Flexible job shop scheduling method and system based on hybrid evolutionary algorithm

Abstract

The present application provides a flexible job shop scheduling method and system based on a hybrid evolutionary algorithm, including the following steps: constructing a flexible job shop scheduling model based on multiple time constraints of setting time and processing time, and setting constraints; performing segmented integer encoding and decoding operations on the machine selection part and the process arrangement part of workpiece processing; initializing the population of the hybrid evolutionary algorithm to obtain the parent population; performing crossover operations and mutation operations on the parent population based on the neighborhood to obtain the first population, and performing local search on the parent population based on the neighborhood to obtain the second population; performing elite retention operations on the first population and the second population to obtain the elite population; when the hybrid evolutionary algorithm meets the iteration termination conditions, solving the flexible job shop scheduling model according to the elite population, and scheduling the flexible job shop. Using intelligent algorithms to solve scheduling problems can help workshops find the optimal scheduling method and effectively improve production efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of flexible job shop scheduling, and in particular to a flexible job shop scheduling method and system based on a hybrid evolutionary algorithm. Background Art

[0002] Since the Industrial Revolution, the world's scientific and technological level has developed rapidly in the past century. With the development of globalization, my country is an important participant. my country's scientific and technological development speed leads the world, but another disadvantage of globalization is that market competition is also globalized, and market competition will become more intense. Although my country's manufacturing industry has developed rapidly since the 1990s. However, the development of the world's manufacturing industry will still bring great challenges to my country. At present, the global market has shifted to a customer-centric approach, with more and more product types. In order to respond quickly to these changes, manufacturers are increasingly inclined to adopt a high-mix, low-volume production strategy. With the increasing diversity of customer needs and the growth of customized supply and demand consumption, the current manufacturing situation has developed from the traditional few types and large batches to multiple types and small batches. Therefore, China's manufacturing industry needs to firmly grasp this trend, take the lead in making corresponding reforms, achieve overtaking on the curve, and seize the opportunity of technological transformation and development. For example, the manufacturing model realizes the transformation from labor-intensive to technology-intensive. The transformation of the manufacturing model can realize intelligent manufacturing and green manufacturing, which are in line with the vision of mankind in the 21st century. The realization of these technologies and the research on workshop production scheduling are one of the important keys. Job shop scheduling is a general problem that includes many types, such as job shop scheduling problem (JSP) and flexible job shop scheduling problem (FJSP). Job shop scheduling is an important basis for realizing production automation, and its theoretical research has important practical significance. In actual job shop scheduling, there are often some auxiliary operation times, such as setup time. When the setup time is taken into account, the entire scheduling problem becomes more complicated.

[0003] In actual production, if decision makers do not give enough consideration to the allocation of production scheduling, some machines will be overloaded, some machines will be idle, or workpieces will be accumulated midway, which will extend production time and ultimately lead to reduced benefits in all aspects of the enterprise. Summary of the invention

[0004] The purpose of the present invention is to overcome the above technical deficiencies and provide a flexible job shop scheduling method and system based on a hybrid evolutionary algorithm to solve the technical problems of insufficient consideration of the allocation problem of production scheduling in the prior art and difficulty in flexible job shop scheduling.

[0005] In order to achieve the above technical objectives, in a first aspect, the technical solution of the present invention provides a flexible job shop scheduling method based on a hybrid evolutionary algorithm, comprising the following steps:

[0006] Construct a flexible job shop scheduling model with multiple time constraints based on setup time and processing time, and set the constraint conditions;

[0007] Perform segmented integer encoding and decoding operations on the machine selection part and process arrangement part of workpiece processing;

[0008] Initialize the population of the hybrid evolutionary algorithm to obtain the parent population;

[0009] Performing a crossover operation and a mutation operation on the parent population based on the neighborhood to obtain a first population, and performing a local search on the parent population based on the neighborhood to obtain a second population;

[0010] Performing an elite retention operation on the first population and the second population to obtain an elite population, and determining whether the hybrid evolutionary algorithm meets an iteration termination condition;

[0011] When the hybrid evolutionary algorithm meets the iteration termination condition, the flexible job shop scheduling model is solved according to the elite population, and the flexible job shop is scheduled.

[0012] Compared with the prior art, the beneficial effects of the present invention include:

[0013] The flexible job shop scheduling method based on hybrid evolutionary algorithm provided by the present invention uses intelligent algorithm to solve the scheduling problem, which can help the workshop find the optimal scheduling method, thereby helping enterprises to effectively improve production efficiency, significantly reduce production costs and consumption, and thus realize the vision of green manufacturing. In addition, a hybrid algorithm based on genetic algorithm and neighborhood search algorithm is designed to solve the problem of setting time flexible job shop scheduling, which realizes the function of quickly calculating good scheduling results by inputting workshop parameters, and can effectively improve the production efficiency of flexible job shops to produce more products.

