CN116957440A - Large-scale power generation group-oriented coal transportation planning method and system - Google Patents
Large-scale power generation group-oriented coal transportation planning method and system Download PDFInfo
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Abstract
The invention discloses a coal transportation planning method and system for a large power generation group, wherein the method comprises the following steps: constructing a purchase-allocation coordination optimization model before a coal month by using purchase-allocation history data, constructing a large-scale ship unified scheduling model by using the relation between the number of used ships and the carrying capacity utilization rate of the ships, and constructing a purchase-transportation-inventory management two-layer optimization model based on the upper-layer purchase-allocation coordination optimization model and the lower-layer large-scale ship unified scheduling model; solving an upper layer purchase-allocation coordination optimization model by adopting a linear programming algorithm, and outputting a total purchase quantity plan and a total freight quantity plan in a scheduling period; and acquiring the state quantity of the wharf system of the power plant at the current moment, solving a lower-layer large-scale ship unified scheduling model by adopting a greedy algorithm based on the total purchase quantity plan, the total transport quantity plan and the state quantity of the wharf system of the power plant, and outputting a ship optimization scheduling plan. The invention reduces port unloading times while guaranteeing safe production.
Description
Technical Field
The invention relates to the technical field of coal transportation planning, in particular to a large-scale power generation group-oriented coal transportation planning method and system.
Background
Cost control and reasonable distribution of coal are key to the development of large power generation groups. Taking a certain group as an example, the coal purchasing cost in 2012 is more than 300 hundred million yuan; the transportation cost is about 20 hundred million yuan; the lag time cost caused by uncontrollable factors and unreasonable transportation arrangement is about 7000 ten thousand yuan. Therefore, how to meet the premise of safe production of a power plant for a large power generation enterprise, the benefits of shipping companies are considered, and unreasonable coal purchasing-transporting plans are avoided, so that the total cost of coal is saved, and the problem is worth researching.
The above practical problems mainly comprise three aspects of coordination optimization: purchase optimization, transportation optimization and inventory management. In the existing comprehensive purchasing-transporting-inventory management method, related researches have established a better overall optimization model frame, so that the problems of coal proportioning, limitation of the maximum berthing tonnage of a wharf and the like are solved, but a certain gap exists between the existing model and the actual situation, and the method mainly comprises the following steps of: 1) It is generally assumed that the types of vehicles are small and the number available is sufficiently large, which is a great deal of in-and-out from the actual situation where the ship types in the fleet are diversified; 2) The voyage time is usually ignored, which assumption is against the characteristic of long sea transport time; 3) The number of ships that can berth at the power plant dock is not considered, and the assumption does not meet the reality that the number of berths at the power plant dock is small. If this constraint is not considered, the dock of the power plant may be congested due to centralized ship dispatching, and thus, unnecessary dead time costs may be incurred due to increased berthing time of the ship. In addition, from the aspect of the method, the existing research mainly adopts the integral modeling idea, and utilizes the existing algorithm of mixed integer programming or MDP (Markov Decision Process) dynamic programming method to solve, wherein the scale of the mixed integer programming model is often larger, so that the solution is complex, and the feasible solution of the problem is difficult to obtain in a reasonable time.
Disclosure of Invention
In order to overcome the defects and the shortcomings of the prior art, the invention aims to provide a large-scale power generation group-oriented coal transportation planning method and system.
In order to achieve the above purpose, the invention adopts the following technical means:
the first aspect of the invention provides a coal transportation planning method for a large power generation group, which comprises the following steps:
constructing a purchase-allocation coordination optimization model before a coal month by using purchase-allocation history data, constructing a large-scale ship unified scheduling model by using the relation between the number of used ships and the carrying capacity utilization rate of the ships, and constructing a purchase-transportation-inventory management two-layer optimization model based on the upper-layer purchase-allocation coordination optimization model and the lower-layer large-scale ship unified scheduling model;
solving an upper layer purchase-allocation coordination optimization model by adopting a linear programming algorithm, and outputting a total purchase quantity plan and a total freight quantity plan in a scheduling period;
acquiring the state quantity of a power plant wharf system at the current moment, wherein the state quantity of the power plant wharf system comprises the following components: residual coal transportation task amount information, a ship state information table, a power plant wharf inventory information table, an inventory warning time information table and an available berth number information table; and solving a lower-layer large-scale ship unified scheduling model by adopting a greedy algorithm based on the total purchase quantity plan, the total traffic quantity plan and the state quantity of the power plant wharf system, and outputting a ship optimization scheduling plan.
As a further improvement of the present invention, the upper layer purchase-shipment coordination optimization model includes:
A. minimum construction objective function from sum of coal purchase cost and transportation cost
B. Balance constraint of purchase quantity
C. Demand balance constraint
D. Vendor-related constraints
1) Maximum purchase amount constraint
2) Purchasing proportion constraint
E. Decision quantity constraint
Wherein, the subscript M represents harbor number 1 … M, the subscript N represents harbor number 1 … N, the subscript S represents supplier number 1 … S, and the subscript L represents coal type 1 … L; constant valueRepresenting coal purchasing unit price, constant->Represents the shipping unit price, constant->Represents the coal demand, constant->Represent the upper limit of the purchase quantity, constant alpha s Representing an upper limit of the purchase proportion; variable->Representing the purchase quantity of coal in the dispatching period, and the variable +.>Indicating the amount of coal traffic during the scheduling period.
