CN107730029B

CN107730029B - Manufacturing process optimization method and apparatus based on quantum-behaved particle swarm optimization

Info

Publication number: CN107730029B
Application number: CN201710867441.5A
Authority: CN
Inventors: 姜雪松; 王润泽; 逄焕君
Original assignee: Qilu University of Technology
Current assignee: Qilu University of Technology
Priority date: 2017-09-22
Filing date: 2017-09-22
Publication date: 2019-09-06
Anticipated expiration: 2037-09-22
Also published as: CN107730029A

Abstract

The manufacturing process optimization method based on quantum-behaved particle swarm optimization that the invention discloses a kind of, the following steps are included: using air pressure in furnace on oxygen consumption, the consumption of heavy oil, the consuming cost of machine and production line as optimization aim, using time and resource constraint as constraint condition, multiple target manufacturing process Optimized model is constructed；The multiple target manufacturing process Optimized model is solved using quantum-behaved particle swarm optimization.The present invention provides a kind of quantitative mode to produce the optimization of manufacturing process, and experiential adjustment mode is more reasonable than before, and accuracy is high, helps to optimize allocation of resources.

Description

Production and manufacturing process optimization method and device based on quantum behavior particle swarm optimization

Technical Field

The invention belongs to the field of production, manufacturing and optimization scheduling, and particularly relates to a production and manufacturing process optimization method and device based on a quantum-behaved particle swarm algorithm.

Background

The manufacturing industry directly reflects the productivity level of a country, is an important factor for distinguishing developing countries and developed countries, and has an important share in national economy of developed countries in the world. Meanwhile, the manufacturing industry is the core strength and support industry of China, the manufacturing industry of China develops rapidly, but the defects of low productivity, serious waste and the like still exist, and the manufacturing industry is the main channel of employment in cities and towns of China and the centralized embodiment of international competitiveness as an important support for the development of the economy and the society. Under the proposal of '2025 made by China', the manufacturing industry needs transformation urgently, but the most urgent of the current production scheduling is to achieve energy conservation and emission reduction on the premise of benefit, and reduce the emission of pollutants by means of technological innovation. For scheduling problems in many fields, optimization of multiple targets, such as economic benefits, ecological benefits and social benefits of an enterprise, needs to be considered to obtain the most beneficial implementation scheme for the enterprise, and a multi-target planning method needs to be used for solving the problems. At present, the prior production technology in China does not achieve the green production, and a large number of production problems still exist. In the actual production process of a workshop, the main problems are as follows: (1) the production process is complicated, and the flexibility of workshop production is poor; (2) the recorded production data is not optimized, the input quantity of raw material energy and the like is adjusted according to the past experience, the optimization requirement of production cannot be met, and the unreasonable allocation and waste of resources are caused.

Therefore, how to optimize the actual production process and maximize economic and ecological benefits is a technical problem that those skilled in the art are eagerly to solve at present.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention provides a method and a device for optimizing the production and manufacturing process based on a quantum-behaved particle swarm algorithm. Determining a plurality of objective functions and constraint conditions based on actual production manufacturing data, constructing a multi-objective optimization model in the manufacturing process, and then performing model solution by adopting a quantum-behavior-based particle swarm algorithm.

In order to achieve the purpose, the invention adopts the following technical scheme:

a production and manufacturing process optimization method based on quantum behavior particle swarm optimization comprises the following steps:

step 1: constructing a multi-objective manufacturing process optimization model by taking oxygen consumption, heavy oil consumption, machine consumption cost and furnace internal gas pressure on a production line as optimization targets and time and resource constraints as constraint conditions;

step 2: and solving the multi-target manufacturing process optimization model by adopting a quantum behavior particle swarm algorithm.

Further, the multi-objective manufacturing process optimization model is as follows:

miny＝F(x)＝{f₁(x),f₂(x),f₃(x),f₄(x)}

f is₁(x),f₂(x),f₃(x),f₄(x) Respectively representing four objective functions of oxygen consumption, heavy oil consumption, machine consumption cost and furnace internal gas pressure on a production line.

