CN114580743A - Flexible job shop scheduling method and system based on hybrid evolution algorithm - Google Patents
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Abstract
The application provides a flexible job shop scheduling method and system based on a hybrid evolution algorithm, which comprises the following steps: constructing a multi-time constraint flexible job shop scheduling model based on the set time and the processing time, and setting constraint conditions; performing segmented integer encoding and decoding operations on a machine selection part and a process arrangement part of the workpiece processing; initializing the population of the hybrid evolution algorithm to obtain a parent population; performing cross operation and mutation operation 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 reservation operation on the first population and the second population to obtain an elite population; and when the hybrid evolution algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite group, and scheduling the flexible job shop. The intelligent algorithm is adopted to solve the scheduling problem, so that a workshop can be helped to find an optimal scheduling mode, and the production efficiency is effectively improved.
Description
Technical Field
The invention relates to the technical field of flexible job shop scheduling, in particular to a flexible job shop scheduling method and system based on a hybrid evolution algorithm.
Background
Since the industrial revolution, the scientific and technological level of the world has been rapidly developed in the past hundred years, and the development of globalization is an important participant in China, the scientific and technological development speed of China is ahead of the world, but another disadvantage of globalization is that the market competition is also globalization, and the market competition is more intense. Although the manufacturing industry in our country has developed rapidly since the last 90 s. However, the development of the world manufacturing industry still brings great challenges to China. The global market has now been transformed to customer-centric approaches, with an increasing number of product categories. In order to react quickly to these changes, manufacturers are increasingly inclined to adopt high-mix low-volume production strategies. With the increasing diversity of customer needs and the growing customized supply and demand, the current manufacturing situation is moving from traditional few varieties, large batches to many varieties, small batches. Therefore, the Chinese manufacturing industry needs to firmly grasp the trend, take the corresponding reform to realize curve overtaking and grasp the opportunity of the transformation development of the technology. Just as manufacturing models are labor intensive to turn to technology intensive, converting manufacturing models can achieve manufacturing modes that meet the human vision of the 21 st century, such as smart manufacturing and green manufacturing. The research of the technology for realizing workshop production scheduling is one of the important keys. The workshop scheduling is a general problem, and includes many kinds, such as a workshop scheduling problem (JSP) and a flexible job workshop scheduling problem (FJSP). The job shop scheduling is an important basis for realizing production automation, and the theoretical research of the job shop scheduling has important practical significance. In actual plant scheduling, there are usually some auxiliary operation times, such as setup time, which makes the whole scheduling problem more complicated when the setup time is considered.
In actual production, if the decision maker does not consider the allocation problem of the production scheduling, some machines are overloaded, some machines are idle, or workpieces are accumulated halfway, the production time is prolonged, and finally the benefits of the enterprise are reduced.
Disclosure of Invention
The invention aims to overcome the technical defects, provides a flexible job shop scheduling method and system based on a hybrid evolution algorithm, and solves the technical problems that the distribution problem of production scheduling is not considered sufficiently and the flexible job shop scheduling is difficult in the prior art.
In order to achieve the above technical objective, in a first aspect, the technical solution of the present invention provides a flexible job shop scheduling method based on a hybrid evolution algorithm, including the following steps:
constructing a flexible job shop scheduling model based on multi-time constraint of setting time and processing time, and setting constraint conditions;
performing segmented integer encoding and decoding operations on a machine selection part and a process arrangement part of the workpiece processing;
initializing the population of the hybrid evolution algorithm to obtain a parent population;
performing cross operation and mutation operation 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 reservation operation on the first population and the second population to obtain an elite population, and judging whether the mixed evolutionary algorithm meets an iteration termination condition;
and when the hybrid evolution algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite group, and scheduling the flexible job shop.
Compared with the prior art, the invention has the beneficial effects that:
according to the flexible job workshop scheduling method based on the hybrid evolution algorithm, the intelligent algorithm is adopted to solve the scheduling problem, so that the workshop can be helped to find the optimal scheduling mode, the production efficiency of an enterprise is effectively improved, the production cost and the consumption are greatly reduced, and the vision of green manufacturing is realized. And a hybrid algorithm based on a genetic algorithm and a neighborhood search algorithm is designed for solving the problem of flexible job shop scheduling in set time, the function of inputting workshop parameters to quickly calculate a good scheduling result is realized, the production efficiency of a flexible job shop can be effectively improved, and more products can be produced.
