CN115375193A

CN115375193A - Method, device and equipment for optimizing double-target production scheduling and readable storage medium

Info

Publication number: CN115375193A
Application number: CN202211299165.4A
Authority: CN
Inventors: 韩方正; 刘斌; 李�杰; 郭宇翔; 傅慧初
Original assignee: Ax Industries Ltd
Current assignee: Ax Industries Ltd
Priority date: 2022-10-24
Filing date: 2022-10-24
Publication date: 2022-11-22
Anticipated expiration: 2042-10-24
Also published as: CN115375193B

Abstract

The application discloses a method, a device, equipment and a readable storage medium for optimizing dual-target production scheduling, which relate to the technical field of production scheduling, wherein the method comprises the following steps: acquiring production scheduling information, and generating a production coding data set in a preset coding function according to the production scheduling information; establishing a dual-target production scheduling optimization model, inputting the production coding data set into the dual-target production scheduling optimization model, and performing neighborhood search on the production coding data set to obtain a corresponding target coding data set; aggregating the target encoding data set and the production encoding data set to obtain an aggregation result; and based on a preset non-dominated sorting mechanism, detecting the quality of the aggregation result to obtain a dual-objective production scheduling optimization result. The technical problem that the solving efficiency of the existing dual-target production scheduling problem is low is solved.

Description

Method, device and equipment for optimizing double-target production scheduling and readable storage medium

Technical Field

The application relates to the technical field of production scheduling, in particular to a method, a device and equipment for optimizing dual-target production scheduling and a readable storage medium.

Background

Job-shop Scheduling (JSP) is a classic type of Problem in the field of manufacturing. The problem is to process a group of workpieces to be processed on a limited number of production facilities and to output the products. Each workpiece has a plurality of processing steps, each step is called a procedure, and each procedure is completed on a designated production device. The goal of JSP is generally to minimize makespan (total processing time), which means that the total time it takes a factory to produce a batch of workpieces is minimized, which is beneficial to improving production efficiency. JSP has important research value because of being directly derived from the factory production process, is widely researched by a plurality of expert and scholars and is expanded to the more general Flexible Job Scheduling Problem (FJSP). In the FJSP, each process is not corresponding to a certain production equipment, but is a set of optional production equipment, so that the problem of production equipment allocation is also considered besides the need of specifying the processing sequence of each process, namely, on which production equipment each process is processed. Compared with JSP, FJSP is more suitable for actual production conditions but more complex, and the research on FJSP is focused on in the application.

The FJSP is a classical NP-hard (Non-deterministic Polynomial hard) and the precise algorithm can hardly solve the problem when the scale is large, and various heuristic algorithms are usually used to solve the FJSP problem at present, however, the heuristic algorithm usually only considers a single target, namely makespan minimization, when aiming at the multi-target FJSP problem. In fact, a factory pays attention to production efficiency and also considers production cost in production, when facing multiple optimization targets, most of existing researches convert problems into single-target problems to be solved in a weighting mode, when a decision maker cannot clearly give weights, the algorithm may not obtain satisfactory results, the heuristic algorithm is complex in coding, the neighborhood searching process is complex, and therefore the solving efficiency of the double-target FJSP problem is low.

Disclosure of Invention

The application mainly aims to provide a method, a device and equipment for optimizing dual-target production scheduling and a readable storage medium, and aims to solve the technical problem that the solving efficiency of the existing dual-target production scheduling problem is low.

In order to achieve the above object, the present application provides a dual target production scheduling optimization method, including:

acquiring production scheduling information, and generating a production coding data set in a preset coding function according to the production scheduling information;

establishing a dual-target production scheduling optimization model, inputting the production coding data set into the dual-target production scheduling optimization model, and performing neighborhood search on the production coding data set to obtain a corresponding target coding data set;

aggregating the target encoding data set and the production encoding data set to obtain an aggregation result;

and based on a preset non-dominated sorting mechanism, detecting the quality of the aggregation result to obtain a dual-objective production scheduling optimization result.

Optionally, the production scheduling information includes preset code generating conditions, the preset code generating conditions include production process code generating conditions and production equipment code generating conditions, the production coding data set includes production process coding data sets and production equipment coding data sets, the preset coding functions include production process coding functions and production equipment coding functions, and the step of generating the production coding data set in the preset coding functions according to the production scheduling information includes:

generating the production procedure encoding data set in the production procedure encoding function according to the production procedure encoding generation condition;

and generating the production equipment encoding data set in the production equipment encoding function according to the production equipment encoding generation condition.

Optionally, the production procedure code generating condition includes a random ordering condition and a time duration ordering condition, the production procedure code data set includes a procedure random ordering code data set and a procedure time duration ordering code data set, the production procedure code data set at least corresponds to one production procedure, and the step of generating the production procedure code data set in the production procedure code function according to the production procedure code generating condition includes:

according to the random sorting conditions, carrying out random sorting on the production procedures to obtain a first permutation and combination;

inputting the first arrangement combination into the production procedure coding function to generate the procedure random ordering coding data set;

processing time length information corresponding to each production procedure is obtained, and each production procedure is sequentially sequenced according to the time length sequencing condition and each processing time length information to obtain a second permutation and combination;

and inputting the second permutation and combination into the production procedure coding function to generate the procedure duration sequencing coding data set.