Further, wherein the oxygen consumption: f. of₁(x)＝minX_ijkW_ijk；

Heavy oil consumption: f. of₂(x)＝minX_ijkH_ijk；

The machine consumes power: f. of₃(x)＝X_ijkP_ijk；

Gas pressure in the reaction furnace: f. of₄(x)＝minX_ijkQ_ijk；

X_ijkA j-th process representing the workpiece i is performed on the machine k; w_ijkRepresents the amount of oxygen consumed by the machine k in the j-th process of the workpiece i; h_ijkHeavy oil consumed in the machine k in the jth process of the workpiece i; p_ijkEnergy consumption of the machine k in the j-th process of the workpiece i; q_ijkThe pressure at which the j-th process of the workpiece i is performed on the machine k is shown.

Further, the method comprises, among others,

the time constraint is: the machining starting time between adjacent working procedures of the same workpiece is in sequence;

the resource constraints are: the current task must be completed before starting the next task on the same machine, and any machine cannot process two workpieces of the same or different processes at the same time.

Further, the time constraint is expressed as:

in the formula, the working procedure processing time t_ijkRepresents the time S required for the jth process of the workpiece i to process on the kth machine_ijkThe time when the jth process of the workpiece i starts to process on the kth machine table is shown. The whole equation shows that the j-1 st process of the workpiece i must be completed before the j process.

Further, the resource constraint is expressed as:

x_ijk＝x_mnk＝1 and R_ijmnq＝1

X_ijk1 represents a step V_ijPerformed on machine k, R_ijmnqShowing the sequence of the process of the jth step of the workpiece i and the nth step of the workpiece m on the machine q, R_ijmnqStep j precedes step n by 1.

Further, the quantum behavior particle swarm algorithm specifically comprises the following steps:

(1) initializing algorithm parameters: the method comprises the following steps of (1) obtaining a particle population X, a dimension R, a position of a particle i, a maximum iteration number MAXISTER and an optimal solution set L;

four objective function regions of the region reachable by particle i are set as: oxygen consumption O (i), heavy oil consumption G (i), machine power consumption M (i), kiln pressure S (i), and four target areas are defined as four matrixes so as to update the particles in the next iteration;

(2) calculating an adaptive value of each particle according to the objective function;

(3) for each particle, a search path is selected: selecting a next update point to be reached by a particle i (i ═ 1, 2, …, R) in matrices o (i), g (i), m (i), and s (i) according to a particle evolution equation; the particle evolution equation is:

wherein α is a compression-expansion factor, t is the current iteration number, u is a random number uniformly distributed between 0 and 1, L_ijIs the characteristic length of the delta well; x_ij(t) and X_ij(t +1) respectively representing positions before and after the evolution of the particles, and M representing a population of potential problem solutions in the particle swarm;

(4) for each particle, the position X of the particle is calculated_i(t) solving the individual best position S of the particle from the individual best position solving formula_i(t) the best position of the previous particle unitPut S_i(t-1) and if the adaptive value is better, S is compared_i(t) as a new locally optimal solution; the individual best position solving formula is as follows:

wherein f (-) represents the current position of the particle;

(5) for each particle, comparing the adaptive value with the best position Sbest passed by the particle, and if the adaptive value is better, taking the current position as the current global optimal solution;

(6) repeating (2) - (5);

(7) termination conditions were as follows: and the iteration times reach the set maximum iteration times or the calculation of the objective function area is completed.

According to a third object of the present invention, the present invention further provides a device for optimizing a manufacturing process based on a quantum-behaved particle swarm algorithm, comprising a memory, a processor and a computer program stored in the memory and operable on the processor, wherein the processor implements the method for optimizing a manufacturing process based on a quantum-behaved particle swarm algorithm when executing the program.

According to a third object of the present invention, the present invention further provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, performs the method for optimizing a manufacturing process based on a quantum-behaved particle swarm algorithm.

The invention has the advantages of

1. The method takes the oxygen consumption, the heavy oil consumption, the machine consumption cost and the gas pressure in the furnace on the production line as optimization targets, takes time and resource constraints as constraint conditions, establishes an optimization model, provides a quantitative mode for the optimization of the production and manufacturing process, is more reasonable than the previous adjustment mode according to experience, and has high accuracy;

2. the QPSO is adopted for model solution, and experiments prove that the efficiency of the method is obviously superior to that of a genetic algorithm and a particle swarm algorithm, so that the defect that the PSO algorithm is easy to fall into a local optimal solution is overcome.

The invention only aims at the energy control design in the production, does not relate to raw materials (such as silicon dioxide, aluminum oxide, calcium oxide and the like) in the production of the glass fiber, and has simple implementation and accordance with the practice.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.