According to some embodiments of the present invention, the determining whether the hybrid evolution algorithm satisfies the iteration termination condition includes:
and calculating the fitness of the elite population, and judging whether the hybrid evolution algorithm meets the iteration termination condition according to the fitness of the elite population.
According to some embodiments of the invention, the optimization objective function of the flexible job shop scheduling model is:
wherein C isiFinishing time for the workpiece, i: an index of the workpiece;
minimizing the total set time function to
PTt thereinijsFinish time for workpiece, j: index of procedure s: the index of the operator.
According to some embodiments of the invention, the performing segmented integer encoding and decoding operations on the machine selection portion and the process arrangement portion of the workpiece processing comprises the steps of:
carrying out sectional integer coding operation on a machine selection part and a 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 setting time matrix;
and reading a process arrangement part for workpiece processing, and obtaining a processing machine, processing time and setting time corresponding to each workpiece process according to the process arrangement part, the machine sequence matrix, the processing time matrix and the setting time matrix.
According to some embodiments of the present invention, the initializing the population of the hybrid evolution algorithm to obtain the parent population includes:
and setting the population size, the iteration times, the cross probability and the mutation probability of the hybrid evolution algorithm.
According to some embodiments of the invention, performing a cross-operation on the parent population based on a neighborhood comprises:
sequentially selecting two chromosomes from the parent population as a first parent and a second parent according to the cross probability;
intercepting machine selection part fragments of the first parent and the second parent, and selecting parts of the first parent and the second parent which need to be crossed;
performing cross operation on the part needing to be crossed to obtain a first filial generation and a second filial generation;
replacing the machine selection part segment of the first parent with the first child, and replacing the machine selection part segment of the second parent with the second child.
According to some embodiments of the invention, performing a cross-operation on the parent population based on a neighborhood comprises:
sequentially selecting two chromosomes from the parent population as a first parent and a second parent according to the cross probability;
intercepting process arrangement partial fragments of the first parent and the second parent, and selecting a part of the first parent and the second parent which needs to be crossed;
partitioning a proper subset from a set of artifacts, retaining genes contained in the proper subset in the first parent in a first child correspondence position, and retaining genes contained in the proper subset in the second parent in a second child correspondence position;
copying genes in the first parent that are not included in the proper subset to the second child, copying genes in the second parent that are not included in the proper subset to the first child;
replacing the process arrangement partial segment of the first parent with the first child, and replacing the process arrangement partial segment of the second parent with the second child.
According to some embodiments of the invention, performing a mutation operation on the parent population based on a neighborhood comprises:
selecting a chromosome from the parent population as a parent chromosome according to the mutation probability;
randomly selecting a plurality of positions on the parent chromosome, wherein the workpieces represented on the positions are different, and generating a neighborhood solution;
calculating the fitness values of all the neighborhood solutions, and selecting the individual with the best fitness value as a child chromosome;
replacing the machine-selected partial segment of the parent chromosome with the process arrangement partial segment of the child chromosome.
According to some embodiments of the invention, the locally searching the parent population based on the neighborhood to obtain the second population comprises:
randomly selecting a plurality of positions on a machine selection part segment of an initial chromosome in the parent population, and changing the selected machine into a machine with the shortest processing time or a machine with the shortest setting time;
randomly selecting genes at a plurality of positions on the machine-selected partial segment of the initial chromosome in the parent population, and reordering the genes at the plurality of positions.