Optionally, the production device encoding generating condition includes a cost distribution condition and a uniform distribution condition, the production device encoding dataset includes a cost distribution encoding dataset and a uniform distribution encoding dataset, the production device encoding dataset at least corresponds to one production device, and the step of generating the production device encoding dataset in the production device encoding function according to the production device encoding generating condition includes:

acquiring configuration information corresponding to each production device, wherein the configuration information comprises processing cost information and workload information;

according to the processing cost information and the cost distribution conditions, performing cost distribution on each production device to obtain a first distribution combination;

inputting the first allocation combination into the production equipment encoding function to generate the cost allocation encoding data set;

according to the workload information and the uniform distribution conditions, uniformly distributing the production equipment to obtain a second distribution combination;

and inputting the second distribution combination into the production equipment coding function to generate the uniform distribution coding data set.

Optionally, the production data set at least includes one production coding object, the neighborhood search includes coding destruction and coding repair, and the step of obtaining the corresponding target coding data set by performing the neighborhood search on the production coding data set includes:

performing the code destruction on the production coding data set to randomly delete each production coding object to obtain a first temporary data set;

and performing coding repair on the first temporary data set to perform reassignment on each deleted production coding object to obtain the target coding data set.

Optionally, the aggregation result at least includes one encoded data set, and the step of performing quality detection on the aggregation result based on a preset non-dominated sorting mechanism to obtain a dual-target production scheduling optimization result includes:

based on the preset non-dominated sorting mechanism, carrying out grade division on each coding data set to obtain corresponding grade division information;

and detecting the goodness of each coding data set according to each grade division information to obtain an optimal coding data set, and taking the optimal coding data set as the dual-target production scheduling optimization result.

Optionally, after the step of performing quality screening on each encoded data set according to each of the hierarchical classification information to obtain an optimal encoded data set, and using the optimal encoded data set as the dual target production scheduling optimization result, the method further includes:

detecting whether the operation times of the dual-target production scheduling optimization model reach a preset maximum iteration time or not;

if yes, outputting the optimal encoding data set;

if not, updating the encoding data set to the optimal encoding data set, and returning to the step: and inputting the production coding data set into the dual-target production scheduling optimization model, and performing neighborhood search on the production coding data set to obtain a corresponding target coding data set.

In addition, in order to realize the above-mentioned purpose, this application still provides a two target production scheduling optimizing apparatus, two target production scheduling optimizing apparatus include:

the production coding data set generating module is used for acquiring production scheduling information and generating a production coding data set in a preset coding function according to the production scheduling information;

the model establishing and neighborhood searching module is used for establishing a dual-target production scheduling optimization model, inputting the production coding data set into the dual-target production scheduling optimization model, and performing neighborhood searching on the production coding data set to obtain a corresponding target coding data set;

the aggregation module is used for aggregating the target encoding data set and the production encoding data set to obtain an aggregation result;

and the non-domination sorting mechanism module is used for detecting the quality of the aggregation result based on a preset non-domination sorting mechanism to obtain a dual-target production scheduling optimization result.

Optionally, the generate production coded data set module is further configured to:

according to the random sorting condition, carrying out random sorting on each production procedure to obtain a first permutation and combination;

Optionally, the generating the production coding dataset module is further configured to:

according to the processing cost information and the cost distribution conditions, carrying out cost distribution on each production device to obtain a first distribution combination;

inputting the first distribution combination into the production equipment coding function to generate the cost distribution coding data set;

Optionally, the model building and neighborhood searching module is further configured to:

Optionally, the non-dominated sorting mechanism module is further configured to:

and detecting the goodness and the badness of each coding data set according to each grade division information to obtain an optimal coding data set, and taking the optimal coding data set as the dual-target production scheduling optimization result.

if yes, outputting the optimal encoding data set;

if not, updating the production coded data set to the optimal coded data set, and returning to the step: and inputting the production coding data set into the dual-target production scheduling optimization model, and performing neighborhood search on the production coding data set to obtain a corresponding target coding data set.

The application also provides a dual-target production scheduling optimization device, the dual-target production scheduling optimization device includes: the system comprises a memory, a processor and a dual-target production scheduling optimization program stored on the memory and capable of running on the processor, wherein when the dual-target production scheduling optimization program is executed by the processor, the steps of the dual-target production scheduling optimization method are realized.

The present application further provides a readable storage medium having stored thereon a dual target production scheduling optimization program, which when executed by a processor, implements the steps of the dual target production scheduling optimization method as described above.

The present application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of the dual goal production scheduling optimization method as described above.

Compared with the conventional mode that various heuristic algorithms are commonly used for solving FJSP (fuzzy spanning Tree) problems, the method firstly acquires production scheduling information, and generates a production coding data set in a preset coding function according to the production scheduling information; establishing a dual-target production scheduling optimization model, inputting the production coding data set into the dual-target production scheduling optimization model, and performing neighborhood search on the production coding data set to obtain a corresponding target coding data set; aggregating the target encoding data set and the production encoding data set to obtain an aggregation result; and based on a preset non-dominated sorting mechanism, detecting the quality of the aggregation result to obtain a dual-objective production scheduling optimization result. The problem of production scheduling with conflicting optimization targets or undefined target weights is solved, the technical defect that the solving efficiency is low due to the complexity of coding of a heuristic algorithm and the complexity of a neighborhood searching process is overcome, the whole algorithm process is simple and convenient to operate without parameter adjustment by using a simpler and more reasonable coding mode and a large neighborhood searching operator, the calculation complexity of the algorithm is effectively reduced, and the solving efficiency of the double-target FJSP problem is improved.

