determines the aisle combination for the "next" pallet load (FIG. 15, block 1505, where the "next" pallet load is the pallet load currently being planned). Here, the aisles are selected sequentially (e.g., i, i+1, i+2...) and for each aisle added (FIG. 15, block 1510), the cumulative case unit volume Vcomb and cumulative pallet weight Wcomb are updated (FIG. 15, block 1515). Where the cumulative volume Vcomb is less than or equal to Vp0 and the cumulative weight Wcomb is less than or equal to Wmax (FIG. 15, block 1520), the next aisle in the aisle sequence is added to the aisle combination (FIG. 15, block 1510), thereby achieving verification/confirmation that each aisle pallet is optimized over the RPA. Aisles are sequentially added to the aisle combination until the cumulative volume Vcomb exceeds one of the value Vp0 and the cumulative weight Wcomb exceeds the maximum pallet load weight Wmax.

In the event that the cumulative volume Vcomb exceeds the value Vp0 and the cumulative weight Wcomb exceeds the maximum pallet load weight Wmax, the remaining product volume Vrem and the remaining product weight Wrem are updated (FIG. 15, block 1530). An updated estimate of the number of pallets Np1 for the order is determined by the pallet load generator 165, 165' in a manner similar to that described above, but using the updated values of Vrem and Wrem as follows (i.e., the remaining volume and weight after the last aisle selected in block 1510 of FIG. 15 of the first nested loop RL1, which includes blocks 1510, 1515, 1520 of FIG. 15 and is nested within the overall/wider loop illustrated in blocks 1500-1580 and 1590 of FIG. 15)

In the event that the total number of pallets Np0 determined before aisle selection for the next pallet load is the same as the updated number of pallets Np1 (i.e., Np0=Np1+1, where the number 1 represents the current pallet) (Figure 15, box 1536), aisle selection for the next pallet load is stopped and the pallet load is planned from the optimized aisle combination that achieves the minimum number of pallets (Figure 15, box 1565).

In the event that the updated number of pallets Np1 increases (i.e., Np0<Np1+1), the additional aisle in the aisle sequence is added to the aisle combination in the second nested loop RL2 (FIG. 15, box 1540), which includes boxes 1540, 1545, 1550, 1555, 1560 of FIG. 15 and is nested within the overall/wider loop illustrated in boxes 1500-1580 and 1590 of FIG. 15. As the next sequential aisle is added to the aisle combination (FIG. 15, box 1540), the cumulative case unit volume Vcomb and the cumulative pallet weight Wcomb are updated (FIG. 15, box 1545). The remaining volume Vrem and the remaining weight Wrem of the case units in the order are also updated (FIG. 15, box 1550). An updated estimate of the number of pallets Np1(updated) for the order is determined by the pallet load generator 165, 165' (FIG. 15, block 1555) in the manner described above (see Equation 8), but using the updated values of Vrem and Wrem determined in block 1550 of FIG. 15. Here, if any of the following conditions (Equations 9-11) are not met, then recursive loop RL2 is repeated, adding additional aisles to the aisle combination:

Vcomb>Vp1 [Equation 9]

Wcomb>Wmax [Equation 10]

or

Np0 = Np1(updated) + 1 [Equation 11]

In the event that any of the above conditions (Equations 9-11) are met, aisle selection for the next pallet load is stopped and the pallet load is planned from the aisle combination (Figure 15, block 1565), again optimizing for the minimum number of pallets.

In the case of a planned pallet load (FIG. 15, box 1565), the pallet load generator 165, 165' adds unplanned products from aisle combinations (e.g., split aisles, such as, for example, aisle 6 split into case unit volumes V6A, V6B and aisle 12 split into case unit volumes V12A, V12B) to the remaining products in the order (FIG. 15, box 1570). The pallet load generator 165, 165' adds the planned pallet load (from FIG. 15, box 1565) to the output list of pallet loads (FIG. 15, box 1575), which enables the pallet load to be built in the output list. The pallet load generator 165, 165' determines whether there are any remaining case units CU in the order (FIG. 15, box 1580), again verifying/confirming the optimization of the per-aisle pallet ratio RPA. In the event that no more case units exist, pallet load planning for the order is stopped (FIG. 15, block 1585) and pallet loads PALOA, PALOA' are built and shipped to the order store 200 in the manner described above with respect to blocks 1240, 1250 and 1260 of FIG. 12B. In the event that case units CU remain in the order, the pallet count for the order is updated (FIG. 15, block 1590) and another pallet is planned for the order in the manner described above, thereby minimizing the number of pallets.