In a second aspect, a technical solution of the present invention provides a flexible job shop scheduling system based on a hybrid evolution algorithm, including: the workshop scheduling model building module is used for building a flexible job workshop scheduling model based on multi-time constraint of set time and processing time and setting constraint conditions;
the encoding and decoding module is used for carrying out segmented integer encoding and decoding operations on a machine selection part and a process arrangement part of workpiece processing;
the initialization module is used for initializing the population of the hybrid evolution algorithm to obtain a parent population;
the population processing module is used for performing cross operation and mutation operation 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;
the elite reservation operation module is used for performing elite reservation operation on the first population and the second population to obtain an elite population and judging whether the hybrid evolution algorithm meets an iteration termination condition;
and the flexible job shop scheduling module is used for solving the flexible job shop scheduling model according to the elite group and scheduling the flexible job shop when the hybrid evolution algorithm meets the iteration termination condition.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which the abstract is to be fully consistent with one of the figures of the specification:
FIG. 1 is a flowchart of a flexible job shop scheduling method based on a hybrid evolution algorithm according to an embodiment of the present invention;
fig. 2 is a flowchart of flexible job shop scheduling based on a hybrid evolution algorithm according to another embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It should be noted that although functional block divisions are provided in the system drawings and logical orders are shown in the flowcharts, in some cases, the steps shown and described may be performed in different orders than the block divisions in the systems or in the flowcharts. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The invention provides a flexible job shop scheduling method based on a hybrid evolution algorithm, which can help a shop to find an optimal scheduling mode by solving a scheduling problem through an intelligent algorithm, thereby helping an enterprise to effectively improve the production efficiency, greatly reducing the production cost and consumption and further realizing the vision of green manufacturing.
The embodiments of the present invention will be further explained with reference to the drawings.
Referring to fig. 1, fig. 1 is a flowchart of a flexible job shop scheduling method based on a hybrid evolution algorithm according to an embodiment of the present invention; the flexible job shop scheduling method based on the hybrid evolution algorithm includes, but is not limited to, steps S110 to S160.
Step S110, constructing a flexible job shop scheduling model based on multi-time constraint of setting time and processing time, and setting constraint conditions;
step S120, performing segmented integer coding and decoding operation on a machine selection part and a process arrangement part of workpiece processing;
step S130, initializing the population of the hybrid evolution algorithm to obtain a parent population;
step S140, performing cross operation and mutation operation 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;
step S150, performing elite reservation operation on the first population and the second population to obtain an elite population, and judging whether the hybrid evolution algorithm meets an iteration termination condition;
and S160, when the hybrid evolution algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite group, and scheduling the flexible job shop.
In one embodiment, the flexible job shop scheduling method based on the hybrid evolution algorithm comprises the following steps: constructing a flexible job shop scheduling model based on multi-time constraint of set time and processing time, and setting constraint conditions, wherein the constraint conditions are as follows: at the same time, each machine can only process one procedure, and once the workpiece begins to process, the process cannot be interrupted; the processing time and the setup time of each process are different for the selected machine and known in advance; because the operator can leave when the machine is processed, the moving time and the transportation time of the operator which is separated from the workpiece between the machines are not considered; the workpiece buffer area above the machine is large enough; the machining is started after the machine set-up is completed, i.e. the machine set-up time is completed first with the workpiece just transferred to the machine.
Performing segmented integer coding and decoding operations on a machine selection part and a process arrangement part of workpiece processing; initializing the population of the hybrid evolution algorithm to obtain a parent population; performing cross operation and mutation operation 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 reservation operation on the first population and the second population to obtain an elite population, and judging whether the mixed evolution algorithm meets an iteration termination condition; and when the hybrid evolution algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite group, and scheduling the flexible job shop.
According to the flexible job shop scheduling method based on the hybrid evolution algorithm, the intelligent algorithm is adopted to solve the scheduling problem, so that the workshop can be helped to find the optimal scheduling mode, the production efficiency of an enterprise is effectively improved, the production cost and the consumption are greatly reduced, and the vision of green manufacturing is realized. And a hybrid algorithm based on a genetic algorithm and a neighborhood search algorithm is designed for solving the problem of flexible job shop scheduling in set time, the function of inputting workshop parameters to quickly calculate a good scheduling result is realized, the production efficiency of a flexible job shop can be effectively improved, and more products can be produced.
In one embodiment, the flexible job shop scheduling method based on the hybrid evolution algorithm comprises the following steps: constructing a flexible job shop scheduling model based on multi-time constraint of setting time and processing time, and setting constraint conditions; performing segmented integer coding and decoding operations on a machine selection part and a process arrangement part of workpiece processing; initializing the population of the hybrid evolution algorithm to obtain a parent population; performing cross operation and mutation operation 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 reservation operation on the first population and the second population to obtain an elite population, and judging whether a hybrid evolution algorithm meets an iteration termination condition; and when the hybrid evolution algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite group, and scheduling the flexible job shop. And judging whether the hybrid evolution algorithm meets the iteration termination condition or not, comprising the following steps: and calculating the fitness of the elite population, and judging whether the hybrid evolution algorithm meets the iteration termination condition according to the fitness of the elite population.