Drawings

FIG. 1 is a schematic flow chart diagram illustrating a first embodiment of a dual-objective scheduling optimization method according to the present application;

FIG. 2 is a diagram of coding algorithms involved in the dual-objective scheduling optimization method of the present application;

FIG. 3 is a flowchart of a large neighborhood search algorithm involved in the dual-objective production scheduling optimization method of the present application;

FIG. 4 is a diagram illustrating examples of coding destruction and coding repair of a coding data set of a production device involved in the dual-objective scheduling optimization method according to the present application;

FIG. 5 is a diagram illustrating examples of code destruction and code restoration of a production process coding data set involved in the dual-objective scheduling optimization method according to the present application;

FIG. 6 is a schematic diagram illustrating the non-dominated ranking involved in the dual goal production scheduling optimization method of the present application;

FIG. 7 is a diagram of FJSP example optimization results involved in the dual-target production scheduling optimization method of the present application;

FIG. 8 is a schematic diagram of an apparatus involved in the dual-objective scheduling optimization method of the present application;

fig. 9 is a schematic device structure diagram of a hardware operating environment involved in the dual-target production scheduling optimization method according to the present application.

The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.

Detailed Description

In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments of the present application are described in detail below with reference to the accompanying drawings. It should be apparent that the described embodiments are only a few embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Various heuristic algorithms are currently commonly used to solve the FJSP problem, however, heuristic algorithms tend to consider only a single objective, namely makespan minimization. In fact, in production of a factory, not only the production efficiency is concerned, but also the production cost is considered, when facing a plurality of optimization targets, most of the existing researches convert the problems into single-target problem solving in a weighting mode, and when a decision maker cannot clearly give weights, the algorithm may not obtain satisfactory results. Most of the existing optimization algorithms are complex in coding, and the neighborhood searching process is complicated, so that the solving efficiency of the double-target FJSP problem is low.

In a first embodiment of the dual-target production scheduling optimization method of the present application, referring to fig. 1, the dual-target production scheduling optimization method includes:

step S10, acquiring production scheduling information, and generating a production coding data set in a preset coding function according to the production scheduling information;

in this embodiment, it should be noted that, in this embodiment, before encoding, production scheduling information needs to be obtained to clarify optimization targets of production scheduling, that is, production efficiency and production cost, the former is to minimize makespan and improve production efficiency by minimizing total processing time, the latter is to minimize total processing cost, and production efficiency and production cost are usually determined by production equipment and production processes involved in the production process, so the encoding targets of this embodiment may be set as production equipment and production processes, that is, a production process encoding function and a production equipment encoding function are established, where the production scheduling information refers to information such as production workpiece information, production process information, production equipment information, production flow information, and preset encoding generation conditions involved in the whole production scheduling process, the production process encoding function is used to explain a processing order of the production process, and the production equipment encoding function is used to explain a corresponding relationship between the production process and the production equipment.

As an example, step S10 includes: and generating a production coding function corresponding to the production process in the production process coding function according to preset coding production conditions respectively corresponding to the production process and the production equipment, and generating a production equipment coding function corresponding to the production equipment in the production equipment coding function.

In one possible implementation, the production process coding function and the production equipment coding function are respectively as follows:

wherein the content of the first and second substances,

is shown as

A first of the workpieces

The number of the production equipment corresponding to each step,

to represent

First, the

Working procedures, i.e. processesThe number is numbered,

representing the total number of steps, the total number of steps refers to the total number of processing steps,

each step is referred to as a single process step,

denotes the first

The total number of steps for each workpiece,

the total number of the workpieces is the total number of the workpieces,

and

instead of a one-to-one correspondence,

the values are taken according to the sequence from the first step of the first workpiece to the last step of the last workpiece, the process sequencing data set can be separated from the production equipment distribution data set through the coding design scheme, the corresponding relation between the process sequencing data set and the production equipment distribution data set does not need to be considered in the subsequent neighborhood searching stage, and the complexity of the algorithm is effectively reduced.

For example, as shown in the coding algorithm diagram shown in FIG. 2, assume that

Is shown as

The production equipment comprises 3 workpieces to be processed and 4 available production equipment, the numerical values in the table respectively represent the processing time and the cost of each procedure on each production equipment,such as the first workpiece first

The steps are as follows

The above processing time is 5, the processing cost is 12, this example is completely flexible, that is, each step of each workpiece can complete processing on any production equipment, if only part of the production equipment can be selected, it is called a partially flexible job shop scheduling problem, the solution objective of this problem is to find an optimal set of encoded data sets, so that the total processing time and the total processing cost are minimized, that is, the encoded data sets obtained in fig. 2, wherein the first row is the production equipment encoded data set, and the processing production equipment of each process is specified, for example, the first step of the first workpiece is processed on the production equipment No. 3, and the second step of the second workpiece is processed on the production equipment No. 4; the second action is a production process coding data set, and each production equipment sequentially processes each workpiece according to the sequence in the process data set, for example, no. 3 production equipment sequentially processes No. 2, no. 3 and No. 1 workpieces. The total processing time of all the procedures in the production scheme is 15, and the total processing cost is 104.