[0014] According to some embodiments of the present invention, the step of determining whether the hybrid evolutionary algorithm satisfies an iteration termination condition comprises the following steps:

[0015] The fitness of the elite population is calculated, and whether the hybrid evolutionary algorithm meets the iteration termination condition is determined according to the fitness of the elite population.

[0016] According to some embodiments of the present invention, the optimization objective function of the flexible job shop scheduling model is:

[0017]

[0018] Among them Ci is the completion time of the workpiece, i: the index of the workpiece;

[0019] The function that minimizes the total setup time is

[0020]

[0021] PTt ijs is the completion time of the workpiece, j: the index of the process, s: the index of the operator.

[0022] According to some embodiments of the present invention, the segmented integer encoding and decoding operation of the machine selection part and the process arrangement part of the workpiece processing comprises the steps of:

[0023] Perform segmented integer coding operations on the machine selection part and process arrangement part of workpiece processing;

[0024] Reading the machine selection part of the workpiece processing, and converting the machine selection part into a machine sequence matrix, a processing time matrix, and a setup time matrix;

[0025] The process arrangement part of the workpiece processing is read, and the processing machine, processing time and setting time corresponding to each workpiece process are obtained according to the process arrangement part and the machine sequence matrix, the processing time matrix and the setting time matrix.

[0026] According to some embodiments of the present invention, the initialization process of the population of the hybrid evolutionary algorithm to obtain the parent population includes the steps of:

[0027] Set the population size, number of iterations, crossover probability and mutation probability of the hybrid evolutionary algorithm.

[0028] According to some embodiments of the present invention, performing a neighborhood-based crossover operation on the parent population comprises the steps of:

[0029] Selecting two chromosomes from the parent population in sequence as the first parent and the second parent according to the crossover probability;

[0030] intercepting machine selection partial segments of the first parent generation and the second parent generation, and selecting the portions of the first parent generation and the second parent generation that need to be crossed;

[0031] Performing a crossover operation on the part that needs to be crossovered to obtain a first offspring and a second offspring;

[0032] The machine selected portion of the fragments of the first parent is replaced by the first child, and the machine selected portion of the fragments of the second parent is replaced by the second child.

[0033] According to some embodiments of the present invention, performing a neighborhood-based crossover operation on the parent population comprises the steps of:

[0034] Selecting two chromosomes from the parent population in sequence as the first parent and the second parent according to the crossover probability;

[0035] intercepting partial segments of the process arrangement of the first parent generation and the second parent generation, and selecting the portions of the first parent generation and the second parent generation that need to be crossed;

[0036] Divide a true subset from the workpiece set, retain the genes in the first parent generation that are included in the true subset to the corresponding positions of the first child generation, and retain the genes in the second parent generation that are included in the true subset to the corresponding positions of the second child generation;

[0037] Copy the genes in the first parent generation that are not included in the true subset to the second child generation, and copy the genes in the second parent generation that are not included in the true subset to the first child generation;

[0038] A partial segment of the process arrangement of the first parent generation is replaced by the first child generation, and a partial segment of the process arrangement of the second parent generation is replaced by the second child generation.

[0039] According to some embodiments of the present invention, performing a neighborhood-based mutation operation on the parent population comprises the steps of:

[0040] Selecting a chromosome from the parent population as a parent chromosome according to the mutation probability;

[0041] Randomly selecting multiple positions on the parent chromosome, wherein the multiple positions represent different artifacts, and generating a neighborhood solution;

[0042] Calculating the fitness values ​​of all the neighborhood solutions, and selecting the individual with the best fitness value as the offspring chromosome;

[0043] The machine-selected partial segment of the parent chromosome is replaced by the process-arranged partial segment of the child chromosome.

[0044] According to some embodiments of the present invention, the step of performing a local search on the parent population based on a neighborhood to obtain a second population comprises the steps of:

[0045] Randomly select a number of positions on the machine selection part of the initial chromosome in the parent population, and change the selected machine to a machine with the shortest processing time or a machine with the shortest setup time;

[0046] Genes at multiple positions are randomly selected on a machine-selected partial segment of the initial chromosome in the parent population, and the genes at the multiple positions are reordered.

[0047] In a second aspect, the technical solution of the present invention provides a flexible job shop scheduling system based on a hybrid evolutionary algorithm, comprising: a shop scheduling model construction module for constructing a flexible job shop scheduling model based on multiple time constraints of setup time and processing time, and setting constraint conditions;

[0048] The encoding and decoding module is used to perform segmented integer encoding and decoding operations on the machine selection part and the process arrangement part of the workpiece processing;

[0049] An initialization module is used to initialize the population of the hybrid evolutionary algorithm to obtain the parent population;

[0050] A population processing module, configured to perform a crossover operation and a mutation operation on the parent population based on the neighborhood to obtain a first population, and perform a local search on the parent population based on the neighborhood to obtain a second population;

[0051] An elite retention operation module, used for performing an elite retention operation on the first population and the second population to obtain an elite population, and determining whether the hybrid evolutionary algorithm meets an iteration termination condition;

[0052] The flexible job shop scheduling module solves the flexible job shop scheduling model according to the elite population and schedules the flexible job shop when the hybrid evolutionary algorithm meets the iteration termination condition.