As a further improvement of the present invention, the lower-layer large-scale ship unified shift model includes:
A. objective function
B. Traffic planning constraints
C. Dock inventory state transition
D. Wharf safety stock constraints
E. Dock physical restraint
1) Dock berth number constraint
2) Constraint of maximum berthing tonnage of ship
F. Ship maximum load restraint
G. Ship scheduling plan variable constraint
x t,m,n,v ＝f(x 1,m',n',v ,x 2,m',n',v ...,x t-1,m',n',v ) (17)
H. Decision quantity constraint
x t,m,n,v ＝0 or 1 (18)
Wherein, the subscript T represents a discrete period of 1 … T, and the subscript V represents a vessel number of 1 … V; constant valueRepresents an initial stock quantity, constant +.>Represent the upper/lower limit of the stock quantity, constant d t,n,l Represents the coal consumption, constant->Represents the unloading time of the ship at the port unloading dock, constant +.>Representing the number of berths of the port unloading dock, constant +.>Represents the maximum load of the ship, constant->Representing the maximum berthing tonnage of the port unloading wharf; variable->Represents the end coal inventory of period t, variable x t,m,n,v Representing the schedule variable, x t,m,n,v When=1, the end of period t is indicated, ship v is arranged to route (m-n), x t,m,n,v =0, indicating the end of period t, no ship v is arranged to route (m-n), variable ∈ ->Representing the end of period t, arranging vessel v to route (m-n) to transport the coal load of the first coal; function f (x 1,m',n',v ,x 2,m',n',v ...,x t-1,m',n',v ) Indicating that the current ship scheduling plan variable is a function of all of the historical ship scheduling plan variables.
As a further improvement of the invention, the method adopts a greedy algorithm to solve the unified scheduling model of the lower-layer large-scale ship, and outputs the optimized scheduling plan of the ship, which comprises the following steps:
s3.1, according to the total purchase amount plan and the total transport amount plan, the total transport planPower plant dock initial inventoryQuantity->The number of berths of a power plant wharf >Initializing each information table specifically comprises the following steps: the system comprises a residual coal transportation task amount information table, a ship state information table, a power plant wharf inventory information table, an inventory warning time information table and an available berth number information table, wherein the specific steps are as follows:
residual coal transportation task amount information table
Ship state information table S v,t ＝1(21)
Power plant dock inventory information table
Inventory alert time information table
Available berth number information table
Wherein the variables areRepresenting the number of available berths at the end of a period T for the power plant dock n for preventing congestion of the power plant dock, T m,n Representing the voyage time of the route (m-n);
s3.2, reading an available ship information table, a residual coal transportation task amount information table, a power plant wharf inventory information table, an inventory warning time information table and an available berth number information table at the moment t; judging whether available ships exist, if so, calculating a route set H capable of arranging transportation tasks t Otherwise let t=t+1,re-executing the step; wherein, set H t An airline set representing that a transport task can be scheduled at the end of a period t, any element representing an airline (m, n) for which a transport task can be scheduled at the end of a period t, and an airline set H for which a transport task can be scheduled at the end of a period t t Any one element (m, n) of (a) satisfies the formula (25);
S3.3 if H t Not empty, then on the route set H where transport tasks can be scheduled t If not, let t=t+1, and return to S3.2;
s3.4, establishing a knapsack model with a smaller scale by taking the ship load adaptation principle, and optimally solving the single coal transportation quantity and the transportation ship of the transportation task in the period;
s3.5, updating each information table based on the single coal transportation amount of the transportation tasks and the transportation ship, and circulating the steps until the arrangement of all the transportation tasks is completed.
As a further improvement of the invention, the inventory warning time parameterThe specific calculation formula is as follows:
wherein ,indicating the duration in which safe production can be maintained, T m,n Representing the voyage time of the route (m-n);
representing that the first coal transport on the scheduled course (m-n) at the end of period t cannot meet the minimum safe storage lower limit for jetty n +.>Requirements;
representing a first coal transport on a scheduled course (m-n) at the end of the period t; and for different transport tasks (m, n, l), are provided>Smaller means greater demand for coal transportation, the more urgent the transportation task.
As a further improvement of the invention, the method for establishing a knapsack model with smaller scale to optimize and solve single coal transportation amount of the transportation task in the period and the transportation ship by taking the ship load as a principle comprises the following steps:
Defining the system state quantity as the information of the residual coal transportation task quantityShip state information table->Power plant dock inventory information table->The knapsack model specifically comprises:
A. objective function
B. Available ship set constraint
T is recorded 0 The time available ships are assembled intoThen->Any element v of the formula (30)
C. Limitation constraint of maximum berthing tonnage of wharf
D. Upper limit constraint of safety stock
E. Ship load adapting principle
Wherein set V t Represents the set of available vessels at the end of the period t, any element of which represents the available vessels v at the end of the period t,representing t 0 The ship collection can be used at any time; variable S v,t Representing a vessel state variable, i.e. whether the vessel v is available at time t, S v,t =1 means that vessel v is available at time t, S v,t =0 indicates that the ship is not available; variable->Representing the remaining coal transportation mission amount of the transportation mission (m-n-l); variable r t,m,n,l Representing the end of period t, arranging the ship to perform part of the coal transportation of the transportation task (m-n-l), wherein the transportation quantity is r;
the optimal solutions of formulas (27) - (35) were noted asI.e. at t 0 Time-of-day ship v * Transporting coal, wherein the optimal single-pass traffic is +.>
As a further improvement of the present invention, the single coal traffic and transport ship update information tables based on the transport tasks include:
1) Remaining coal transportation mission update
If a transport task (m) 0 -n 0 -l 0 ) Part of the volume of transportThe remaining coal transportation task amount is reduced as follows:
2) Ship status information update
Assuming that the navigation time is the same as the return time, if t 0 Time-of-day ship v * Transporting coal, the occupied period of the vessel includes: three periods of sailing, unloading and returning, i.e. duringCannot be reused for a period of time as follows:
3) Power plant dock inventory information update
After the coal arrives at the dock, the dock inventory information is updated, such as:
4) Inventory alert time update
Variable(s)Representing an inventory warning time of a transportation task (m-n-l) for measuring the urgency of the transportation task; begin to transport coal->To the wharf n of the power plant 0 When the inventory warning time is updated, the inventory warning time is updated as follows:
5) Available berth number information table update
If the ship is arranged to transport coal to the wharf n of the power plant at the moment t 0 Dock n 0 At the position ofAn occupied berth is added in the period, and the number of available berths is reduced by 1, specifically:
the second aspect of the invention provides a coal transportation planning system for large power generation groups, comprising:
the model construction module is used for constructing a purchase-transfer coordination optimization model before a coal month by using purchase-transfer history data, constructing a large-scale ship unified scheduling model by using the relation between the number of used ships and the carrying capacity utilization rate of the ships, and constructing a purchase-transport-inventory management two-layer optimization model based on the upper-layer purchase-transfer coordination optimization model and the lower-layer large-scale ship unified scheduling model;
The upper layer solving module is used for solving the upper layer purchasing-dispatching coordination optimization model by adopting a linear programming algorithm and outputting a total purchasing quantity plan and a total transporting quantity plan in a dispatching period;
the lower layer solving module is used for obtaining the state quantity of the power plant wharf system at the current moment, and the state quantity of the power plant wharf system comprises: residual coal transportation task amount information, a ship state information table, a power plant wharf inventory information table, an inventory warning time information table and an available berth number information table; and solving a lower-layer large-scale ship unified scheduling model by adopting a greedy algorithm based on the total purchase quantity plan, the total traffic quantity plan and the state quantity of the power plant wharf system, and outputting a ship optimization scheduling plan.
The third aspect of the invention provides an electronic device comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the coal transportation planning method facing to the large power generation group when executing the computer program.
A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program which, when executed by a processor, implements the large-power-generation-group-oriented coal transportation planning method.
Compared with the prior art, the invention has the following beneficial effects:
according to the coal transportation planning method disclosed by the invention, the coal purchasing cost and the transportation cost of a large power generation group, the ship 'load-adapting principle' of a shipping company and the benefit of a subordinate power plant safety production are comprehensively considered, a more practical optimization model is established, on the basis of the analysis problem characteristics, the complex coal purchasing-transportation-inventory management problem is equivalently decoupled, two-layer optimization models are established, the coal purchasing problem and the ship scheduling problem are respectively solved, the model solving complexity caused by a unified modeling mode in the traditional research is reduced, and a greedy algorithm is designed to solve the large-scale ship unified scheduling problem. The purchasing plan obtained by optimizing the solving can well reduce the enterprise cost, the shipping plan can well guide the ship scheduling, the port unloading times are reduced while the safe production is ensured, and the problems of port unloading dock congestion and the like are avoided.
Drawings
FIG. 1 is a flow chart of a coal transportation planning method for a large power generation group;
FIG. 2 is a diagram of steps of a method according to an embodiment of the present invention;
FIG. 3 is a variable relationship diagram of a procurement-ship coordination optimization model;
FIG. 4 is a flowchart of an algorithm for solving the unified scheduling problem of the lower-layer large-scale ship by adopting a greedy algorithm;
FIG. 5 is a task time diagram of a power plant dock N1;
FIG. 6 is a diagram of a power plant dock N1 inventory change;
FIG. 7 is a flowchart of a coal transportation planning method for large power generation groups according to an embodiment of the invention;
FIG. 8 is a coal transportation planning system for large power generation groups, provided by the invention;
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
As shown in fig. 1, a first object of the present invention is to provide a coal transportation planning method for a large power generation group, which includes the following steps:
the first aspect of the invention provides a coal transportation planning method for a large power generation group, which comprises the following steps:
s1, constructing a purchase-transfer coordination optimization model before a coal month by using purchase-transfer historical data, constructing a large-scale ship unified scheduling model by using the relation between the number of used ships and the carrying capacity utilization rate of the ships, and constructing a purchase-transport-inventory management two-layer optimization model based on the upper-layer purchase-transfer coordination optimization model and the lower-layer large-scale ship unified scheduling model;
s2, solving an upper layer purchase-allocation coordination optimization model by adopting a linear programming algorithm, and outputting a total purchase quantity plan and a total freight quantity plan in a scheduling period;
s3, acquiring the state quantity of a power plant wharf system at the current moment, wherein the state quantity of the power plant wharf system comprises: residual coal transportation task amount information, a ship state information table, a power plant wharf inventory information table, an inventory warning time information table and an available berth number information table; and solving a lower-layer large-scale ship unified scheduling model by adopting a greedy algorithm based on the total purchase quantity plan, the total traffic quantity plan and the state quantity of the power plant wharf system, and outputting a ship optimization scheduling plan.
The method of the embodiment of the invention uniformly considers the problems of coal purchasing-transporting-inventory management, and constructs a large-scale sea transportation optimizing and scheduling method with low model complexity and reasonable solving time. According to the invention, the purchase-transportation-inventory management problem is uniformly considered, a two-layer optimization model is established, and the model solving complexity caused by a uniform modeling mode in the traditional research is reduced; a greedy algorithm is designed, so that the solving time of a large-scale marine inventory path optimization problem is reduced; the obtained purchasing plan can well reduce the enterprise cost, the obtained shipping plan can well guide the ship scheduling, the port unloading times are reduced while the safe production is ensured, and the problems of port unloading dock congestion and the like are avoided.