Step S20, establishing a dual-target production scheduling optimization model, inputting the production coding data set into the dual-target production scheduling optimization model, and performing large neighborhood search on the production coding data set to obtain a corresponding target coding data set;

in this embodiment, it should be noted that the dual target production scheduling optimization model may be a Large Neighborhood Search algorithm model, where a Large Neighborhood Search algorithm (LNS) is a population algorithm, and the main idea is to obtain a new solution set through "destroy" and "repair" operations on an initial solution, and then gradually improve the solution to find a better solution in each iteration, a flow chart of the Large Neighborhood Search algorithm is shown in fig. 3, where the initial solution is equivalent to the production encoded data set in this embodiment, a target solution is an object encoded data set in this embodiment, and combining the initial solution and the target solution is equivalent to aggregating the production encoded data set and the target encoded data set, and an optimal solution is equivalent to an optimal encoded data set, that is, a non-dominated solution set; the large neighborhood search can comprise a production equipment neighborhood search operator and a production process neighborhood search operator, wherein the production equipment neighborhood search operator is used for carrying out coding destruction and coding restoration on the production equipment coded data set, and the production process neighborhood search operator is used for carrying out coding destruction and coding restoration on the production process coded data set.

As an example, step S20 includes: establishing a dual-target production scheduling optimization model, and taking the production encoding data set as an initial solution of the dual-target production scheduling optimization model, wherein the production encoding data set comprises a production process encoding data set and a production equipment encoding data set, namely, the production process encoding data set and the production equipment encoding data set are jointly taken as an initial solution, so that the purpose that two contents of a process and production equipment are contained in one initial solution is achieved, and the contents specifically comprise a process processing sequence and a corresponding relation between the process and the production equipment; and carrying out coding destruction and coding restoration on the production equipment coding data set through the production equipment neighborhood search operator to obtain a first data set corresponding to the production equipment coding data set, carrying out coding destruction and coding restoration on the production procedure coding data set through the production procedure neighborhood search operator to obtain a second data set corresponding to the production procedure coding data set, and taking the first data set and the second data set as the target coding data set together.

Step S30, aggregating the target encoding data set and the production encoding data set to obtain an aggregation result;

and S40, detecting the quality of the aggregation result based on a preset non-dominated sorting mechanism to obtain a dual-target production scheduling optimization result.

In this embodiment, it should be noted that the aggregation result refers to a mixed solution set obtained by merging the target encoding data set and the production encoding data set; the preset Non-dominated Sorting mechanism is from an NSGA-II (Non-dominated Sorting Genetic Algorithm, fast Non-dominated Sorting Genetic Algorithm) Algorithm and is used for solving a dual-target optimization problem; the dual-target production scheduling optimization result is a Pareto optimal solution obtained by screening the mixed solution set in a non-dominated sorting mode, wherein the Pareto optimal solution (Pareto optility) is an ideal state of resource allocation, the probability that a binocular standard obtains the optimal solution in practical application is small, most of two targets cannot be obtained under the same condition, when the solution is between the two targets, the solution is called the Pareto optimal solution, the Pareto optimal solution is a solution with acceptable quality, and generally, the Pareto optimal solution is multiple.

As an example, steps S30 to S40 include: merging the target encoding data set and the production encoding data set to obtain a mixed solution set, so that the detection range of subsequent quality detection is expanded; and based on a preset non-dominated sorting mechanism, performing quality detection on the mixed solution set to obtain a dual-objective production scheduling optimization result, wherein the purpose of the quality detection is to divide the mixed solution set into an excellent data set and an inferior data set, reserve the excellent data set and eliminate the inferior data set.

The production scheduling information comprises preset code generation conditions, the preset code generation conditions comprise production process code generation conditions and production equipment code generation conditions, the production coding data set comprises a production process coding data set and a production equipment coding data set, the preset coding function comprises a production process coding function and a production equipment coding function, and the step of generating the production coding data set in the preset coding function according to the production scheduling information comprises the following steps:

step S11, generating the production procedure coding data set in the production procedure coding function according to the production procedure coding generation condition;

and S12, generating the production equipment coding data set in the production equipment coding function according to the production equipment coding generation condition.

In this embodiment, it should be noted that the production process code generation condition refers to a condition for generating a production process code data set, and may include a random ordering condition and a time length ordering condition; the production device code generation condition refers to a condition for generating a production device code data set, and may include a cost distribution condition and a uniform distribution condition.

As an example, steps S11 to S12 include: generating a corresponding procedure random ordering coded data set in the production procedure coding function according to a random ordering condition, and generating a corresponding procedure time duration ordering coded data set in the production procedure coding function according to a time duration ordering condition; and generating a corresponding cost distribution coding data set in the production equipment coding function according to the cost distribution condition, and generating a corresponding uniform distribution coding data set in the production equipment coding function according to the uniform distribution condition.