[0053] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, wherein the abstract drawings are identical to one of the drawings in the specification:

[0055] Figure 1 A flow chart of a flexible job shop scheduling method based on a hybrid evolutionary algorithm provided by an embodiment of the present invention;

[0056] Figure 2 A flowchart of flexible job shop scheduling based on a hybrid evolutionary algorithm is provided for another embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] It should be noted that, although the functional modules are divided in the system schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0059] The present invention provides a flexible job shop scheduling method based on a hybrid evolutionary algorithm. Using an intelligent algorithm to solve the scheduling problem can help the workshop find the optimal scheduling method, thereby helping the enterprise to effectively improve production efficiency, significantly reduce production costs and consumption, and thus realize the vision of green manufacturing.

[0060] The embodiments of the present invention are further described below in conjunction with the accompanying drawings.

[0061] Reference Figure 1 , Figure 1 A flow chart of a flexible job shop scheduling method based on a hybrid evolutionary algorithm is provided for one embodiment of the present invention; the flexible job shop scheduling method based on a hybrid evolutionary algorithm includes but is not limited to steps S110 to S160.

[0062] Step S110, constructing a flexible job shop scheduling model with multiple time constraints based on setup time and processing time, and setting constraint conditions;

[0063] Step S120, performing segmented integer encoding and decoding operations on the machine selection part and the process arrangement part of the workpiece processing;

[0064] Step S130, initializing the population of the hybrid evolutionary algorithm to obtain a parent population;

[0065] Step S140, performing a crossover operation and a mutation operation on the parent population based on the neighborhood to obtain a first population, and performing a local search on the parent population based on the neighborhood to obtain a second population;

[0066] Step S150, performing an elite retention operation on the first population and the second population to obtain an elite population, and determining whether the hybrid evolutionary algorithm meets the iteration termination condition;

[0067] Step S160, when the hybrid evolutionary algorithm meets the iteration termination condition, the flexible job shop scheduling model is solved according to the elite population, and the flexible job shop is scheduled.

[0068] In one embodiment, a flexible job shop scheduling method based on a hybrid evolutionary algorithm includes the steps of: constructing a flexible job shop scheduling model with multiple time constraints based on setup time and processing time, and setting constraints, wherein the constraints are as follows: at the same time, each machine can only process one process, and once the workpiece starts processing, the process cannot be interrupted; the processing time and setup time of each process vary depending on the selected machine and are known in advance; since the operator can leave during machine processing, the operator's movement time and transportation time between machines are not considered; the workpiece buffer on the machine is large enough; processing begins after the machine is set up, that is, the earliest time the machine setup time is completed is when the workpiece is just transferred to the machine.

[0069] Perform segmented integer encoding and decoding operations on the machine selection part and the process arrangement part of workpiece processing; initialize the population of the hybrid evolutionary algorithm to obtain the parent population; perform crossover and mutation operations on the parent population based on the neighborhood to obtain the first population, and perform local search on the parent population based on the neighborhood to obtain the second population; perform elite retention operations on the first population and the second population to obtain the elite population, and determine whether the hybrid evolutionary algorithm meets the iteration termination conditions; when the hybrid evolutionary algorithm meets the iteration termination conditions, solve the flexible job shop scheduling model according to the elite population, and schedule the flexible job shop.

[0070] The flexible job shop scheduling method based on the hybrid evolutionary algorithm provided in this embodiment uses an intelligent algorithm to solve the scheduling problem, which can help the workshop find the optimal scheduling method, thereby helping the enterprise to effectively improve production efficiency, significantly reduce production costs and consumption, and thus realize the vision of green manufacturing. In addition, a hybrid algorithm based on a genetic algorithm and a neighborhood search algorithm is designed to solve the problem of setting time for flexible job shop scheduling, which realizes the function of quickly calculating good scheduling results by inputting workshop parameters, and can effectively improve the production efficiency of flexible job shops to produce more products.

[0071] In one embodiment, a flexible job shop scheduling method based on a hybrid evolutionary algorithm includes the following steps: constructing a flexible job shop scheduling model based on multiple time constraints of setup time and processing time, and setting constraint conditions; performing piecewise integer encoding and decoding operations on the machine selection part and the process arrangement part of workpiece processing; initializing the population of the hybrid evolutionary algorithm to obtain a parent population; performing crossover operations and mutation operations on the parent population based on the neighborhood to obtain a first population, and performing local search on the parent population based on the neighborhood to obtain a second population; performing elite retention operations on the first population and the second population to obtain an elite population, and judging whether the hybrid evolutionary algorithm meets the iteration termination condition; when the hybrid evolutionary algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite population, and scheduling the flexible job shop. And judging whether the hybrid evolutionary algorithm meets the iteration termination condition, including the steps of: calculating the fitness of the elite population, and judging whether the hybrid evolutionary algorithm meets the iteration termination condition according to the fitness of the elite population.