The greedy algorithm is designed to solve the lower-layer optimization model, the large-scale ship scheduling problem is converted into a series of small-scale knapsack problems to be solved independently, and the solving time of the large-scale ship scheduling problem is shortened.
The present invention will be described in detail with reference to the following examples:
the invention discloses a coal transportation planning method for a large power generation group. The description is mainly made about the following aspects:
firstly, uniformly considering the purchase-transportation-inventory management problem, and establishing a two-layer optimization model: the upper layer is to build a purchase-allocation coordination optimization model before a coal month with minimum total cost, and the lower layer is to build a ship unified scheduling model with minimum ship use number and maximum ship load capacity utilization rate;
Then, solving an upper layer purchase-allocation coordination optimization model by using a linear programming algorithm, and outputting a total purchase quantity plan and a total freight quantity plan in a scheduling period;
and finally, solving the total traffic plan in the obtained scheduling period according to the upper model, and designing a greedy algorithm to solve the problem of uniform scheduling of the large-scale ships of the lower model.
As shown in fig. 2, the embodiment of the invention discloses a coal transportation planning method for a large power generation group, which comprises the following steps:
step 1: and uniformly considering the purchase-transportation-inventory management problem, and establishing a two-layer optimization model. The upper layer is used for establishing a purchase-allocation coordination optimization model before a coal month with minimum total cost, and the lower layer is used for establishing a large-scale ship unified scheduling model with minimum ship use number and maximum ship load capacity utilization rate. The method comprises the following steps:
(1) The upper layer purchase-allocation coordination optimization model is as follows:
A. objective function
The formula (1) represents that the sum of coal purchase cost and transportation cost is minimum.
B. Balance constraint of purchase quantity
Equation (2) represents that the total purchase amount of a certain type of coal at all suppliers at a certain port is equal to the total traffic amount of the type of coal from the port to all terminals during the scheduling period.
C. Demand balance constraint
Equation (3) shows that during a dispatch period, the coal traffic delivered from all ports to a terminal meets the terminal coal consumption.
D. Vendor-related constraints
1) Maximum purchase amount constraint
Equation (4) limits the purchase amount of each type of coal from each provider at each port to not more than its maximum supply amount.
2) Purchasing proportion constraint
To prevent a certain vendor from monopolizing the coal market, equation (5) considers the upper limit of the purchasing proportion of each vendor.
E. Decision quantity constraint
The variable relationship of the upper layer purchase-dispatch coordination optimization model is shown in fig. 3.
Wherein, the subscript M represents harbor number 1 … M, the subscript N represents harbor number 1 … N, the subscript S represents supplier number 1 … S, and the subscript L represents coal type 1 … L; constant valueRepresenting coal purchasing unit price, constant->Represents the shipping unit price, constant->Represents the coal demand, constant->Represent the upper limit of the purchase quantity, constant alpha s Representing an upper limit of the purchase proportion; variable->Representing the purchase quantity of coal in the dispatching period, and the variable +.>Indicating the amount of coal traffic during the scheduling period.
(2) Lower-layer large-scale ship unified shift-arrangement model
A. Objective function
Equation (8) represents that the number of ship uses is minimized; the ship load capacity utilization rate of the formula (9) is maximized.
B. Traffic planning constraints
Equation (10) represents the total volume of a certain type of coal over all time periods equal to the total volume schedule for that type of coal during the scheduling period.
C. Dock inventory state transition
Equations (11) - (12) represent the inventory state transitions and initial inventory constraints for jetty n. Wherein at T-T m,n The coal transportation task arranged at the moment is bought at the moment T, so that the stock increment at the moment T is T-T m,n Time-of-day coal traffic.
D. Wharf safety stock constraints
Equation (13) indicates that at the end of period t, the stock of terminal n should meet the safety stock requirement.
E. Dock physical restraint
1) Dock berth number constraint
Equation (14) indicates that at the end of the period t, the number of vessels at which dock n is berthed is not greater than the number of berths thereof. Wherein, the left side of the formula (14) represents the number of vessels berthed in the t period and the number of vessels not unloaded in the t period.
2) Constraint of maximum berthing tonnage of ship
Equation (15) indicates that during the dispatch period, the tonnage of the ship reaching dock n is not greater than the maximum berthing tonnage of the dock.
F. Ship maximum load restraint
Equation (16) indicates that the capacity of the ship v is not greater than the maximum load capacity thereof.
G. Ship scheduling plan variable constraint
x t,m,n,v ＝f(x 1,m',n',v ,x 2,m',n',v ...,x t-1,m',n',v ) (17)
Equation (17) indicates that the ship resource must be exclusive for a certain period of time. The ship scheduling plan variable for period t is thus related to all lane history ship scheduling plans at time 1 … t-1.
H. Decision quantity constraint
x t,m,n,v ＝0 or 1 (18)
Wherein, the subscript T represents a discrete period of 1 … T, and the subscript V represents a vessel number of 1 … V; constant valueRepresents an initial stock quantity, constant +.>Represent the upper/lower limit of the stock quantity, constant d t,n,l Represents the coal consumption, constant->Represents the unloading time of the ship at the port unloading dock, constant +.>Representing the number of berths of the port unloading dock, constant +.>Represents the maximum load of the ship, constant->Representing the maximum berthing tonnage of the port unloading wharf; variable->Represents the end coal inventory of period t, variable x t,m,n,v Representing the schedule variable, x t,m,n,v When=1, the end of period t is indicated, ship v is arranged to route (m-n), x t,m,n,v =0, indicating the end of period t, no ship v is arranged to route (m-n), variable ∈ ->Representing the end of period t, arranging vessel v to route (m-n) to transport the coal load of the first coal; function f (x 1,m',n',v ,x 2,m',n',v ...,x t-1,m',n',v ) Indicating that the current ship scheduling plan variable isAll historical ship scheduling plan variables.