In one embodiment, the flexible job shop scheduling method based on the hybrid evolution algorithm comprises the following steps: constructing a multi-time constraint flexible job shop scheduling model based on the set time and the processing time, and setting constraint conditions; performing segmented integer coding and decoding operations on a machine selection part and a process arrangement part of workpiece processing; initializing the population of the hybrid evolution algorithm to obtain a parent population; performing cross operation and mutation operation 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 reservation operation on the first population and the second population to obtain an elite population, and judging whether a hybrid evolution algorithm meets an iteration termination condition; and when the hybrid evolution algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite group, and scheduling the flexible job shop.
The optimization objective function of the flexible job shop scheduling model is as follows:
wherein C isiFinishing time for the workpiece, i: an index of the workpiece;
minimizing the total set time function to
PTt thereinijsFinish time for workpiece, j: index of procedure s: the index of the operator.
Referring to fig. 2, fig. 2 is a flowchart of a flexible job shop scheduling method based on a hybrid evolution algorithm according to an embodiment of the present invention; the flexible job shop scheduling method based on the hybrid evolution algorithm includes, but is not limited to, steps S210 to S230.
Step S210, carrying out sectional integer coding operation on a machine selection part and a process arrangement part of workpiece processing;
step S220, reading a machine selection part of workpiece processing, and converting the machine selection part into a machine sequence matrix, a processing time matrix and a setting time matrix;
and step S230, reading the process arrangement part of the workpiece processing, and obtaining the processing machine, the processing time and the setting time corresponding to each workpiece process according to the process arrangement part, the machine sequence matrix, the processing time matrix and the setting time matrix.
In one embodiment, the flexible job shop scheduling method based on the hybrid evolution algorithm comprises the following steps: constructing a flexible job shop scheduling model based on multi-time constraint of setting time and processing time, and setting constraint conditions; performing segmented integer encoding and decoding operations on a machine selection part and a process arrangement part of the workpiece processing; initializing the population of the hybrid evolution algorithm to obtain a parent population; performing cross operation and mutation operation 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 reservation operation on the first population and the second population to obtain an elite population, and judging whether a hybrid evolution algorithm meets an iteration termination condition; and when the hybrid evolution algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite group, and scheduling the flexible job shop.
Performing segmented integer encoding and decoding operations on a machine selection part and a process sequence arrangement part of a workpiece process, comprising the steps of:
carrying out sectional integer coding operation on a machine selection part and a process arrangement part of workpiece processing;
reading a 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;
and reading the process arrangement part for workpiece processing, and obtaining the processing machine, the processing time and the setting time corresponding to each workpiece process according to the process arrangement part, the machine sequence matrix, the processing time matrix and the setting time matrix.
In one embodiment, the flexible job shop scheduling method based on the hybrid evolution algorithm comprises the following steps: constructing a flexible job shop scheduling model based on multi-time constraint of setting time and processing time, and setting constraint conditions; performing segmented integer encoding and decoding operations on a machine selection part and a process arrangement part of the workpiece processing; initializing the population of the hybrid evolution algorithm to obtain a parent population; performing cross operation and mutation operation 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 reservation operation on the first population and the second population to obtain an elite population, and judging whether the mixed evolution algorithm meets an iteration termination condition; and when the hybrid evolution algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite group, and scheduling the flexible job shop. Initializing the population of the hybrid evolution algorithm to obtain a parent population, comprising the following steps of: and setting the population size, the iteration times, the cross probability and the variation probability of the hybrid evolution algorithm.
In one embodiment, the flexible job shop scheduling method based on the hybrid evolution algorithm comprises the following steps: constructing a flexible job shop scheduling model based on multi-time constraint of setting time and processing time, and setting constraint conditions; performing segmented integer encoding and decoding operations on a machine selection part and a process arrangement part of the workpiece processing; initializing the population of the hybrid evolution algorithm to obtain a parent population; performing cross operation and mutation operation 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 reservation operation on the first population and the second population to obtain an elite population, and judging whether a hybrid evolution algorithm meets an iteration termination condition; and when the hybrid evolution algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite group, and scheduling the flexible job shop. Initializing the population of the hybrid evolution algorithm to obtain a parent population, comprising the following steps of: and setting the population size, the iteration times, the cross probability and the variation probability of the hybrid evolution algorithm.