[0072] In one embodiment, a flexible job shop scheduling method based on a hybrid evolutionary algorithm includes the steps of: constructing a flexible job shop scheduling model based on multiple time constraints of setup time and processing time, and setting constraints; performing piecewise integer encoding and decoding operations on the machine selection part and the process arrangement part of workpiece processing; initializing the population of the hybrid evolutionary algorithm to obtain a parent population; performing crossover operations and mutation operations on the parent population based on the neighborhood to obtain a first population, and performing local search on the parent population based on the neighborhood to obtain a second population; performing elite retention operations on the first population and the second population to obtain an elite population, and determining whether the hybrid evolutionary algorithm meets the iteration termination conditions; when the hybrid evolutionary algorithm meets the iteration termination conditions, solving the flexible job shop scheduling model according to the elite population, and scheduling the flexible job shop.

[0073] The optimization objective function of the flexible job shop scheduling model is:

[0074]

[0075] Among them C i is the completion time of the workpiece, i: the index of the workpiece;

[0076] The function that minimizes the total setup time is

[0077]

[0078] PTt ijs is the completion time of the workpiece, j: the index of the process, s: the index of the operator.

[0079] Reference Figure 2 , Figure 2A flow chart of a flexible job shop scheduling method based on a hybrid evolutionary algorithm is provided for one embodiment of the present invention; the flexible job shop scheduling method based on a hybrid evolutionary algorithm includes but is not limited to steps S210 to S230.

[0080] Step S210, performing a segmented integer encoding operation on a machine selection part and a process arrangement part of workpiece processing;

[0081] Step S220, reading the machine selection part of the workpiece processing, and converting the machine selection part into a machine sequence matrix, a processing time matrix, and a setting time matrix;

[0082] Step S230, read the process arrangement part of the workpiece processing, and obtain the processing machine, processing time and setting time corresponding to each workpiece process according to the process arrangement part and the machine sequence matrix, processing time matrix and setting time matrix.

[0083] In one embodiment, a flexible job shop scheduling method based on a hybrid evolutionary algorithm includes the steps of: constructing a flexible job shop scheduling model based on multiple time constraints of setup time and processing time, and setting constraints; performing piecewise integer encoding and decoding operations on the machine selection part and the process arrangement part of workpiece processing; initializing the population of the hybrid evolutionary algorithm to obtain a parent population; performing crossover operations and mutation operations on the parent population based on the neighborhood to obtain a first population, and performing local search on the parent population based on the neighborhood to obtain a second population; performing elite retention operations on the first population and the second population to obtain an elite population, and determining whether the hybrid evolutionary algorithm meets the iteration termination conditions; when the hybrid evolutionary algorithm meets the iteration termination conditions, solving the flexible job shop scheduling model according to the elite population, and scheduling the flexible job shop.

[0084] The machine selection part and the process arrangement part of the workpiece processing are subjected to segmented integer encoding and decoding operations, including the steps of:

[0085] Perform segmented integer coding operations on the machine selection part and process arrangement part of workpiece processing;

[0086] Read the machine selection part of the workpiece processing, and convert the machine selection part into a machine sequence matrix, a processing time matrix, and a setup time matrix;

[0087] The process arrangement part of the workpiece processing is read, and the processing machine, processing time and setting time corresponding to each workpiece process are obtained according to the process arrangement part and the machine sequence matrix, processing time matrix and setting time matrix.

[0088] In one embodiment, a flexible job shop scheduling method based on a hybrid evolutionary algorithm includes the following steps: constructing a flexible job shop scheduling model based on multiple time constraints of setup time and processing time, and setting constraint conditions; performing piecewise integer encoding and decoding operations on the machine selection part and the process arrangement part of workpiece processing; initializing the population of the hybrid evolutionary algorithm to obtain a parent population; performing crossover operations and mutation operations on the parent population based on the neighborhood to obtain a first population, and performing local search on the parent population based on the neighborhood to obtain a second population; performing elite retention operations on the first population and the second population to obtain an elite population, and determining whether the hybrid evolutionary algorithm meets the iteration termination condition; when the hybrid evolutionary algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite population, and scheduling the flexible job shop. Initializing the population of the hybrid evolutionary algorithm to obtain a parent population includes the following steps: setting the population size, number of iterations, crossover probability, and mutation probability of the hybrid evolutionary algorithm.

[0089] In one embodiment, a flexible job shop scheduling method based on a hybrid evolutionary algorithm includes the following steps: constructing a flexible job shop scheduling model based on multiple time constraints of setup time and processing time, and setting constraint conditions; performing piecewise integer encoding and decoding operations on the machine selection part and the process arrangement part of workpiece processing; initializing the population of the hybrid evolutionary algorithm to obtain a parent population; performing crossover operations and mutation operations on the parent population based on the neighborhood to obtain a first population, and performing local search on the parent population based on the neighborhood to obtain a second population; performing elite retention operations on the first population and the second population to obtain an elite population, and determining whether the hybrid evolutionary algorithm meets the iteration termination condition; when the hybrid evolutionary algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite population, and scheduling the flexible job shop. Initializing the population of the hybrid evolutionary algorithm to obtain a parent population includes the following steps: setting the population size, number of iterations, crossover probability, and mutation probability of the hybrid evolutionary algorithm.