In the above adjacent aisle pallet load parcel delivery method, having the volume of the selected box unit above the first threshold volume Vp0 can increase the probability that at least one aisle will not be fully packed into the pallet load currently being planned, so that a portion of at least one aisle will overflow into the next subsequent pallet load being planned. The overflow of box units from one pallet load to the next subsequent pallet load will increase the value of the pallet per aisle ratio RPA and may reduce aisle adjacency (e.g., the overall store friendliness of the ordered pallet load). As noted above, the values of Vp0 and Vp1 can be adjusted to reflect the importance of minimizing the total number of pallets relative to the pallet per aisle ratio RPA. Higher values of both Vp0 and Vp1 (e.g., close to Vp) can reduce the expected number of pallets, while lower values of both Vp0 and Vp1 can reduce the likelihood of splitting aisles between pallets (but may increase the expected number of pallets).

FIG. 11 illustrates a pallet load for an order planned with the adjacent aisle pallet load parcel delivery method described above. As noted above, the volumes illustrated in FIG. 11 are the same volumes corresponding to those of the aisles illustrated in FIG. 5 . According to the adjacent aisle pallet load parcel delivery method, a portion V2A of the volume V2 of aisle 2 consumes the entire/full pallet load (e.g., pallet load 1), while the remaining volume V2B of aisle 2 is considered for pallet planning according to FIGS. 12 and 15 (described above). Note that the volume V6 of aisle 6 is split between pallet loads 4 and 5, while the volume V12 of aisle 12 is split between pallet loads 6 and 7. The remaining volumes V1, V3, V4, V5, and V7-V11 for aisles 1, 3, 4, 5, and 7-11, as well as the remaining volume of aisle 2, are assigned to only one corresponding pallet load, and each pallet load has an uninterrupted sequence of aisles assigned to that pallet load. In this example, the total number of pallet loads is seven (as in FIG. 10 , whose pallet loads are planned with “pure” aisle adjacency, e.g., without employing thresholds Vp0, Vp1 and double nested loops RL1, RL2); however, in FIG. 11 , the last pallet load (pallet load 7) has a smaller volume than the last pallet load in FIG. 10 , and can be placed on top of another pallet load in sequence, thereby reducing the amount of floor space required to transport the ordered pallet loads. Note that, in general, a “modified” adjacent aisle pallet load parcel delivery method (which takes into account splitting aisle case unit volumes between pallet loads) results in a smaller number of planned pallet loads than a “pure” adjacent aisle pallet load parcel delivery method (which does not take into account splitting aisle case unit volumes between pallet loads).

Referring now to FIGS. 1-4 and 16 , a method of constructing a pallet load PALO in accordance with any one or more of a cluster aisle pallet load parcel delivery method, a mixed mode cluster and adjacent aisle pallet load parcel delivery method, and an adjacent aisle pallet load parcel delivery method will be described. Here, parcels are placed on a pallet (see FIG. 1 ) to form a pallet load PALO ( FIG. 16 , block 1600). As described herein, individual case units CU are provided from a storage array 130 to an automated palletizer to form a pallet load PALO, wherein the pallet load PALO includes more than one composite layer L1-Ln of case units CU. The pallet load PALO is formed by case units CU arranged in the pallet load PALO, which embodies at least one pallet to order store affinity characteristic 166, 166′ ( FIG. 16 , block 1610) for a predetermined method of pallet load parcel delivery at the order store 200. As described herein, at least one pallet-to-order-store affinity characteristic 166, 166' is for at least one of a clustered aisle pallet load parcel delivery method, a mixed-mode cluster and adjacent aisle pallet load parcel delivery method, and an adjacent aisle pallet load parcel delivery method at the order store.

According to one or more aspects of the present disclosure, a material handling system for processing packages and placing the packages on a pallet bound for an order store, the material handling system comprising: a storage array having storage spaces for holding packages therein; an automated package transport system communicatively connected to the storage array, for storing packages in the storage spaces of the storage array and retrieving packages from the storage spaces of the storage array; an automated palletizer for placing packages on the pallet to form a pallet load, the automated palletizer being communicatively connected to the automated package transport system, the automated package transport system being configured to provide individual packages from the storage array to the automated palletizer to form a pallet load, the pallet load comprising more than one composite layer of packages; and a controller operably connected to the automated palletizer, the controller being programmed with a pallet load generator, the pallet load generator having at least one pallet-to-order store affinity characteristic, a predetermined method for pallet load package delivery at the order store, the pallet load generator being configured so that the pallet load is formed by the automated palletizer of packages arranged in the pallet load embodying the at least one pallet-to-order store affinity characteristic.