The method comprises the following steps that production procedure code generation conditions comprise random sorting conditions and duration sorting conditions, a production procedure code data set comprises a procedure random sorting code data set and a procedure duration sorting code data set, each bit of code of the production procedure code data set corresponds to one production procedure, and the step of generating the production procedure code data set in a production procedure code function according to the production procedure code generation conditions comprises the following steps:

step S111, randomly sequencing each production procedure according to the random sequencing conditions to obtain a first permutation and combination;

step S112, inputting the first arrangement combination into the production procedure coding function, and generating the procedure random ordering coding data set;

step S113, acquiring processing length information corresponding to each production process, and sequencing each production process according to the time length sequencing condition and the processing length information to obtain a second permutation and combination;

and step S114, inputting the second permutation and combination into the production process coding function, and generating the process duration sequencing coding data set.

In this embodiment, the random ordering condition refers to an arrangement condition for ordering the processes in a random order; the first arrangement combination refers to a procedure arrangement sequence generated according to a random ordering condition; the time length sorting condition is an arrangement condition for sorting all the working procedures according to the residual processing time length; the second permutation and combination refers to a procedure permutation sequence generated according to the time length permutation condition.

As an example, steps S111 to S114 include: according to the random ordering condition, the initial sequence of each procedure is disordered, and each disordered procedure is rearranged at random to obtain the first ordering combination; generating the procedure random ordering coding data set in the production procedure coding function according to the first permutation and combination; processing length information corresponding to each production process is obtained, namely remaining processing length information corresponding to each workpiece is obtained, and the processes are sorted from large to small according to a duration sorting condition and the remaining processing length information to obtain a second permutation and combination; and inputting the second permutation and combination into the production process coding function to generate the process duration ordering coding data set, wherein after each process is completed, the residual processing time of all the workpieces needs to be updated and reordered, and the condition is performed after the production equipment is allocated, so that the total processing time of the workpieces is known.

Wherein the production device encoding generation condition includes a cost distribution condition and a uniform distribution condition, the production device encoding dataset includes a cost distribution encoding dataset and a uniform distribution encoding dataset, the production device encoding dataset at least corresponds to one production device, and the step of generating the production device encoding dataset in the production device encoding function according to the production device encoding generation condition includes:

step S121, obtaining configuration information corresponding to each production device, wherein the configuration information comprises processing cost information and workload information;

step S122, according to the processing cost information and the cost distribution condition, performing cost distribution on each production device to obtain a first distribution combination;

step S123, inputting the first distribution combination into the production equipment coding function to generate the cost distribution coding data set;

step S124, according to the workload information and the uniform distribution condition, uniformly distributing all the production equipment to obtain a second distribution combination;

step S125, inputting the second distribution combination into the production device coding function, and generating the uniform distribution coded data set.

In this embodiment, it should be noted that the processing cost information may include processing cost fees corresponding to each production device; the workload information may include workload amounts corresponding to the production devices, where the workload amounts refer to total processing time of the production devices after the current process is completed; the cost distribution condition refers to a distribution condition for distributing each production device according to the processing cost; the uniform distribution condition refers to a distribution condition for distributing each production device according to the work load; the first distribution combination is obtained by distributing each production device according to the processing cost; and the second distribution combination is obtained by distributing each production device according to the workload.

As one example, steps S121 to S125 include: acquiring configuration information corresponding to each production device, wherein the configuration information comprises processing cost information and workload information, and the corresponding configuration information can be acquired through a production specification or production label information of each production device; distributing each procedure to the production equipment with the lowest processing cost according to the cost distribution condition to obtain a first distribution combination, and inputting the first distribution combination into the production equipment coding function to generate the cost distribution coding data set; and according to the uniform distribution condition, randomly sequencing all the working procedures, sequentially distributing all the working procedures to the production equipment with the minimum work load to obtain a second distribution combination of all the production equipment, and inputting the second distribution combination into the production equipment coding function to generate the uniform distribution coding data set, wherein when the work load of a plurality of production equipment is the same, one production equipment is randomly selected, so that the aim of shortening the total processing time length on one production equipment in a large number of working procedure sets can be effectively fulfilled.

Wherein the production data set at least comprises one production coding object, the large neighborhood search comprises coding destruction and coding restoration, and the step of obtaining the corresponding target solution by performing the large neighborhood search on the initial solution comprises the following steps:

step S21, the code destruction is carried out on the production coding data set so as to delete each production coding object randomly to obtain a first temporary data set;

and S22, carrying out coding repair on the first temporary data set to reassign each deleted production coding object to obtain the target coding data set.