Step 2: solving an upper layer purchase-allocation coordination optimization model by using a linear programming algorithm, and outputting a total purchase quantity plan and a total transport quantity plan in a scheduling period;
step 3: and according to the total traffic plan in the obtained scheduling period of the upper model solving, a greedy algorithm is designed to solve the problem of uniform scheduling of the large-scale ships of the lower model, and the ship optimization scheduling plan is output. The specific solving algorithm flow chart is shown in fig. 4, and the specific solving method is as follows:
Step 3.1 Total transportation plan in the scheduling period obtained according to the upper layer purchase-scheduling coordination optimization modelPower plant terminal initial stock->The number of berths of a power plant wharf>Initializing each information table, specifically as follows:
residual coal transportation task amount information table
Ship state information table S v,t ＝1(21)
Power plant dock inventory information table
Inventory alert time information table
Available berth number information table
Step 3.2, reading an available ship information table at the time t, a residual coal transportation task amount information table, a power plant wharf inventory information table, an inventory warning time information table and an available berth number information table, and if available ships exist, calculating a route set H capable of arranging transportation tasks t Otherwise t=t+1, the step is re-executed. Wherein, set H t An airline set representing that a transport task can be scheduled at the end of a period t, any element representing an airline (m, n) for which a transport task can be scheduled at the end of a period t, and an airline set H for which a transport task can be scheduled at the end of a period t t Any one element (m, n) of (a) should satisfy the formula (25).
Step 3.3 if H t Not empty, then on the route set H where transport tasks can be scheduled t Is selected to be the transport task (m) 0 -n 0 -l 0 ) Otherwise, t=t+1, returning to step 3.2;
the inventory alert time parameter The specific calculation formula is as follows:
wherein ,indicating the duration in which safe production can be maintained, T m,n Representing the voyage time of the route (m-n).
Representing that the first coal transport on the scheduled course (m-n) at the end of period t cannot meet the minimum safe storage lower limit for jetty n +.>Requirements;
indicating that the first coal transport on the route (m-n) can be scheduled at the end of the t period; and for different transport tasks (m, n, l), are provided>Smaller means greater demand for coal transportation, the more urgent the transportation task.
Step 3.4, establishing a knapsack model with a smaller scale based on the principle of ship load adaptation, and optimally solving the single coal transportation amount and the transportation ship of the transportation task in the period;
defining the system state quantity as the information of the residual coal transportation task quantityShip state information table->Power plant dock inventory information table->The knapsack model specifically comprises:
A. objective function
B. Available ship set constraint
T is recorded 0 The time available ships are assembled intoThen->Any element v of the formula (30)
C. Limitation constraint of maximum berthing tonnage of wharf
D. Upper limit constraint of safety stock
E. Ship load adapting principle
Wherein set V t Represents the set of available vessels at the end of the period t, any element of which represents the available vessels v at the end of the period t,representing a set of available vessels at time t 0; variable S v,t Representing a state variable of the vessel, i.e. vessel v is inWhether or not the time t is available, S v,t =1 means that vessel v is available at time t, S v,t =0 indicates that the ship is not available; variable->Representing the remaining coal transportation mission amount of the transportation mission (m-n-l); variable r t,m,n,l Representing the end of period t, a portion of the coal transportation for which the vessel is arranged to perform a transportation mission (m-n-l), where the volume of transportation is r.
The optimal solutions of formulas (27) - (35) were noted asI.e. at t 0 Time-of-day ship v * Transporting coal, wherein the optimal single-pass traffic is +.>
And 3.5, updating each information table, and cycling the above steps until the arrangement of all transportation tasks is completed. The update time of each information table is shown in table 1.
Table 1 characteristics of information tables
The updated information tables are specifically as follows:
(1) Remaining coal transportation mission update
If a transport task (m) 0 -n 0 -l 0 ) Part of the volume of transportThe remaining coal transportation mission is reduced as in equation (36).
(2) Ship status information update
Assuming voyage timeThe same return time, if t 0 Time-of-day ship v * Transporting coal, the occupied period of the vessel includes: three periods of sailing, unloading and returning, i.e. duringCannot be reused within a period of time, satisfying the following constraints:
(3) Power plant dock inventory information update
After the coal arrives at the dock, dock inventory information is updated as in equation (38).
(4) Inventory alert time update
Variable(s)Representing the inventory warning time of a transportation task (m-n-l) for measuring the urgency of the transportation task. Begin to transport coal->To the wharf n of the power plant 0 When the inventory warning time is updated to formula (39).
(5) Available berth number information table update
If the ship is arranged to transport coal to the wharf n of the power plant at the moment t 0 Dock n 0 At the position ofAn occupied berth is added in the period, and the number of available berths is reduced by 1, as shown in formula (40)。
The method of the present invention is further described below with reference to specific application examples.
Examples:
take a group of three power plants as an example. The scale of the calculation is shown in Table 2. Equations (1) - (7) were solved using lingo11.0 software. Equations (8) - (19) are solved using a VC++ design greedy algorithm.
Table 2 example scale
The procurement and volume plans are shown in table 3. The purchase plan aims at reducing purchase costs and transportation costs.
Taking the actual purchase-transportation information of 13 power plants under a certain group as an example, the cost saving condition is calculated. The purchase cost and the transportation cost can be saved by optimizing the method as shown in the table 4.