According to one or more aspects of the present disclosure, at least one pallet-to-order-store affinity characteristic is for at least one of a clustered aisle pallet load parcel delivery method, a mixed-mode clustered and adjacent aisle pallet load parcel delivery method, and an adjacent aisle pallet load parcel delivery method at an order store.

According to one or more aspects of the present disclosure, at least one pallet-to-order-store affinity characteristic is informed by a repeated double-loop determination, at least one loop of which relates order-store aisles to one another.

According to one or more aspects of the present disclosure, within at least one loop determination, order store aisles are related to each other by at least one of an aisle-to-aisle affinity characteristic and a product group type to product group type affinity characteristic.

According to one or more aspects of the present disclosure, the aisle-to-aisle affinity characteristic is the distance separating one order store aisle from another order store aisle, or the contiguity or proximity of one order store aisle to another order store aisle.

According to one or more aspects of the present disclosure, at least one pallet-to-order-store affinity characteristic is informed by repeating a double-loop determination, at least one of which loops determines available combinations of order-store aisles to resolve the arrangement of packages in a pallet load.

According to one or more aspects of the present disclosure, each available combination of order store aisles is determined based on maximization of pallet load, or maximization of a combination of aisle contiguity or adjacency and pallet load in the available combinations, where maximization of pallet load is weighted higher than aisle contiguity or adjacency.

According to one or more aspects of the present disclosure, each available combination of order store aisles is determined based more on the contiguity or proximity of the order store aisles in the available combination and less on the maximization of pallet loads.

According to one or more aspects of the present disclosure, a pallet load generator interprets the pallet load based on at least one pallet-to-order-store affinity characteristic such that the pallet load is maximized with respect to at least one of a maximum pallet load volume and a maximum pallet load weight.

According to one or more aspects of the present disclosure, a pallet load generator parses a pallet load based on at least one pallet-to-order-store affinity characteristic such that the pallet load has a maximum number of packages from a minimum number of order-store aisles.

According to one or more aspects of the present disclosure, a pallet load generator parses the pallet loads according to at least one pallet-to-order store affinity characteristic to generate a minimum number of pallet loads for each order store.

According to one or more aspects of the present disclosure, a pallet load generator parses pallet loads according to at least one pallet-to-order-store affinity characteristic such that for each pallet load bound for an order store, the packages forming the pallet load represent a minimum quantity for an aisle of the order store.

According to one or more aspects of the present disclosure, a pallet load generator resolves pallet loads based on at least one pallet-to-order-store affinity characteristic such that for each pallet load going to an order store, the resolved pallet load represents a minimum quantity for an order store aisle.

According to one or more aspects of the present disclosure, a pallet load generator is configured to sequentially resolve each pallet load via a repeated double loop determination informing at least one pallet-to-order store affinity characteristic.

According to one or more aspects of the present disclosure, at least one pallet-to-order-store affinity characteristic is informed by a dual nested loop determination, at least one of which relates order-store aisles to one another or determines available combinations of order-store aisles to resolve the arrangement of packages in a pallet load.

According to one or more aspects of the present disclosure, an automated palletizer includes: automated parcel picking equipment capable of moving parcels from a parcel storage section to a pallet to form a pallet load from the parcels, the pallet load including more than one composite layer of parcels; and a controller operably connected to the automated palletizer, the controller being programmed with a pallet load generator having at least one pallet-to-order store affinity characteristic, a predetermined method for pallet load parcel delivery at an order store, the pallet load generator being configured such that the pallet load is formed by the automated palletizer of the parcels arranged in the pallet load embodying the at least one pallet-to-order store affinity characteristic.

According to one or more aspects of the present disclosure, at least one pallet-to-order-store affinity characteristic is for at least one of a clustered aisle pallet load parcel delivery method, a mixed-mode clustered and adjacent aisle pallet load parcel delivery method, and an adjacent aisle pallet load parcel delivery method at the order store.

According to one or more aspects of the present disclosure, at least one pallet-to-order-store affinity characteristic is informed by a repeated double-loop determination, at least one loop of which relates order-store aisles to one another.

According to one or more aspects of the present disclosure, within at least one loop determination, order store aisles are related to each other by at least one of an aisle-to-aisle affinity characteristic and a product group type to product group type affinity characteristic.

According to one or more aspects of the present disclosure, the aisle-to-aisle affinity characteristic is the distance separating one order store aisle from another order store aisle, or the contiguity or proximity of one order store aisle to another order store aisle.

According to one or more aspects of the present disclosure, at least one pallet-to-order-store affinity characteristic is informed by repeating a double-loop determination, at least one of which loops determines available combinations of order-store aisles to resolve the arrangement of packages in a pallet load.