As an example, steps S21 to S22 include: performing code destruction on the production coding data set to randomly delete each production coding object to obtain a first temporary data set, wherein the production coding data set comprises a production process coding data set and a production equipment coding data set, so the code destruction also comprises production equipment code destruction and production process code destruction, the first temporary data set comprises a data set obtained after the production equipment coding data set is destroyed and a data set obtained after the production process coding data set is destroyed, the production equipment code destruction is performed, namely, each equipment code in the production equipment coding data set is deleted according to a certain preset deletion probability, the preset deletion probability can be set by self, for example, if the length of the coding data set is n, the deletion probability of each equipment code is 1/n, for the process code destruction, firstly, the production process coding data set is divided into a plurality of sub data sets according to the production equipment codes, and a process is randomly deleted in each sub data set according to the preset deletion probability; and performing coding restoration on the first temporary data set to perform reassignment on each deleted production coding object to obtain the target coding data set, wherein similarly, the coding restoration also comprises production equipment coding restoration and production procedure coding restoration, namely, performing random reassignment on the deleted equipment coding position for the production equipment coding restoration, namely, performing random reassignment on production equipment for each procedure according to a certain probability, for the production procedure coding restoration, reinserting all the deleted procedures into corresponding sub data sets, randomly selecting the insertion positions, finally updating the procedure sequence of the sub data sets into the initial production procedure coding data set, keeping the coding positions corresponding to the sub series unchanged, and using the data set obtained after the production equipment coding restoration and the data set obtained after the procedure coding restoration as the target coding data set.

For example, as shown in the example diagram of code destruction and code repair of the production equipment coding data set in fig. 4, assuming that the production equipment coding data set is {3,2,3,4, 1}, and the length n of the coding data set is 8, the deletion probability of each bit of code is 1/8, after the data set is destroyed, the third bit of code and the seventh bit of code in the data set are deleted, and then the third coding position and the seventh coding position are re-assigned randomly, i.e., the production equipment is re-assigned randomly, so that the repaired data set is {3,2,1,4,2,3, 1}; as can be seen from the example diagram of the code destruction and the code repair of the production process coded data set in FIG. 5, similarly, it is assumed that the production equipment coded data set is {3,2,3,4, 1}, the production equipment coded data set is divided into 4 sub-data sets according to the production equipment code, a process is randomly deleted in each sub-data set according to a preset deletion probability, the deletion probability is the same as the deletion probability setting mode of the code destruction of the production equipment, the deleted processes are respectively the 1 st process and the 2 nd process of the No. 2 workpiece, the repair operation is to reinsert the 2 processes into the process data set, the insertion positions are randomly selected, and the 1 st process is inserted into the process data set at this time

The 2 nd process is inserted into

The last position in the process is finally updated according to the code of the production equipment, the code position corresponding to the initial process of each production equipment is kept unchanged, and the code position is repairedThe data set is encoded by the recovered production process.

The aggregation result at least comprises 2 encoding data sets which are divided into a target encoding data set and a production encoding data set, the quality detection is carried out on the aggregation result based on a preset non-dominated sorting mechanism, and the step of obtaining the dual-target production scheduling optimization result comprises the following steps:

step S41, based on the preset non-dominated sorting mechanism, carrying out grade division on each coded data set to obtain corresponding grade division information;

and S42, detecting the quality of each coding data set according to each grade division information to obtain an optimal coding data set, and taking the optimal coding data set as the dual-target production scheduling optimization result.

As an example, steps S41 to S42 include: based on the preset non-dominated sorting mechanism, performing level sorting on each encoded data set to obtain corresponding level sorting information, wherein the level sorting refers to non-dominated level sorting, also called pareto level sorting, calculating a non-dominated level according to an objective function value, the objective function value is a production time function value and a production cost function value, sorting the encoded data sets according to the order of the non-dominated level from small to large, as shown in fig. 6, for individuals with the same non-dominated level, sorting is performed according to the congestion degree of the individuals on a front edge surface, the front edge surface refers to a curve formed by the individuals, the congestion degree represents the density value of the individuals in a space, and can be intuitively represented by a rectangle which does not include other individuals around the individuals; and screening the coded data sets according to the grade division information to obtain optimal coded data sets, and taking the optimal coded data sets as the dual-target production scheduling optimization results, wherein the advantages and the disadvantages of the coded data sets depend on the corresponding fitness, the coded data sets with high fitness are more likely to be reserved, the fitness can be the target function value, and the method for screening the advantages and the disadvantages can be roulette selection, sorting selection, most individual storage, immediate tournament selection and the like.

After the step of performing goodness detection on each encoded data set according to each hierarchical classification information to obtain an optimal encoded data set, and using the optimal encoded data set as the dual target production scheduling optimization result, the method further includes:

step A10, detecting whether the operation times of the dual-target production scheduling optimization model reach a preset maximum iteration time or not;

step A20, if yes, outputting the optimal coding data set;

step A30, if not, updating the production encoded data set to the optimal encoded data set, and returning to the step: and inputting the production coding data set into the dual-target production scheduling optimization model, and performing large neighborhood search on the production coding data set to obtain a corresponding target coding data set.

In this embodiment, it should be noted that the termination condition of the dual-target production scheduling optimization model is that the number of operations reaches a preset maximum number of iterations.

As an example, steps a10 to a30 include: detecting whether the operation times of the dual-target production scheduling optimization model reach a preset maximum iteration time or not; if yes, outputting an optimal encoding data set obtained after the quality screening, namely a non-dominant solution set, namely a pareto optimal solution; if not, updating the production coding data set to the optimal coding data set, namely updating the initial solution to the optimal solution, and returning to the step: and inputting the production coding data set into the dual-target production scheduling optimization model, and performing large neighborhood search on the production coding data set to obtain a corresponding target coding data set, wherein algorithm validity check can be performed after the algorithm operation is finished.