[0090] Perform a neighborhood-based crossover operation on the parent population, including the following steps:

[0091] Select two chromosomes from the parent population in sequence as the first parent and the second parent according to the crossover probability;

[0092] Cut off the machine selection part fragments of the first parent generation and the second parent generation, and select the part of the first parent generation and the second parent generation that need to be crossed;

[0093] Perform a crossover operation on the parts that need to be crossed to obtain the first and second offspring;

[0094] Replace the machine selected portion of the fragment from the first parent with the first child, and replace the machine selected portion of the fragment from the second parent with the second child.

[0095] In one embodiment, a flexible job shop scheduling method based on a hybrid evolutionary algorithm includes the following steps: constructing a flexible job shop scheduling model based on multiple time constraints of setup time and processing time, and setting constraint conditions; performing piecewise integer encoding and decoding operations on the machine selection part and the process arrangement part of workpiece processing; initializing the population of the hybrid evolutionary algorithm to obtain a parent population; performing crossover operations and mutation operations on the parent population based on the neighborhood to obtain a first population, and performing local search on the parent population based on the neighborhood to obtain a second population; performing elite retention operations on the first population and the second population to obtain an elite population, and determining whether the hybrid evolutionary algorithm meets the iteration termination condition; when the hybrid evolutionary algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite population, and scheduling the flexible job shop. Initializing the population of the hybrid evolutionary algorithm to obtain a parent population includes the following steps: setting the population size, number of iterations, crossover probability, and mutation probability of the hybrid evolutionary algorithm.

[0096] Perform a neighborhood-based crossover operation on the parent population, including the following steps:

[0097] Select two chromosomes from the parent population in sequence as the first parent and the second parent according to the crossover probability;

[0098] Cut off the process arrangement fragments of the first parent generation and the second parent generation, and select the part where the first parent generation and the second parent generation need to be crossed;

[0099] Divide the true subset from the artifact set, retain the genes in the first parent generation that are included in the true subset to the corresponding positions of the first child generation, and retain the genes in the second parent generation that are included in the true subset to the corresponding positions of the second child generation;

[0100] The genes in the first parent generation that are not included in the true subset are copied to the second child generation, and the genes in the second parent generation that are not included in the true subset are copied to the first child generation;

[0101] Replace part of the process arrangement fragment of the first parent generation with the first child generation, and replace part of the process arrangement fragment of the second parent generation with the second child generation.

[0102] In one embodiment, a flexible job shop scheduling method based on a hybrid evolutionary algorithm includes the following steps: constructing a flexible job shop scheduling model based on multiple time constraints of setup time and processing time, and setting constraint conditions; performing piecewise integer encoding and decoding operations on the machine selection part and the process arrangement part of workpiece processing; initializing the population of the hybrid evolutionary algorithm to obtain a parent population; performing crossover operations and mutation operations on the parent population based on the neighborhood to obtain a first population, and performing local search on the parent population based on the neighborhood to obtain a second population; performing elite retention operations on the first population and the second population to obtain an elite population, and determining whether the hybrid evolutionary algorithm meets the iteration termination condition; when the hybrid evolutionary algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite population, and scheduling the flexible job shop. Initializing the population of the hybrid evolutionary algorithm to obtain a parent population includes the following steps: setting the population size, number of iterations, crossover probability, and mutation probability of the hybrid evolutionary algorithm.

[0103] Performing neighborhood-based mutation operations on the parent population includes the following steps:

[0104] A chromosome is selected from the parent population as the parent chromosome according to the mutation probability;

[0105] A plurality of positions are randomly selected on the parent chromosome, and the artifacts represented by the plurality of positions are different, and a neighborhood solution is generated;

[0106] Calculate the fitness values ​​of all neighborhood solutions and select the individuals with the best fitness values ​​as offspring chromosomes;

[0107] The machine selection part of the parent chromosome is replaced by the process arrangement part of the daughter chromosome.

[0108] In one embodiment, a flexible job shop scheduling method based on a hybrid evolutionary algorithm includes the following steps: constructing a flexible job shop scheduling model based on multiple time constraints of setup time and processing time, and setting constraint conditions; performing piecewise integer encoding and decoding operations on the machine selection part and the process arrangement part of workpiece processing; initializing the population of the hybrid evolutionary algorithm to obtain a parent population; performing crossover operations and mutation operations on the parent population based on the neighborhood to obtain a first population, and performing local search on the parent population based on the neighborhood to obtain a second population; performing elite retention operations on the first population and the second population to obtain an elite population, and determining whether the hybrid evolutionary algorithm meets the iteration termination condition; when the hybrid evolutionary algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite population, and scheduling the flexible job shop. Initializing the population of the hybrid evolutionary algorithm to obtain a parent population includes the following steps: setting the population size, number of iterations, crossover probability, and mutation probability of the hybrid evolutionary algorithm.