According to one or more aspects of the present disclosure, each available combination of order store aisles is determined based on maximization of pallet load, or maximization of a combination of aisle contiguity or adjacency and pallet load in the available combinations, where maximization of pallet load is weighted higher than aisle contiguity or adjacency.

According to one or more aspects of the present disclosure, each available combination of order store aisles is determined based more on the contiguity or proximity of the order store aisles in the available combination and less on maximization of pallet loads.

According to one or more aspects of the present disclosure, a pallet load generator interprets the pallet load based on at least one pallet-to-order-store affinity characteristic such that the pallet load is maximized with respect to at least one of a maximum pallet load volume and a maximum pallet load weight.

According to one or more aspects of the present disclosure, a pallet load generator parses a pallet load based on at least one pallet-to-order-store affinity characteristic such that the pallet load has a maximum number of packages from a minimum number of order-store aisles.

According to one or more aspects of the present disclosure, a pallet load generator parses the pallet loads according to at least one pallet-to-order store affinity characteristic to generate a minimum number of pallet loads for each order store.

According to one or more aspects of the present disclosure, a pallet load generator parses pallet loads according to at least one pallet-to-order-store affinity characteristic such that for each pallet load bound for an order store, the packages forming the pallet load represent a minimum quantity for an aisle of the order store.

According to one or more aspects of the present disclosure, a pallet load generator resolves pallet loads based on at least one pallet-to-order-store affinity characteristic such that for each pallet load going to an order store, the resolved pallet load represents a minimum quantity for an order store aisle.

According to one or more aspects of the present disclosure, a pallet load generator is configured to sequentially resolve each pallet load via a repeated double loop determination informing at least one pallet-to-order store affinity characteristic.

According to one or more aspects of the present disclosure, at least one pallet-to-order-store affinity characteristic is informed by a dual nested loop determination, at least one of which relates order-store aisles to one another or determines available combinations of order-store aisles to resolve the arrangement of packages in a pallet load.

According to one or more aspects of the present disclosure, a method for building a pallet load includes: placing packages on a pallet to form the pallet load, wherein individual packages are provided from a storage array to form the pallet load, the pallet load comprising more than one composite layer of packages; and wherein the pallet load is formed by packages arranged in the pallet load that embody at least one pallet-to-order store affinity characteristic of a predetermined method for pallet load package delivery at an order store.

According to one or more aspects of the present disclosure, at least one pallet-to-order-store affinity characteristic is for at least one of a clustered aisle pallet load parcel delivery method, a mixed-mode clustered and adjacent aisle pallet load parcel delivery method, and an adjacent aisle pallet load parcel delivery method at the order store.

According to one or more aspects of the present disclosure, at least one pallet-to-order-store affinity characteristic is informed by a repeated double-loop determination, at least one loop of which relates order-store aisles to one another.

According to one or more aspects of the present disclosure, within at least one loop determination, order store aisles are related to each other by at least one of an aisle-to-aisle affinity characteristic and a product group type to product group type affinity characteristic.

According to one or more aspects of the present disclosure, the aisle-to-aisle affinity characteristic is the distance separating one order store aisle from another order store aisle, or the contiguity or proximity of one order store aisle to another order store aisle.

According to one or more aspects of the present disclosure, at least one pallet-to-order-store affinity characteristic is informed by repeating a double-loop determination, at least one of which loops determines available combinations of order-store aisles to resolve the arrangement of packages in a pallet load.

According to one or more aspects of the present disclosure, each available combination of order store aisles is determined based on maximization of pallet load, or maximization of a combination of aisle contiguity or adjacency and pallet load in the available combinations, where maximization of pallet load is weighted higher than aisle contiguity or adjacency.

According to one or more aspects of the present disclosure, each available combination of order store aisles is determined based more on the contiguity or proximity of the order store aisles in the available combination and less on maximization of pallet loads.

According to one or more aspects of the present disclosure, pallet loads are parsed based on at least one pallet-to-order-store affinity characteristic such that the pallet loads are maximized with respect to at least one of a maximum pallet load volume and a maximum pallet load weight.

According to one or more aspects of the present disclosure, a pallet load is parsed based on at least one pallet-to-order-store affinity characteristic such that the pallet load has a maximum number of packages from a minimum number of order-store aisles.

According to one or more aspects of the present disclosure, pallet loads are parsed based on at least one pallet-to-order-store affinity characteristic to generate a minimum number of pallet loads for each order store.