FIG. 1 is a flow chart of a production and manufacturing process optimization method based on quantum-behaved particle swarm optimization according to the present invention;

FIG. 2 is a flow chart of the quantum-behaved particle swarm algorithm of the present invention.

Detailed Description

It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.

The embodiments and features of the embodiments in the present application may be combined with each other without conflict.

Example one

The embodiment discloses a production and manufacturing process optimization method based on a quantum behavior particle swarm algorithm, which comprises the following steps of:

the business process is a group of interrelated, dependent and progressive activity processes which are completed by a plurality of main bodies together in order to achieve the goal, the business process optimization is to continuously modify and perfect the existing process, and the business process optimization mainly comprises four aspects, namely time, cost, quality and flexibility according to the key success factor analysis of the business process optimization. Time includes execution time and activity latency, cost mainly includes information cost, labor cost and resource cost, quality mainly includes qualification rate, service standard-reaching rate, reliability, service integrity, etc., flexibility includes time flexibility, quantity flexibility and market flexibility.

An objective function:

with the development of actual production, optimization of a single target hardly meets the actual production needs, and usually multiple targets need to be considered simultaneously, that is, the performance of any one target is improved as much as possible without damaging the performance of other targets, in this document, the objective of optimized scheduling of manufacturing resources facing a flexible job shop is to optimize the manufacturing process of the whole task, and four objective functions are set on the basis of analyzing key success factors of a business process:

1. oxygen consumption: f. of₁(x)；

2. Consumption of heavy oil: f. of₂(x)；

3. Consumption cost of machine: f. of₃(x)；

4. The air pressure in the furnace on the production line: f. of₄(x)。

Specifically, four objective functions are set up to design the function during the manufacturing process.

Oxygen consumption: f. of₁(x)＝minX_ijkW_ijk

Heavy oil consumption: f. of₂(x)＝minX_ijkH_ijk

The machine consumes power: f. of₃(x)＝X_ijkP_ijk

Gas pressure in the reaction furnace: f. of₄(x)＝minX_ijkQ_ijk

X_ijkA j-th process representing the workpiece i is performed on the machine k; w_ijkRepresents the amount of oxygen consumed by the machine k in the j-th process of the workpiece i; h_ijkHeavy oil consumed in the machine k in the jth process of the workpiece i; p_ijkEnergy consumption of the machine k in the j-th process of the workpiece i; q_ijkThe air pressure of the j process of the workpiece i when the workpiece i is produced on the machine k is shown;

the multi-objective optimization problem is also known as a multi-objective optimization problem. Without loss of generality, the multi-objective optimization problem with d decision variables and R objective variables can be expressed as:

miny＝F(x)＝{f₁(x),f₂(x)，x₃(x),...f_n(x)}

in the objective function formula, X ═ (X1, X2 … X) is an R-dimensional decision vector, and X is an R-dimensional decision space. Objective function f_(x)Define the slave blockFour mapping functions of policy space to target space.

Constraint conditions are as follows:

according to the constraint division, common constraint conditions of workshop scheduling include production resources (energy, raw materials, equipment and the like), cache capacity, product delivery date, product process flow, batch size, cost limitation and the like. The constraints referred to herein are primarily time constraints and resource constraints.

And (3) time constraint: the processing starting time between the adjacent working procedures of the same workpiece has the process requirement

Resource constraint: the current task must be completed before starting the next task on the same machine, and any machine cannot process two workpieces of the same or different processes at the same time.

Wherein x_ijk＝x_mnk＝1 and R_ijmnq＝1。

X_ijk1 represents a step V_ijPerformed on machine k, R_ijmnqShowing the machining sequence R of the jth process of the workpiece i and the nth process of the workpiece m on the machine q_ijmnqStep j precedes step n by 1.

Quantum-behaved particle swarm optimization:

consider the following disadvantages of the particle swarm algorithm itself. (1) The found optimal solution may be a locally optimal solution rather than a globally optimal solution. (2) The convergence rate of the algorithm in the initial search period is high, and the convergence rate in the later search period is low. (3) Randomness of parameter selection. SUN et al proposed a new PSO algorithm model based on DELTA potential well in 2004 from the perspective of quantum mechanics, and proposed a particle swarm optimization algorithm based on quantum behavior. In quantum space, particles can be searched in the whole feasible solution space, so the global search performance of the QPSO algorithm is far better than that of the standard PSO algorithm. The QPSO algorithm describes the state of the particles through a wave function, obtains a probability density function of the particles appearing at a certain point in space by solving the Schle equation, and obtains a position equation of the particles through MonetCarfo random simulation.