[0109] The second population is obtained by performing a local search based on the neighborhood of the parent population, including the steps:

[0110] Randomly select several positions on the machine selection part of the initial chromosome in the parent population, and change the selected machine to the machine with the shortest processing time or the machine with the shortest setup time;

[0111] Genes at multiple positions are randomly selected on a machine-selected partial segment of an initial chromosome in a parent population, and the genes at the multiple positions are reordered.

[0112] In one embodiment, a flexible job shop scheduling method based on a hybrid evolutionary algorithm comprises the following steps:

[0113] The encoding method used is segmented integer encoding. Segmented encoding means processing the process information separately according to the problem description. For the classic FJSP problem, it is usually divided into two sub-problems: process allocation and machine selection. Therefore, a segmented integer encoding strategy is adopted. The segmented type means that the chromosome is divided into several parts, and the genes on each part are different and carry different information. In view of the fact that MTCFJSP needs to consider the sub-problem of personnel allocation, this problem is considered during decoding, and in order to reduce the complexity of the chromosome, the chromosome encoding of the MTCFJSP problem is divided into two parts: the machine selection part (machines selection, MS) and the operation sequencing part (operations sequencing, OS), with a total length of 2×T0. T0 represents the total number of workpiece processes.

[0114] Right now

[0115] The specific decoding process of the hybrid multi-objective evolutionary algorithm is as follows.

[0116] 1. Decode the machine selection part, read the MS segments from left to right and convert them into machine order matrix J M , processing time matrix T1, setting time matrix T2. M (i, j) represents workpiece J i The jth process is O ij The machine selected for processing; T1(i, j) represents process O ij The time required for processing on the selected machine; T2(i, j) represents the process O ij If the time needs to be set on the selected machine, the size of the time to be set. M The relationship between (i, j), T1(i, j) and T2(i, j) is one-to-one.

[0117] 2. Decode the process arrangement part and read the OS fragments from left to right. According to the machine order matrix J corresponding to step 1 M , processing time matrix T1, setting time matrix T2, and each workpiece process O are obtained in turn ij Corresponding processing machine M k , Processing time T ijk and set time St ijk .

[0118] Population initialization, the specific steps are as follows:

[0119] 1. OS part: The process arrangement part is generated in the same way as the random initialization method.

[0120] 2. MS part. The machine selection part determines each machine position according to the machine load by the process arrangement part.

[0121] After the population is initialized, a crossover operation is performed to generate a new chromosome. Due to the segmented encoding strategy of this algorithm, different chromosome segments on the same chromosome carry different genes. If different genes cross, the information carried by the chromosome will be destroyed. Therefore, different crossover methods are used for different segments, that is, when crossing, the different segments of the chromosome are separated, and after crossing, they are spliced ​​together to form a complete chromosome for inheritance.

[0122] 1. The MS part adopts two-point crossover. The steps of MS part crossover are as follows:

[0123] (1): Select two chromosomes from the population in sequence according to the crossover probability;

[0124] (2): Cut the MS fragments of the two chromosomes and select the parts of the two chromosomes to be crossed;

[0125] (3): Crossover operation to obtain new individuals;

[0126] (4): The obtained offspring 1 and offspring 2 replace the MS part in the parent 1 and parent 2.

[0127] 2. The OS part uses the POX crossover operator. If the process arrangement part uses two-point crossover like the MS part, the new chromosome generated may not be in line with the actual chromosome. For example, a workpiece process is missing or increased. Therefore, a conflict detection operation needs to be added, which will make the crossover more complicated. The steps of POX crossover are:

[0128] (1): Select two chromosomes from the population in sequence according to the crossover probability;

[0129] (2): Cut the OS fragments of the two chromosomes and select the parts of the two chromosomes to be crossed;

[0130] (3): Divide the artifact set into a true subset, and retain the genes in parent 1 and parent 2 that are included in the true subset to the corresponding positions of offspring 1 and offspring 2;

[0131] (4): The genes in parent 2 and parent 1 that are not included in the true subset are copied to offspring 1 and offspring 2 in sequence;

[0132] (5): Child 1 and Child 2 replace the OS part in Parent 1 and Parent 2.

[0133] Based on the mutation operator of neighborhood search, the specific steps of mutation operation are as follows:

[0134] 1: Select a chromosome from the population based on the probability of mutation;

[0135] 2: Randomly select r positions on the selected chromosome, and make sure that the artifacts represented by the r positions are different, and then generate their neighborhood solutions;

[0136] 3: Calculate the fitness values ​​of all neighborhood solutions and select the best individuals as offspring;

[0137] 4: Replace the parent MS fragment with the obtained child OS fragment.

[0138] Since the NSGA-Ⅱ algorithm has strong global search capabilities but weak local search capabilities, the local search designed by this algorithm is performed after crossover and mutation are completed, which is also the manifestation of this algorithm as a hybrid algorithm. The specific local search consists of the neighborhood structure described below.