According to one or more aspects of the present disclosure, pallet loads are parsed according to at least one pallet-to-order-store affinity characteristic such that for each pallet load bound for an order store, the packages forming the pallet load represent a minimum quantity for an order store aisle.

According to one or more aspects of the present disclosure, pallet loads are parsed according to at least one pallet-to-order-store affinity characteristic such that for each pallet load going to an order store, the parsed pallet load represents a minimum quantity for an order store aisle.

According to one or more aspects of the present disclosure, each pallet load is sequentially parsed via a repeated double loop determination informing at least one pallet-to-order store affinity characteristic.

According to one or more aspects of the present disclosure, at least one pallet-to-order-store affinity characteristic is informed by a dual nested loop determination, at least one of which relates order-store aisles to one another or determines available combinations of order-store aisles to resolve the arrangement of packages in a pallet load.

According to one or more aspects of the present disclosure, a pallet load includes: more than one composite layer of packages stacked on a pallet base; wherein the more than one composite layer of packages is formed by packages arranged in the pallet load, which embodies at least one pallet-to-order store affinity characteristic of a predetermined method for pallet load package delivery at an order store.

According to one or more aspects of the present disclosure, at least one pallet-to-order-store affinity characteristic is for at least one of a clustered aisle pallet load parcel delivery method, a mixed-mode clustered and adjacent aisle pallet load parcel delivery method, and an adjacent aisle pallet load parcel delivery method at the order store.

According to one or more aspects of the present disclosure, at least one pallet-to-order-store affinity characteristic is informed by a repeated double-loop determination, at least one loop of which relates order-store aisles to one another.

According to one or more aspects of the present disclosure, within at least one loop determination, order store aisles are related to each other by at least one of an aisle-to-aisle affinity characteristic and a product group type to product group type affinity characteristic.

According to one or more aspects of the present disclosure, the aisle-to-aisle affinity characteristic is the distance separating one order store aisle from another order store aisle, or the contiguity or proximity of one order store aisle to another order store aisle.

According to one or more aspects of the present disclosure, at least one pallet-to-order-store affinity characteristic is informed by repeating a double-loop determination, at least one of which loops determines available combinations of order-store aisles to resolve the arrangement of packages in a pallet load.

According to one or more aspects of the present disclosure, each available combination of order store aisles is determined based on maximization of pallet load, or maximization of a combination of aisle contiguity or adjacency and pallet load in the available combinations, where maximization of pallet load is weighted higher than aisle contiguity or adjacency.

According to one or more aspects of the present disclosure, each available combination of order store aisles is determined based more on the contiguity or proximity of the order store aisles in the available combination and less on the maximization of pallet loads.

According to one or more aspects of the present disclosure, pallet loads are parsed based on at least one pallet-to-order-store affinity characteristic such that the pallet load is maximized with respect to at least one of a maximum pallet load volume and a maximum pallet load weight.

According to one or more aspects of the present disclosure, a pallet load is parsed based on at least one pallet-to-order-store affinity characteristic such that the pallet load has a maximum number of packages from a minimum number of order-store aisles.

According to one or more aspects of the present disclosure, pallet loads are parsed based on at least one pallet-to-order-store affinity characteristic to generate a minimum number of pallet loads for each order store.

According to one or more aspects of the present disclosure, pallet loads are parsed according to at least one pallet-to-order-store affinity characteristic such that for each pallet load bound for an order store, the packages forming the pallet load represent a minimum quantity for an order store aisle.

According to one or more aspects of the present disclosure, pallet loads are parsed according to at least one pallet-to-order-store affinity characteristic such that for each pallet load going to an order store, the parsed pallet load represents a minimum quantity for an order store aisle.

According to one or more aspects of the present disclosure, each pallet load is sequentially parsed via a repeated double loop determination that informs at least one pallet to order store affinity characteristic. According to one or more aspects of the present disclosure, at least one pallet to order store affinity characteristic is informed by a double nested loop determination, at least one loop of which relates order store aisles to each other or determines an available combination of order store aisles, thereby parsing the arrangement of packages in the pallet load. It should be understood that the foregoing description is merely illustrative of aspects of the present disclosure. Various alternatives and modifications may be conceived by those skilled in the art without departing from the aspects of the present disclosure. Therefore, the various aspects of the present disclosure are intended to include all such alternatives, modifications and variations that fall within the scope of any of the appended claims. In addition, the mere fact that different features are recited in mutually different dependent or independent claims does not indicate that a combination of these features cannot be used advantageously, and such a combination is still within the scope of aspects of the present disclosure.