The QPSO algorithm consists of R groups of particles representing potential problem solutions in a search space of a D-dimensional target, and the group is represented as X ═ (X)₁,x₂,...x_i)^T i＝1,2,...R，(3.8)

Position of ith particle at time t:

X_i(t)＝{X_i,1(t),X_i,2(t),...,X_i,D(t)},i＝1,2,...3,R (3.9)

in the quantum-behaved particle swarm optimization, the particles have no velocity vector, and the individual positions of the particles preferably represent P_i(t)＝[P_i,1(t),P_i,2(t),...,P_i,D(t)] (3.10)

For the optimization problem, the smaller the objective function value, the better the corresponding adaptation value. The individual best position Sbest of particle i is determined by the following equation:

the population positions are represented as:

L(t)＝[L₁(t),L₂(t),...,L_D(t)] (3.11)

when g is the best position, L (t) is S_g(t),g∈{1,2,...,R}。

In the actual algorithm operation, the global best position is compared once in each operation, if the operation S after the operation i is finished_i(t) has a value of better than S_i(t-1) then adding L_iAnd (t) updating.

Order to

The evolutionary equation for the particle is:

in QPSO algorithm, the position vector of the state description of the particles and only one control vector α in the algorithm are the contraction expansion coefficients in the algorithm, are the only control parameter optimization, and are generally worth the following formula

α ═ 0.5+ (1-0.5) × (maximum-t)/maximum, maximum being the number of iterations, and t being the current number of iterations.

The solving steps of the quantum behavior particle swarm are as follows:

(1) initializing algorithm parameters: the method comprises the following steps of particle population X, dimension R, the position of a particle i, maximum iteration number MAXISTER and an optimal solution set L (including a local optimal solution and a global optimal solution). Four objective function regions of the region reachable by particle i are set as: oxygen consumption O (i), heavy oil consumption G (i), machine power consumption M (i), kiln pressure S (i), and four target areas are defined as four matrixes so as to update the particles in the next iteration;

(4) for each particle, the position X of the particle is calculated_i(t) solving the individual best position S of the particle from the individual best position solving formula_i(t) the position S of the previous individual particle_i(t-1) and if the adaptive value is better, S is compared_i(t) as a new locally optimal solution; the individual best position solving formula is as follows:

wherein f (-) represents the current position of the particle;

(5) for each particle, comparing the adaptive value of the particle with the best position Sbest passed by the group particle, and if the adaptive value is better, taking the current position as the current global optimal solution;

(6) repeating (2) - (5);

(7) termination conditions were as follows: the iteration number reaches the set maximum iteration number or the set total number of the four objective functions.

Example two

An object of the present embodiment is to provide a computing device.

A production and manufacturing process optimization device based on quantum behavioral particle swarm optimization comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the following steps, and the method comprises the following steps:

EXAMPLE III

An object of the present embodiment is to provide a computer-readable storage medium.

A computer-readable storage medium, on which a computer program for optimization of a production manufacturing process is stored, which program, when executed by a processor, performs the steps of:

The steps involved in the second and third embodiments correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.

Results of the experiment

According to the invention, aiming at actual production data (actual production data of a certain glass fiber company), the particle swarm algorithm and the quantum behavior particle swarm algorithm in the swarm intelligence algorithm are utilized to optimize the data, and the processed data is compared with the actual production data, so that the advantages of the optimized data can be obviously seen.

And selecting actual production data of a certain glass fiber company for experimental analysis, modifying the experimental data, and performing a simulation experiment by using MATLAB to obtain the actual condition of the result of the invention. The main content of the experimental data is air pressure, energy, engine consumption and exhaust emission, and the sum of water consumption and coal consumption is selected from the energy perspective. 1000 running records of four target functions are selected in the test and used as actual production data, and experimental data are recorded every 10 seconds. Firstly, four target functions are read in a matlab experiment, a suffix name is one, and a matrix is generated for matlab identification.

function Data＝De()