[0139] 1. In the MS segment of the initial chromosome, randomly select 1-3 positions, and then change the selected machine to the one with the shortest processing time;

[0140] 2. In the MS segment of the initial chromosome, randomly select 1-3 positions and change the selected machine to the fastest machine to start processing, that is, set the shortest machine time or the time that does not need to be set;

[0141] 3. In the OS segment of the initial chromosome, three positions are randomly selected and the genes at these three positions are reordered.

[0142] After the population has undergone crossover, mutation, and local search operations, a group of new individuals are obtained, and the algorithm proceeds to another step, the elite retention operation. The algorithm is iterated, and the algorithm is optimized in the process of continuous iteration.

[0143] The present invention also provides a flexible job shop scheduling system based on a hybrid evolutionary algorithm, comprising: a shop scheduling model construction module, used to construct a flexible job shop scheduling model with multiple time constraints based on setting time and processing time, and set constraint conditions; a coding and decoding module, used to perform piecewise integer coding and decoding operations on the machine selection part and the process arrangement part of workpiece processing; an initialization module, used to initialize the population of the hybrid evolutionary algorithm to obtain a parent population; a population processing module, used to perform crossover operations and mutation operations on the parent population based on the neighborhood to obtain a first population, and perform local search on the parent population based on the neighborhood to obtain a second population; an elite retention operation module, used to perform elite retention operations on the first population and the second population to obtain an elite population, and determine whether the hybrid evolutionary algorithm meets the iteration termination conditions; a flexible job shop scheduling module, when the hybrid evolutionary algorithm meets the iteration termination conditions, solves the flexible job shop scheduling model according to the elite population, and schedules the flexible job shop.

[0144] The present invention also provides a flexible job shop scheduling system based on a hybrid evolutionary algorithm, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the flexible job shop scheduling method based on the hybrid evolutionary algorithm as described above is implemented.

[0145] The processor and the memory may be connected via a bus or other means.

[0146] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0147] It should be noted that the flexible job shop scheduling system based on the hybrid evolutionary algorithm in this embodiment may include a business processing module, an edge database, a server version information register, and a data synchronization module. When the processor executes the computer program, it implements the flexible job shop scheduling method based on the hybrid evolutionary algorithm as applied to the flexible job shop scheduling system based on the hybrid evolutionary algorithm as mentioned above.

[0148] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0149] In addition, an embodiment of the present invention also provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are executed by a processor or controller, for example, by a processor in the above-mentioned terminal embodiment, so that the above-mentioned processor can execute the flexible job shop scheduling method based on the hybrid evolutionary algorithm in the above-mentioned embodiment.

[0150] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0151] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above-mentioned implementation mode. Technical personnel familiar with the field can also make various equivalent deformations or substitutions without violating the spirit of the present invention. These equivalent deformations or substitutions are all included in the scope defined by the claims of the present invention.

[0152] The specific implementation of the present invention described above does not constitute a limitation on the protection scope of the present invention. Any other corresponding changes and modifications made based on the technical concept of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A flexible job shop scheduling method based on a hybrid evolutionary algorithm, characterized in that: The following steps are involved: Construct a flexible job shop scheduling model with multiple time constraints based on setup time and processing time, and set the constraint conditions; Perform segmented integer encoding and decoding operations on the machine selection part and process arrangement part of workpiece processing; Initialize the population of the hybrid evolutionary algorithm to obtain the parent population; Performing a crossover operation and a mutation operation on the parent population based on the neighborhood to obtain a first population, and performing a local search on the parent population based on the neighborhood to obtain a second population; Performing an elite retention operation on the first population and the second population to obtain an elite population, and determining whether the hybrid evolutionary algorithm meets an iteration termination condition; When the hybrid evolutionary algorithm meets the iteration termination condition, the flexible job shop scheduling model is solved according to the elite population, and the flexible job shop is scheduled; The step of performing segmented integer encoding and decoding operations on the machine selection part and the process arrangement part of the workpiece processing comprises the following steps: Perform segmented integer coding operations on the machine selection part and process arrangement part of workpiece processing; Reading the machine selection part of the workpiece processing, and converting the machine selection part into a machine sequence matrix, a processing time matrix, and a setup time matrix; Reading a process arrangement portion of workpiece processing, and obtaining a processing machine, processing time, and setup time corresponding to each workpiece process according to the process arrangement portion and the machine sequence matrix, the processing time matrix, and the setup time matrix; The parent population is locally searched based on the neighborhood to obtain a second population, comprising the steps of: Randomly select a number of positions on the machine selection part of the initial chromosome in the parent population, and change the selected machine to a machine with the shortest processing time or a machine with the shortest setup time; Genes at multiple positions are randomly selected on a machine-selected partial segment of the initial chromosome in the parent population, and the genes at the multiple positions are reordered.