Claims (60)

1. A material handling system for handling and placing packages onto trays for an order store, the material handling system comprising:

A storage array having a storage space for holding packages therein;

An automated package transport system communicatively coupled to the storage array for storing packages within and retrieving packages from storage space of the storage array;

An automated palletizer for placing packages on trays to form a tray load, the automated palletizer communicatively connected to an automated package transport system, the automated package transport system configured to provide individual packages from a storage array to the automated palletizer to form a tray load, the tray load comprising more than one composite tier of packages; and

A controller operatively connected to the automated palletizer, the controller programmed with a pallet load generator having at least one pallet-to-order store affinity characteristic for a predetermined method of pallet load parcel delivery at the order store, the pallet load generator configured such that the pallet load is formed by the automated palletizer reflecting parcels disposed in the pallet load of the at least one pallet-to-order store affinity characteristic.

2. The material processing system of claim 1, wherein the at least one tray-to-order store affinity characteristic is for at least one of a clustered aisle tray load package delivery method, a mixed mode clustered and adjacent aisle tray load package delivery method, and an adjacent aisle tray load package delivery method at an order store.

3. The material processing system of claim 1, wherein the at least one pallet-to-order store affinity characteristic is notified by repeating a dual cycle determination, at least one cycle of which correlates order store aisles to one another.

4. The material processing system of claim 3, wherein, within the determination of at least one cycle, the order store aisles are related to each other by at least one of an aisle-to-aisle affinity characteristic and a product group type-to-product group type affinity characteristic.

5. The material handling system of claim 4, wherein the aisle-to-aisle affinity characteristic is a distance separating one order store aisle from another order store aisle, or an adjacency or adjacency of one order store aisle to another order store aisle.

6. The material processing system of claim 1, wherein the arrangement of packages in the pallet load is resolved by repeating a dual cycle determination to inform at least one pallet-to-order store affinity characteristic, at least one cycle of which determines available combinations of order store aisles.

7. The material processing system of claim 6, wherein each available combination of order store aisles is determined based on:

maximising tray load, or

The adjacency or adjacency of aisles and tray loads in a combination can be maximized,

Wherein the maximization of the pallet load is weighted higher than the adjacency or adjacency of aisles.

8. The material processing system of claim 6, wherein each available combination of order store aisles is determined based more on an adjacency or adjacency of order store aisles in the available combinations and less on a maximization of pallet load.

9. The material processing system of claim 1, wherein the tray load generator resolves the tray load according to at least one tray-to-order store affinity characteristic such that the tray load is maximized relative to at least one of a maximum tray load volume and a maximum tray load weight.

10. The material processing system of claim 1, wherein the tray load generator parses the tray load from at least one tray-to-order store affinity characteristic such that the tray load has a maximum number of packages from a minimum number of order store passes.

11. The material processing system of claim 1, wherein the pallet load generator resolves the pallet load according to at least one pallet-to-order store affinity characteristic to generate a minimum number of pallet loads for each order store.

12. The material processing system of claim 1, wherein the pallet load generator resolves the pallet loads according to at least one pallet-to-order store affinity characteristic such that for each pallet load going to an order store, a package forming the pallet load represents a minimum number of order store passes.

13. The material processing system of claim 1, wherein the pallet load generator resolves the pallet load according to at least one pallet-to-order store affinity characteristic such that for each pallet load going to an order store, the resolved pallet load represents a minimum number of order store passes.

14. The material processing system of claim 1, wherein the pallet load generator is configured to sequentially parse each pallet load via a repeated two-cycle determination informing of at least one pallet-to-order store affinity characteristic.

15. The material processing system of claim 1, wherein the at least one pallet-to-order store affinity characteristic is notified by a dual nested loop determination, at least one of which loops relates order store aisles to each other or determines available combinations of order store aisles to resolve placement of packages in the pallet load.

16. An automated palletizer, comprising:

An automated package picking apparatus capable of moving packages from a package storage section to a tray to form a tray load from the packages, the tray load comprising more than one composite layer of packages; and

A controller operatively connected to the automated palletizer, the controller programmed with a pallet load generator having at least one pallet-to-order store affinity characteristic for a predetermined method of pallet load parcel delivery at the order store, the pallet load generator configured such that the pallet load is formed by the automated palletizer reflecting parcels disposed in the pallet load of the at least one pallet-to-order store affinity characteristic.

17. An automated palletizer as in claim 16, wherein the at least one pallet-to-order store affinity characteristic is for at least one of a clustered aisle pallet load parcel delivery method, a mixed mode clustered and adjacent aisle pallet load parcel delivery method, and an adjacent aisle pallet load parcel delivery method at the order store.