For example, assume a two-scale FJSP algorithm in which the number of workpieces is 20 for a small-scale algorithm, 5 processing steps are performed per workpiece, the number of production facilities is 5, the number of workpieces is 100 for a large-scale algorithm, 15 processing steps are performed per workpiece, and the number of production facilities is 20. The algorithm population size is 100, and the iteration number is 1000. As shown in fig. 7, the optimization results of the two examples are shown, and it can be seen that, compared with the initial population, the number of non-dominant solutions (that is, optimal solutions) in the optimized population is greatly increased, which means that a decision maker can obtain more high-quality candidate strategies at the same time, and can make a selection according to preferences among multiple optimization targets, and in addition, the quality of the optimal solution for the two optimization targets in the optimized population is also significantly improved, in the optimal solution with respect to the production time, the optimization ranges of the two examples reach about 30%, and at the same time, the production cost is also reduced, in the optimal solution with respect to the production cost, the optimization range of the production cost is 0, because the production equipment allocation scheme obtained by the minimum method when the initial solution is generated already reaches the lowest production cost, and cannot be further optimized subsequently, but on the premise of keeping the lowest cost unchanged, the large neighborhood search algorithm designed in the present application optimizes the production time by 20% to 40%, greatly improves the production efficiency, and also gives the decision maker greater flexibility.

Compared with the mode that various heuristic algorithms are commonly used to solve the FJSP problem at present, the method for optimizing the dual-target production scheduling first acquires production scheduling information, and generates a production coding data set in a preset coding function according to the production scheduling information; establishing a dual-target production scheduling optimization model, inputting the production coding data set into the dual-target production scheduling optimization model, and performing neighborhood search on the production coding data set to obtain a corresponding target coding data set; aggregating the target encoding data set and the production encoding data set to obtain an aggregation result; and based on a preset non-dominated sorting mechanism, detecting the quality of the aggregation result to obtain a dual-objective production scheduling optimization result. The problem of production scheduling with conflicting optimization targets or undefined target weights is solved, the technical defect that the solving efficiency is low due to the complexity of coding of a heuristic algorithm and the complexity of a neighborhood searching process is overcome, the whole algorithm process is simple and convenient to operate without parameter adjustment by using a simpler and more reasonable coding mode and a large neighborhood searching operator, the calculation complexity of the algorithm is effectively reduced, and the solving efficiency of the double-target FJSP problem is improved.

In addition, an embodiment of the present application further provides a dual target production scheduling optimization apparatus, as shown in fig. 8, the dual target production scheduling optimization apparatus includes:

a production encoding data set generating module 10, configured to obtain production scheduling information, and generate a production encoding data set in a preset encoding function according to the production scheduling information;

the model establishing and neighborhood searching module 20 is used for establishing a dual-target production scheduling optimization model, inputting the production coding data set into the dual-target production scheduling optimization model, and performing neighborhood searching on the production coding data set to obtain a corresponding target coding data set;

an aggregation module 30, configured to aggregate the target encoding data set and the production encoding data set to obtain an aggregation result;

and the non-dominated sorting mechanism module 40 is used for detecting the quality of the aggregation result based on a preset non-dominated sorting mechanism to obtain a dual-target production scheduling optimization result.

Optionally, the generating encoded data set module 10 is further configured to:

processing length information corresponding to each production process is obtained, and each production process is sequentially sequenced according to the duration sequencing condition and the processing length information to obtain a second permutation and combination;

Optionally, the generating encoded data set module 10 is further configured to:

Optionally, the model building and neighborhood searching module 20 is further configured to:

Optionally, the non-dominated sorting mechanism module 40 is further configured to:

based on the preset non-dominated sorting mechanism, carrying out grade division on each coded data set to obtain corresponding grade division information;

if yes, outputting the optimal encoding data set;

if not, updating the production coding data set into the optimal coding data set, and returning to the step: and inputting the production coding data set into the dual-target production scheduling optimization model, and performing neighborhood search on the production coding data set to obtain a corresponding target coding data set.

The dual-target production scheduling optimization device provided by the application adopts the dual-target production scheduling optimization method in the embodiment, and solves the technical problem that the solving efficiency of the current dual-target production scheduling problem is low. Compared with the prior art, the beneficial effects of the dual-target production scheduling optimization device provided by the embodiment of the application are the same as the beneficial effects of the dual-target production scheduling optimization method provided by the embodiment, and other technical features of the dual-target production scheduling optimization device are the same as those disclosed by the method of the embodiment, which are not described herein again.

An embodiment of the present application provides an electronic device, which includes: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the dual target production scheduling optimization method in the first embodiment.

Referring now to FIG. 9, shown is a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 9 is only an example, and should not bring any limitation to the functions and the use range of the embodiment of the present disclosure.

As shown in fig. 9, the electronic device may include a processing means (e.g., a central processing unit, a graphic processor, etc.) that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) or a program loaded from a storage means into a Random Access Memory (RAM). In the RAM, various programs and data necessary for the operation of the electronic apparatus are also stored. The processing device, the ROM, and the RAM are connected to each other through a bus. An input/output (I/O) interface is also connected to the bus.