2. A flexible job shop scheduling method based on a hybrid evolutionary algorithm according to claim 1, characterized in that: The method of judging whether the hybrid evolutionary algorithm satisfies the iteration termination condition comprises the steps of: The fitness of the elite population is calculated, and whether the hybrid evolutionary algorithm meets the iteration termination condition is determined according to the fitness of the elite population.

3. The flexible job shop scheduling method based on hybrid evolutionary algorithm according to claim 1 is characterized in that: The optimization objective function of the flexible job shop scheduling model is: in C i is the completion time of the workpiece, i : The index of the artifact; The function that minimizes the total setup time is: in PT ijs is the completion time of the workpiece, j : Index of the process s : The index of the operator.

4. The flexible job shop scheduling method based on hybrid evolutionary algorithm according to claim 1 is characterized in that: The process of initializing the population of the hybrid evolutionary algorithm to obtain the parent population includes the following steps: Set the population size, number of iterations, crossover probability and mutation probability of the hybrid evolutionary algorithm.

5. The flexible job shop scheduling method based on hybrid evolutionary algorithm according to claim 4 is characterized in that: The parent population is subjected to a neighborhood-based crossover operation, comprising the steps of: Selecting two chromosomes from the parent population in sequence as the first parent and the second parent according to the crossover probability; intercepting machine selection partial segments of the first parent generation and the second parent generation, and selecting the portions of the first parent generation and the second parent generation that need to be crossed; Performing a crossover operation on the part that needs to be crossovered to obtain a first offspring and a second offspring; The machine selected portion of the fragments of the first parent is replaced by the first child, and the machine selected portion of the fragments of the second parent is replaced by the second child.

6. The flexible job shop scheduling method based on hybrid evolutionary algorithm according to claim 4 is characterized in that: The parent population is subjected to a neighborhood-based crossover operation, comprising the steps of: Selecting two chromosomes from the parent population in sequence as the first parent and the second parent according to the crossover probability; intercepting partial segments of the process arrangement of the first parent generation and the second parent generation, and selecting the portions of the first parent generation and the second parent generation that need to be crossed; Divide a true subset from the workpiece set, retain the genes in the first parent generation that are included in the true subset to the corresponding positions of the first child generation, and retain the genes in the second parent generation that are included in the true subset to the corresponding positions of the second child generation; Copy the genes in the first parent generation that are not included in the true subset to the second child generation, and copy the genes in the second parent generation that are not included in the true subset to the first child generation; A partial segment of the process arrangement of the first parent generation is replaced by the first child generation, and a partial segment of the process arrangement of the second parent generation is replaced by the second child generation.

7. The flexible job shop scheduling method based on hybrid evolutionary algorithm according to claim 4 is characterized in that: The parent population is subjected to a neighborhood-based mutation operation, comprising the steps of: Selecting a chromosome from the parent population as a parent chromosome according to the mutation probability; Randomly selecting multiple positions on the parent chromosome, wherein the multiple positions represent different artifacts, and generating a neighborhood solution; Calculating the fitness values ​​of all the neighborhood solutions, and selecting the individual with the best fitness value as the offspring chromosome; The machine-selected partial segment of the parent chromosome is replaced by the process-arranged partial segment of the child chromosome.

8. A flexible job shop scheduling system based on a hybrid evolutionary algorithm, characterized in that: include: The workshop scheduling model building module is used to build a flexible job shop scheduling model with multiple time constraints based on setup time and processing time, and set constraint conditions; The encoding and decoding module is used to perform segmented integer encoding and decoding operations on the machine selection part and the process arrangement part of the workpiece processing; An initialization module is used to initialize the population of the hybrid evolutionary algorithm to obtain the parent population; A population processing module, configured to perform a crossover operation and a mutation operation on the parent population based on the neighborhood to obtain a first population, and perform a local search on the parent population based on the neighborhood to obtain a second population; An elite retention operation module, used for performing an elite retention operation on the first population and the second population to obtain an elite population, and determining whether the hybrid evolutionary algorithm meets an iteration termination condition; A flexible job shop scheduling module, when the hybrid evolutionary algorithm meets the iteration termination condition, solves the flexible job shop scheduling model according to the elite population and schedules the flexible job shop; The segmented integer encoding and decoding operation for the machine selection part and the process arrangement part of the workpiece processing includes: Perform segmented integer coding operations on the machine selection part and process arrangement part of workpiece processing; Reading the machine selection part of the workpiece processing, and converting the machine selection part into a machine sequence matrix, a processing time matrix, and a setup time matrix; Reading a process arrangement portion of workpiece processing, and obtaining a processing machine, processing time, and setup time corresponding to each workpiece process according to the process arrangement portion and the machine sequence matrix, the processing time matrix, and the setup time matrix; The parent population is locally searched based on the neighborhood to obtain a second population, including: Randomly select a number of positions on the machine selection part of the initial chromosome in the parent population, and change the selected machine to a machine with the shortest processing time or a machine with the shortest setup time; Genes at multiple positions are randomly selected on a machine-selected partial segment of the initial chromosome in the parent population, and the genes at the multiple positions are reordered.

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