18. An automated palletizer as in claim 16, wherein at least one pallet-to-order store affinity characteristic is notified by repeating a dual cycle determination, at least one cycle of which correlates order store aisles to one another.

19. An automated palletizer according to claim 18, wherein, within the determination of at least one cycle, the order store aisles are related to each other by at least one of aisle-to-aisle affinity characteristics and product group type-to-product group type affinity characteristics.

20. An automated palletizer as in claim 19, wherein the aisle-to-aisle affinity characteristic is a distance separating one order store aisle from another order store aisle, or an adjacency or adjacency of one order store aisle to another order store aisle.

21. An automated palletizer as in claim 16, wherein the arrangement of packages in the pallet load is resolved by repeating a dual cycle determination to inform at least one pallet-to-order store affinity characteristic, at least one cycle of which determines available combinations of order store aisles.

22. An automated palletizer as in claim 21, wherein each available combination of order store aisles is determined based on:

maximising tray load, or

The adjacency or adjacency of aisles and tray loads in a combination can be maximized,

Wherein the maximization of the pallet load is weighted higher than the adjacency or adjacency of aisles.

23. An automated palletizer as in claim 21, wherein each available combination of order store aisles is determined based more on the adjacency or adjacency of the order store aisles in the available combinations and less on the maximization of pallet load.

24. An automated palletizer as in claim 16, wherein the pallet load generator resolves the pallet load according to at least one pallet-to-order store affinity characteristics such that the pallet load is maximized relative to at least one of a maximum pallet load volume and a maximum pallet load weight.

25. An automated palletizer as in claim 16, wherein the pallet load generator resolves the pallet load according to at least one pallet-to-order store affinity characteristic such that the pallet load has a maximum number of packages from a minimum number of order store passes.

26. An automated palletizer as in claim 16, wherein the pallet load generator resolves the pallet loads according to at least one pallet-to-order store affinity characteristic, thereby generating a minimum number of pallet loads for each order store.

27. An automated palletizer as in claim 16, wherein the pallet load generator resolves the pallet loads according to at least one pallet-to-order store affinity characteristic such that for each pallet load going to an order store, the packages forming the pallet load represent a minimum number of order store passes.

28. An automated palletizer as in claim 16, wherein the pallet load generator resolves the pallet loads according to at least one pallet-to-order store affinity characteristics such that for each pallet load going to an order store, the resolved pallet load represents a minimum number of order store passes.

29. An automated palletizer as in claim 16, wherein the pallet load generator is configured to sequentially parse each pallet load via repeated dual cycle determinations of at least one pallet-to-order store affinity characteristics.

30. An automated palletizer as in claim 16, wherein the placement of packages in the pallet load is resolved by dual nested loop determination informing at least one pallet-to-order store affinity characteristics, at least one of which loops relates order store aisles to each other or determines available combinations of order store aisles.

31. A method for building a pallet load, the method comprising:

placing the packages on a tray to form a tray load, wherein individual packages are provided from a storage array to form a tray load, the tray load comprising more than one composite layer of packages; and

Wherein the pallet load is formed of packages arranged in the pallet load, which embody at least one pallet-to-order store affinity characteristic of a predetermined method for pallet load package delivery at the order store.

32. The method of claim 31, wherein the at least one tray-to-order store affinity characteristic is for at least one of a clustered aisle tray load package delivery method, a mixed mode clustered and adjacent aisle tray load package delivery method, and an adjacent aisle tray load package delivery method at an order store.

33. The method of claim 31, wherein the at least one pallet-to-order store affinity characteristic is notified by repeating a dual cycle determination, at least one cycle of which correlates order store aisles to one another.

34. The method of claim 33, wherein, within the determination of at least one cycle, the order store aisles are related to each other by at least one of an aisle-to-aisle affinity characteristic and a product group type-to-product group type affinity characteristic.

35. The method of claim 34, wherein the aisle-to-aisle affinity characteristic is a distance separating one order store aisle from another order store aisle, or an adjacency or adjacency of one order store aisle to another order store aisle.

36. The method of claim 31, wherein the arrangement of packages in the pallet load is resolved by repeating a dual cycle determination to inform at least one pallet-to-order store affinity characteristic, at least one cycle of which determines available combinations of order store aisles.

37. The method of claim 36, wherein each available combination of order store aisles is determined based on:

maximising tray load, or

The adjacency or adjacency of aisles and tray loads in a combination can be maximized,

Wherein the maximization of the pallet load is weighted higher than the adjacency or adjacency of aisles.

38. The method of claim 36, wherein each available combination of order store aisles is determined based more on the adjacency or adjacency of the order store aisles in the available combinations and less on the maximization of tray load.