Generally, the following systems may be connected to the I/O interface: input devices including, for example, touch screens, touch pads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, and the like; output devices including, for example, liquid Crystal Displays (LCDs), speakers, vibrators, and the like; storage devices including, for example, magnetic tape, hard disk, etc.; and a communication device. The communication means may allow the electronic device to communicate wirelessly or by wire with other devices to exchange data. While the figures illustrate an electronic device with various systems, it is to be understood that not all illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means, or installed from a storage means, or installed from a ROM. The computer program, when executed by a processing device, performs the above-described functions defined in the methods of the embodiments of the present disclosure.

The electronic equipment provided by the application adopts the dual-target production scheduling optimization method in the embodiment, and solves the technical problem that the solving efficiency of the existing dual-target production scheduling problem is low. Compared with the prior art, the beneficial effects of the electronic device provided by the embodiment of the present application are the same as the beneficial effects of the dual target production scheduling optimization method provided by the above embodiment, and other technical features of the electronic device are the same as those disclosed in the method of the above embodiment, which are not described herein again.

It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the foregoing description of embodiments, the particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

The present embodiment provides a computer-readable storage medium having computer-readable program instructions stored thereon for executing the dual target production scheduling optimization method in the first embodiment.

The computer readable storage medium provided by the embodiments of the present application may be, for example, a usb disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the above. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present embodiment, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

The computer-readable storage medium may be embodied in an electronic device; or may be present alone without being incorporated into the electronic device.

The computer readable storage medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring production scheduling information, and generating a production coding data set in a preset coding function according to the production scheduling information; establishing a dual-target production scheduling optimization model, inputting the production coding data set into the dual-target production scheduling optimization model, and performing neighborhood search on the production coding data set to obtain a corresponding target coding data set; aggregating the target encoding data set and the production encoding data set to obtain an aggregation result; and based on a preset non-dominated sorting mechanism, detecting the quality of the aggregation result to obtain a dual-objective production scheduling optimization result.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules described in the embodiments of the present disclosure may be implemented by software or hardware. Wherein the names of the modules do not in some cases constitute a limitation of the unit itself.

The computer-readable storage medium provided by the application stores computer-readable program instructions for executing the dual-target production scheduling optimization method, and solves the technical problem that the solving efficiency of the current dual-target production scheduling problem is low. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the embodiment of the present application are the same as the beneficial effects of the dual-target production scheduling optimization method provided by the above embodiment, and are not described herein again.

The computer program product provided by the application solves the technical problem that the solving efficiency of the existing dual-target production scheduling problem is low. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the present application are the same as the beneficial effects of the dual target production scheduling optimization method provided by the above embodiment, and are not described herein again.

The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.

Claims

1. A dual-target production scheduling optimization method is characterized by comprising the following steps:

2. The dual target production scheduling optimization method of claim 1, wherein the production scheduling information includes preset code generation conditions, the preset code generation conditions include production process code generation conditions and production equipment code generation conditions, the production coding data set includes a production process coding data set and a production equipment coding data set, the preset coding function includes a production process coding function and a production equipment coding function, and the step of generating the production coding data set in the preset coding function according to the production scheduling information includes:

3. The dual target production scheduling optimization method of claim 2, wherein the production process code generation conditions include random ordering conditions and duration ordering conditions, the production process coded data set includes a process random ordering coded data set and a process duration ordering coded data set, the production process coded data set corresponds to at least one production process, and the step of generating the production process coded data set in the production process code function according to the production process code generation conditions includes:

4. The dual target production scheduling optimization method of claim 2, wherein the production device encoding generation conditions include cost distribution conditions and uniform distribution conditions, the production device encoding data sets include cost distribution encoding data sets and uniform distribution encoding data sets, the production device encoding data sets correspond to at least one production device, and the step of generating the production device encoding data sets in the production device encoding functions according to the production device encoding generation conditions includes:

5. The dual target production scheduling optimization method of claim 1, wherein the production encoded data set includes at least one production encoded object, the neighborhood search includes code destruction and code repair, and the neighborhood search of the production encoded data set to obtain the corresponding target encoded data set includes:

6. The dual-target production scheduling optimization method of claim 1, wherein the aggregation result at least comprises one encoded data set, and the step of performing the goodness detection on the aggregation result based on the preset non-dominated sorting mechanism to obtain the dual-target production scheduling optimization result comprises:

7. The dual target production scheduling optimization method of claim 6, wherein after the step of performing the quality detection on each of the encoded data sets according to each of the hierarchical classification information to obtain an optimal encoded data set, and using the optimal encoded data set as the dual target production scheduling optimization result, the method further comprises:

if yes, outputting the optimal encoding data set;

8. A dual target production scheduling optimization device, characterized in that, the dual target production scheduling optimization device includes:

9. An electronic device, characterized in that the electronic device comprises:

at least one processor; and the number of the first and second groups,

a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the dual target production schedule optimization method of any one of claims 1 to 7.

10. A readable storage medium having stored thereon a program for implementing a dual target production scheduling optimization, the program being executable by a processor to implement the steps of the dual target production scheduling optimization method according to any one of claims 1 to 7.