TABLE 3 purchasing and volume schedule
Table 4 cost comparison
Shipping plans may be obtained by a greedy algorithm, as shown in Table 5. It can be seen that: (1) assigning ship name to each route shipping information; (2) Only the last transportation of each route is not fully loaded transportation, so that the ship carrying capacity utilization rate is high; (3) The ship occupation time comprises the navigation time of each route, the dock unloading time of the power plant and the ship returning time, and the dispatching result can be directly applied to practice.
Table 5 ship shift schedule
The task time chart can be drawn according to the navigation time, the unloading time and the returning time of each route of the wharf N1 of the power plant, as shown in fig. 5. Wherein the horizontal axis represents a scheduling period; the vertical axis represents each task; grey parts represent sailing tasks and returning tasks from ports to the power plant wharf N1; the black parts represent the respective discharge tasks of the power plant dock N1. The change in the inventory of each plant is observed as shown in fig. 6.
It can be seen that: (1) The coal stock of the wharf N1 at each moment meets the safety production requirement; (2) The maximum berth number of the power plant wharf N1 is 1, so that the second transportation task and the third transportation task are staggered in unloading time (the black parts of the second row and the third row in FIG. 5 are not overlapped), and the safe unloading of the ship arriving at the power plant wharf for the third time can be ensured without waiting.
To test the performance of the algorithm, different scale calculations were constructed, as shown in table 6, and the solution time and various performance metrics were recorded, as shown in table 7. In contrast to the standard Mixed Integer Programming (MIP) branch-and-bound algorithm, the lower layer problem can be translated into a standard Mixed Integer Linear Programming (MILP) problem, assuming a shorter transit time.
TABLE 6 different example Scale
Table 7 algorithm performance comparison
The ship load capacity utilization means a ratio of an actual load capacity to a maximum load capacity. The ratio of docks exceeding the safety stock is defined as the ratio of the number of docks which cannot meet the safety production to the total number of docks, reflecting whether the scheduling plan can guarantee the safety production of the power plant.
It can be seen that: (1) MILP can only solve the problem of small and medium scale, and greedy algorithm can solve the problem of large-scale ship scheduling within 10 seconds; (2) Each performance index of the result obtained by the greedy algorithm is excellent; (3) The scheduling strategy obtained by the greedy algorithm can guarantee the safety production requirements of each power plant.
As shown in fig. 7, a second aspect of the present invention is to provide a coal transportation planning system for a large power generation group, comprising:
the model construction module is used for constructing a purchase-transfer coordination optimization model before a coal month by using purchase-transfer history data, constructing a large-scale ship unified scheduling model by using the relation between the number of used ships and the carrying capacity utilization rate of the ships, and constructing a purchase-transport-inventory management two-layer optimization model based on the upper-layer purchase-transfer coordination optimization model and the lower-layer large-scale ship unified scheduling model;
the upper layer solving module is used for solving the upper layer purchasing-dispatching coordination optimization model by adopting a linear programming algorithm and outputting a total purchasing quantity plan and a total transporting quantity plan in a dispatching period;
The lower layer solving module is used for obtaining the state quantity of the power plant wharf system at the current moment, and the state quantity of the power plant wharf system comprises: residual coal transportation task amount information, a ship state information table, a power plant wharf inventory information table, an inventory warning time information table and an available berth number information table; and solving a lower-layer large-scale ship unified scheduling model by adopting a greedy algorithm based on the total purchase quantity plan, the total traffic quantity plan and the state quantity of the power plant wharf system, and outputting a ship optimization scheduling plan.
As shown in fig. 8, a third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the large power generation group-oriented coal transportation planning method when executing the computer program.
The coal transportation planning method for the large power generation group comprises the following steps:
s1, constructing a purchase-transfer coordination optimization model before a coal month by using purchase-transfer historical data, constructing a large-scale ship unified scheduling model by using the relation between the number of used ships and the carrying capacity utilization rate of the ships, and constructing a purchase-transport-inventory management two-layer optimization model based on the upper-layer purchase-transfer coordination optimization model and the lower-layer large-scale ship unified scheduling model;
S2, solving an upper layer purchase-allocation coordination optimization model by adopting a linear programming algorithm, and outputting a total purchase quantity plan and a total freight quantity plan in a scheduling period;
s3, acquiring the state quantity of a power plant wharf system at the current moment, wherein the state quantity of the power plant wharf system comprises: residual coal transportation task amount information, a ship state information table, a power plant wharf inventory information table, an inventory warning time information table and an available berth number information table; and solving a lower-layer large-scale ship unified scheduling model by adopting a greedy algorithm based on the total purchase quantity plan, the total traffic quantity plan and the state quantity of the power plant wharf system, and outputting a ship optimization scheduling plan.
A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program which, when executed by a processor, implements the large-power-generation-group-oriented coal transportation planning method.