39. The method of claim 31, wherein the pallet load is parsed according to at least one pallet-to-order store affinity characteristic such that the pallet load is maximized relative to at least one of a maximum pallet load volume and a maximum pallet load weight.

40. The method of claim 31, wherein the tray load is parsed according to at least one tray-to-order store affinity characteristic such that the tray load has a maximum number of packages from a minimum number of order store passes.

41. The method of claim 31, wherein the pallet load is resolved according to at least one pallet-to-order store affinity characteristic to generate a minimum number of pallet loads for each order store.

42. The method of claim 31, wherein the pallet loads are parsed according to at least one pallet-to-order store affinity characteristic such that for each pallet load going to an order store, a package forming the pallet load represents a minimum number of order store passes.

43. The method of claim 31, wherein the pallet loads are parsed according to at least one pallet-to-order store affinity characteristic such that for each pallet load going to an order store, the parsed pallet load represents a minimum number of order store passes.

44. The method of claim 31, wherein each pallet load is parsed sequentially via a repeated two-cycle determination of at least one pallet-to-order store affinity characteristic.

45. The method of claim 31, wherein the at least one tray-to-order store affinity characteristic is notified by a dual nested loop determination, at least one of which loops relates order store aisles to each other or determines available combinations of order store aisles to resolve the placement of packages in the tray load.

46. A pallet load, comprising:

more than one composite layer of wrap stacked on the tray base;

Wherein more than one composite layer of packages is formed from packages arranged in a pallet load, which embodies at least one pallet-to-order store affinity characteristic of a predetermined method for pallet load package delivery at an order store.

47. The pallet load of claim 46, wherein the at least one pallet-to-order store affinity characteristic is for at least one of a clustered aisle pallet load package distribution method, a mixed mode clustered and adjacent aisle pallet load package distribution method, and an adjacent aisle pallet load package distribution method at the order store.

48. The pallet load of claim 46, wherein the at least one pallet-to-order store affinity characteristic is notified by repeating a dual cycle determination, at least one cycle of which relates order store aisles to each other.

49. The pallet load of claim 48, wherein, within the determination of at least one cycle, the order store aisles are related to each other by at least one of an aisle-to-aisle affinity characteristic and a product group type-to-product group type affinity characteristic.

50. The pallet load of claim 49, wherein the aisle-to-aisle affinity characteristic is a distance separating one order store aisle from another order store aisle, or an adjacency or adjacency of one order store aisle to another order store aisle.

51. The pallet load of claim 46, wherein the arrangement of packages in the pallet load is resolved by repeating a dual cycle determination to inform at least one pallet-to-order store affinity characteristic, at least one cycle of which determines available combinations of order store aisles.

52. The pallet load of claim 51, wherein each available combination of order store aisles is determined based on:

maximising tray load, or

The adjacency or adjacency of aisles and tray loads in a combination can be maximized,

Wherein the maximization of the pallet load is weighted higher than the adjacency or adjacency of aisles.

53. The pallet load of claim 51, wherein each available combination of order store aisles is determined based more on the adjacency or adjacency of the order store aisles in the available combinations and less on the maximization of the pallet load.

54. The pallet load of claim 46, wherein the pallet load is parsed according to at least one pallet-to-order store affinity characteristic such that the pallet load is maximized relative to at least one of a maximum pallet load volume and a maximum pallet load weight.

55. The pallet load of claim 46, wherein the pallet load is parsed according to at least one pallet-to-order store affinity characteristic such that the pallet load has a maximum number of packages from a minimum number of order store passes.

56. The pallet load of claim 46, wherein the pallet load is parsed according to at least one pallet-to-order store affinity characteristic, thereby generating a minimum number of pallet loads for each order store.

57. The pallet load of claim 46, wherein the pallet load is parsed according to at least one pallet-to-order store affinity characteristic such that for each pallet load going to an order store, the package forming the pallet load represents a minimum number of order store passes.

58. The pallet load of claim 46, wherein the pallet load is parsed according to at least one pallet-to-order store affinity characteristic such that for each pallet load going to an order store, the parsed pallet load represents a minimum number of order store passes.

59. The pallet load of claim 46, wherein each pallet load is parsed sequentially via a repeated two-cycle determination of at least one pallet-to-order store affinity characteristic.

60. The pallet load of claim 46, wherein the at least one pallet-to-order store affinity characteristic is notified by a dual nested loop determination, at least one of which loops relates order store aisles to each other or determines available combinations of order store aisles to resolve the placement of packages in the pallet load.