% read in data

filename＝'Energy.dat'；

filename＝'Machine.dat'；

filename＝'kw.dat'；

filename＝'Waste.dat'；

nRow＝1000；

nColumn＝1；

fid＝fopen(filename,'r')；

temp＝fscanf(fid,'％f')；

fclose(fid)；

Data＝reshape(temp,[nRow nColumn])'；

The actual production data is calculated in a PSO algorithm and a QPSO algorithm, and meanwhile, the maximum iteration number MAXISTER is set to be 1000, the particle number popsize is set to be 50, the dimension is set to be 50, the running number runmax is set to be 30, and the variable range of the position X is selected to be 0-10000. In the PSO algorithm, let c1 ═ c2 ═ 2, r_1,i,j(t)＝0.9，r_2,i,j(t) 0.4, Vmax 6, speed variation not considered here in QPSO algorithm since there is no speed variable; the txt document is output in the experiment, the document comprises an average value, a best value for each iteration, a variance and a Global optimal solution, and the comparison between actual production data, pso processed data and QPSO processed data is as follows: in actual production data, the actual consumption for actually producing ten tons of certain types of glass fibers is as follows: s (x) {2216.698204,3757.072245,29105.1684,1.0409}, the result after processing is: f (x) {2.3157365e +03,3.3084281e +03,2.7113943e +04,1.1022318 }. It can be concluded that the consumption of oxygen increases. The full-oxygen combustion and the oxygen-enriched combustion are adopted, the air pressure in the furnace is increased, the consumption of electric energy is correspondingly increased by utilizing the characteristics of high thermal efficiency and little environmental pollution of the auxiliary electric heating of the glass fiber tank furnace, the combustion use of heavy oil can be obviously reduced, and the emission of harmful gas is reduced.

Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code that is executable by computing means, such that they are stored in memory means for execution by the computing means, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps of them are fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.

Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims

1. A production and manufacturing process optimization method based on quantum behavior particle swarm optimization is characterized by comprising the following steps:

step 2: solving the multi-target manufacturing process optimization model by adopting a quantum behavior particle swarm algorithm;

the multi-objective manufacturing process optimization model is as follows:

miny＝F(x)＝{f₁(x),f₂(x),f₃(x),f₄(x)}

f is₁(x),f₂(x),f₃(x),f₄(x) Respectively representing four objective functions of oxygen consumption, heavy oil consumption, machine consumption cost and furnace internal gas pressure on a production line;

wherein, the oxygen consumption is as follows: f. of₁(x)＝minX_ijkW_ijk；

Heavy oil consumption: f. of₂(x)＝minX_ijkH_ijk；

The machine consumes power: f. of₃(x)＝X_ijkP_ijk；

Gas pressure in the reaction furnace: f. of₄(x)＝minX_ijkQ_ijk；

wherein,

the resource constraints are: before starting the next task on the same machine, the current task must be completed, and any machine cannot simultaneously process two workpieces with the same or different procedures;

the time constraint is expressed as:

x_ijk＝x_i(j-1)k＝1

in the formula, the working procedure processing time t_ijkRepresents the time S required for the jth process of the workpiece i to process on the kth machine_ijkThe time of the jth process of the workpiece i starting to process on the kth machine table is represented, and the whole formula represents that the jth-1 process of the workpiece i must be finished before the jth process;

the resource constraint is represented as:

x_ijk＝x_mnk＝1and R_ijmnq＝1

X_ijk1 represents a step V_ijPerformed on machine k, R_ijmnqShowing the sequence of the process of the jth step of the workpiece i and the nth step of the workpiece m on the machine q, R_ijmnq1 indicates that step j precedes step n;

the quantum behavior particle swarm algorithm comprises the following specific steps:

(4) for each particleCalculating the position X of the particle_i(t) solving the individual best position S of the particle from the individual best position solving formula_i(t) the position S of the previous individual particle_i(t-1) and if the adaptive value is better, S is compared_i(t) as a new locally optimal solution; the individual best position solving formula is as follows:

wherein f (-) represents the current position of the particle;

(6) repeating (2) - (5);

2. A quantum-behavioral-particle-swarm-algorithm-based production manufacturing process optimization apparatus for executing the quantum-behavioral-particle-swarm-algorithm-based production manufacturing process optimization method according to claim 1, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the quantum-behavioral-particle-swarm-algorithm-based production manufacturing process optimization method according to claim 1 when executing the program.

3. A computer-readable storage medium for storing a quantum-behaved particle swarm optimization method according to claim 1, having a computer program stored thereon, wherein the program, when executed by a processor, performs the quantum-behaved particle swarm optimization method according to claim 1.