The coal transportation planning method for the large power generation group comprises the following steps:
s1, constructing a purchase-transfer coordination optimization model before a coal month by using purchase-transfer historical data, constructing a large-scale ship unified scheduling model by using the relation between the number of used ships and the carrying capacity utilization rate of the ships, and constructing a purchase-transport-inventory management two-layer optimization model based on the upper-layer purchase-transfer coordination optimization model and the lower-layer large-scale ship unified scheduling model;
S2, solving an upper layer purchase-allocation coordination optimization model by adopting a linear programming algorithm, and outputting a total purchase quantity plan and a total freight quantity plan in a scheduling period;
s3, acquiring the state quantity of a power plant wharf system at the current moment, wherein the state quantity of the power plant wharf system comprises: residual coal transportation task amount information, a ship state information table, a power plant wharf inventory information table, an inventory warning time information table and an available berth number information table; and solving a lower-layer large-scale ship unified scheduling model by adopting a greedy algorithm based on the total purchase quantity plan, the total traffic quantity plan and the state quantity of the power plant wharf system, and outputting a ship optimization scheduling plan.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. The coal transportation planning method for the large power generation group is characterized by comprising the following steps of:
constructing a purchase-allocation coordination optimization model before a coal month by using purchase-allocation history data, constructing a large-scale ship unified scheduling model by using the relation between the number of used ships and the carrying capacity utilization rate of the ships, and constructing a purchase-transportation-inventory management two-layer optimization model based on the upper-layer purchase-allocation coordination optimization model and the lower-layer large-scale ship unified scheduling model;
Solving an upper layer purchase-allocation coordination optimization model by adopting a linear programming algorithm, and outputting a total purchase quantity plan and a total freight quantity plan in a scheduling period;
acquiring the state quantity of a power plant wharf system at the current moment, wherein the state quantity of the power plant wharf system comprises the following components: residual coal transportation task amount information, a ship state information table, a power plant wharf inventory information table, an inventory warning time information table and an available berth number information table; and solving a lower-layer large-scale ship unified scheduling model by adopting a greedy algorithm based on the total purchase quantity plan, the total traffic quantity plan and the state quantity of the power plant wharf system, and outputting a ship optimization scheduling plan.
2. The large power generation group-oriented coal transportation planning method of claim 1, wherein the upper layer purchase-dispatch coordination optimization model comprises:
A. minimum construction objective function from sum of coal purchase cost and transportation cost
B. Balance constraint of purchase quantity
C. Demand balance constraint
D. Vendor-related constraints
1) Maximum purchase amount constraint
2) Purchasing proportion constraint
E. Decision quantity constraint
Wherein, the subscript M represents harbor number 1 … M, the subscript N represents harbor number 1 … N, the subscript S represents supplier number 1 … S, and the subscript L represents coal type 1 … L; constant value Representing coal purchasing unit price, constant->Represents the shipping unit price, constant->Represents the coal demand, constant->Represent the upper limit of the purchase quantity, constant alpha s Representing an upper limit of the purchase proportion; variable->Representing the purchase quantity of coal in the dispatching period, and the variable +.>Indicating the amount of coal traffic during the scheduling period.
3. The large power generation group-oriented coal transportation planning method of claim 1, wherein the lower-layer large-scale ship unified shift model comprises:
A. objective function
B. Traffic planning constraints
C. Dock inventory state transition
D. Wharf safety stock constraints
E. Dock physical restraint
1) Dock berth number constraint
2) Constraint of maximum berthing tonnage of ship
F. Ship maximum load restraint
G. Ship scheduling plan variable constraint
x t,m,n,v ＝f(x 1,m',n',v ,x 2,m',n',v ...,x t-1,m',n',v ) (17)
H. Decision quantity constraint
x t,m,n,v ＝0 or 1 (18)
Wherein, the subscript T represents a discrete period of 1 … T, and the subscript V represents a vessel number of 1 … V; constant valueRepresents an initial stock quantity, constant +.>Represent the upper/lower limit of the stock quantity, constant d t,n,l Represents the coal consumption, constant->Represents the unloading time of the ship at the port unloading dock, constant +.>Representing the number of berths of the port unloading dock, constant +.>Represents the maximum load of the ship, constant->Representing the maximum berthing tonnage of the port unloading wharf; variable->Representing the end coal stock quantity of the period t and the variable x t,m,n,v Representing the schedule variable, x t,m,n,v When=1, the end of period t is indicated, ship v is arranged to route (m-n), x t,m,n,v =0, indicating the end of period t, no ship v is arranged to route (m-n), variable ∈ ->Representing the end of period t, arranging vessel v to route (m-n) to transport the coal load of the first coal; function f (x 1,m',n',v ,x 2,m',n',v ...,x t-1,m',n',v ) Indicating that the current ship scheduling plan variable is a function of all of the historical ship scheduling plan variables.
4. The large-scale power generation group-oriented coal transportation planning method according to claim 1, wherein the greedy algorithm is adopted to solve a lower-layer large-scale ship unified scheduling model, and output a ship optimization scheduling plan, and the method specifically comprises the following steps:
s3.1, according to the total purchase amount plan and the total transport amount plan, the total transport planInitial stock quantity of wharf of power plantThe number of berths of a power plant wharf>Initializing each information table specifically comprises the following steps: the system comprises a residual coal transportation task amount information table, a ship state information table, a power plant wharf inventory information table, an inventory warning time information table and an available berth number information table, wherein the specific steps are as follows:
residual coal transportation task amount information table
Ship state information table S v,t ＝1(21)
Power plant dock inventory information table
Inventory alert time information table
Available berth number information table
Wherein the variables areRepresenting the number of available berths at the end of a period T for the power plant dock n for preventing congestion of the power plant dock, T m,n Representing the voyage time of the route (m-n);
s3.2, reading an available ship information table, a residual coal transportation task amount information table, a power plant wharf inventory information table, an inventory warning time information table and an available berth number information table at the moment t; judging whether available ships exist, if so, calculating a route set H capable of arranging transportation tasks t Otherwise let t=t+1, re-execute the step; wherein, set H t An airline set representing that a transport task can be scheduled at the end of a period t, any element representing an airline (m, n) for which a transport task can be scheduled at the end of a period t, and an airline set H for which a transport task can be scheduled at the end of a period t t Any one element (m, n) of (a) satisfies the formula (25);
s3.3 if H t Not empty, then on the route set H where transport tasks can be scheduled t If not, let t=t+1, and return to S3.2;
s3.4, establishing a knapsack model with a smaller scale by taking the ship load adaptation principle, and optimally solving the single coal transportation quantity and the transportation ship of the transportation task in the period;
s3.5, updating each information table based on the single coal transportation amount of the transportation tasks and the transportation ship, and circulating the steps until the arrangement of all the transportation tasks is completed.
5. The large power generation group-oriented coal transportation planning method of claim 4, wherein the inventory warning time parameterThe specific calculation formula is as follows:
wherein ,indicating the duration in which safe production can be maintained, T m,n Representing the voyage time of the route (m-n);
representing that the first coal transport on the scheduled course (m-n) at the end of period t cannot meet the minimum safe storage lower limit for jetty n +.>Requirements;
representing a first coal transport on a scheduled course (m-n) at the end of the period t; and for different transport tasks (m, n, l), are provided>Smaller means for coalThe greater the demand for transportation, the more urgent the transportation task.
6. The large-scale power generation group-oriented coal transportation planning method according to claim 4, wherein the establishing a knapsack model with a smaller scale based on the principle of ship load adaptation to optimize and solve a single coal transportation amount of a transportation task in the period of time and the transportation ship comprises the following steps:
defining the system state quantity as the information of the residual coal transportation task quantityShip state information table->Power plant dock inventory information table->The knapsack model specifically comprises:
A. objective function
B. Available ship set constraint
T is recorded 0 The time available ships are assembled intoThen->Any element v of the formula (30)
C. Limitation constraint of maximum berthing tonnage of wharf
D. Upper limit constraint of safety stock
E. Ship load adapting principle
Wherein set V t Represents the set of available vessels at the end of the period t, any element of which represents the available vessels v at the end of the period t,representing t 0 The ship collection can be used at any time; variable S v,t Representing a vessel state variable, i.e. whether the vessel v is available at time t, S v,t =1 means that vessel v is available at time t, S v,t =0 indicates that the ship is not available; variableQuantity->Representing the remaining coal transportation mission amount of the transportation mission (m-n-l); variable r t,m,n,l Representing the end of period t, arranging the ship to perform part of the coal transportation of the transportation task (m-n-l), wherein the transportation quantity is r;
the optimal solutions of formulas (27) - (35) were noted asI.e. at t 0 Time-of-day ship v * Transporting coal, wherein the optimal single-pass traffic is +.>
7. The large power generation group-oriented coal transportation planning method according to claim 4, wherein the single coal transportation amount based on transportation tasks and transportation ships update information tables, comprising:
1) Remaining coal transportation mission update
If a transport task (m) 0 -n 0 -l 0 ) Part of the volume of transportThe remaining coal transportation task amount is reduced as follows:
2) Ship status information update
Assuming that the navigation time is the same as the return time, if t 0 Time-of-day ship v * Transporting coal, the occupied period of the vessel includes: three periods of sailing, unloading and returning, i.e. duringCannot be reused for a period of time as follows:
3) Power plant dock inventory information update
After the coal arrives at the dock, the dock inventory information is updated, such as:
4) Inventory alert time update
Variable(s)Representing an inventory warning time of a transportation task (m-n-l) for measuring the urgency of the transportation task; begin to transport coal->To the wharf n of the power plant 0 When the inventory warning time is updated, the inventory warning time is updated as follows:
5) Available berth number information table update
If the ship is arranged to transport coal to the wharf n of the power plant at the moment t 0 Dock n 0 At the position ofAn occupied berth is added in the period, and the number of available berths is reduced by 1, specifically:
8. a large power generation group-oriented coal transportation planning system, comprising:
the model construction module is used for constructing a purchase-transfer coordination optimization model before a coal month by using purchase-transfer history data, constructing a large-scale ship unified scheduling model by using the relation between the number of used ships and the carrying capacity utilization rate of the ships, and constructing a purchase-transport-inventory management two-layer optimization model based on the upper-layer purchase-transfer coordination optimization model and the lower-layer large-scale ship unified scheduling model;
The upper layer solving module is used for solving the upper layer purchasing-dispatching coordination optimization model by adopting a linear programming algorithm and outputting a total purchasing quantity plan and a total transporting quantity plan in a dispatching period;
the lower layer solving module is used for obtaining the state quantity of the power plant wharf system at the current moment, and the state quantity of the power plant wharf system comprises: residual coal transportation task amount information, a ship state information table, a power plant wharf inventory information table, an inventory warning time information table and an available berth number information table; and solving a lower-layer large-scale ship unified scheduling model by adopting a greedy algorithm based on the total purchase quantity plan, the total traffic quantity plan and the state quantity of the power plant wharf system, and outputting a ship optimization scheduling plan.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the large power generation group-oriented coal transportation planning method of any one of claims 1-7 when the computer program is executed.
10. A computer readable storage medium storing a computer program which when executed by a processor implements the large power generation group-oriented coal transportation planning method of any one of claims 1-7.
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| CN117522235A (en) * | 2024-01-05 | 2024-02-06 | 哪吒港航智慧科技(上海)有限公司 | Intelligent dispatching method, system, electronic equipment and storage medium for wharf shipment |
| CN118569618A (en) * | 2024-08-05 | 2024-08-30 | 华能信息技术有限公司 | Intelligent fuel dispatching system and method |
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| CN117522235A (en) * | 2024-01-05 | 2024-02-06 | 哪吒港航智慧科技(上海)有限公司 | Intelligent dispatching method, system, electronic equipment and storage medium for wharf shipment |
| CN117522235B (en) * | 2024-01-05 | 2024-03-26 | 哪吒港航智慧科技(上海)有限公司 | Intelligent dispatching method, system, electronic equipment and storage medium for wharf shipment |
| CN118569618A (en) * | 2024-08-05 | 2024-08-30 | 华能信息技术有限公司 | Intelligent fuel dispatching system and method |
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