Performing cross operation on the parent population based on the neighborhood, comprising the following steps:
sequentially selecting two chromosomes from the parent population as a first parent and a second parent according to the cross probability;
intercepting machine selection part fragments of the first parent and the second parent, and selecting parts of the first parent and the second parent which need to be crossed;
performing cross operation on the part needing to be crossed to obtain a first filial generation and a second filial generation;
the machine selection part segment of the first parent is replaced by the first child, and the machine selection part segment of the second parent is replaced by the second child.
In one embodiment, the flexible job shop scheduling method based on the hybrid evolution algorithm comprises the following steps: constructing a flexible job shop scheduling model based on multi-time constraint of setting time and processing time, and setting constraint conditions; performing segmented integer encoding and decoding operations on a machine selection part and a process arrangement part of the workpiece processing; initializing the population of the hybrid evolution algorithm to obtain a parent population; performing cross operation and mutation operation 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 reservation operation on the first population and the second population to obtain an elite population, and judging whether a hybrid evolution algorithm meets an iteration termination condition; and when the hybrid evolution algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite group, and scheduling the flexible job shop. Initializing the population of the hybrid evolution algorithm to obtain a parent population, comprising the following steps of: and setting the population size, the iteration times, the cross probability and the mutation probability of the hybrid evolution algorithm.
Performing cross operation on the parent population based on the neighborhood, comprising the following steps:
sequentially selecting two chromosomes from the parent population as a first parent and a second parent according to the cross probability;
intercepting process arrangement partial fragments of the first parent and the second parent, and selecting parts of the first parent and the second parent which need to be crossed;
dividing a proper subset from the workpiece set, reserving genes contained in the proper subset in the first parent to the corresponding positions of the first child, and reserving genes contained in the proper subset in the second parent to the corresponding positions of the second child;
copying genes in the first parent which are not included in the proper subset to the second child, and copying genes in the second parent which are not included in the proper subset to the first child;
and replacing the process arrangement part segment of the first parent with a first child, and replacing the process arrangement part segment of the second parent with a second child.
In one embodiment, the flexible job shop scheduling method based on the hybrid evolution algorithm comprises the following steps: constructing a flexible job shop scheduling model based on multi-time constraint of setting time and processing time, and setting constraint conditions; performing segmented integer encoding and decoding operations on a machine selection part and a process arrangement part of the workpiece processing; initializing the population of the hybrid evolution algorithm to obtain a parent population; performing cross operation and mutation operation 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 reservation operation on the first population and the second population to obtain an elite population, and judging whether a hybrid evolution algorithm meets an iteration termination condition; and when the hybrid evolution algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite group, and scheduling the flexible job shop. Initializing the population of the hybrid evolution algorithm to obtain a parent population, comprising the following steps of: and setting the population size, the iteration times, the cross probability and the variation probability of the hybrid evolution algorithm.
Carrying out mutation operation on the parent population based on the neighborhood, comprising the following steps:
selecting a chromosome from the parent population as a parent chromosome according to the mutation probability;
randomly selecting a plurality of positions on a parent chromosome, wherein workpieces represented on the positions are different, and generating a neighborhood solution;
calculating the fitness values of all neighborhood solutions, and selecting an individual with the best fitness value as a child chromosome;
replacing the machine-selected partial segment of the parent chromosome with the process-aligned partial segment of the child chromosome.
In one embodiment, the flexible job shop scheduling method based on the hybrid evolution algorithm comprises the following steps: constructing a flexible job shop scheduling model based on multi-time constraint of setting time and processing time, and setting constraint conditions; performing segmented integer encoding and decoding operations on a machine selection part and a process arrangement part of the workpiece processing; initializing the population of the hybrid evolution algorithm to obtain a parent population; performing cross operation and mutation operation 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 reservation operation on the first population and the second population to obtain an elite population, and judging whether a hybrid evolution algorithm meets an iteration termination condition; and when the hybrid evolution algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite group, and scheduling the flexible job shop. Initializing the population of the hybrid evolution algorithm to obtain a parent population, comprising the following steps of: and setting the population size, the iteration times, the cross probability and the variation probability of the hybrid evolution algorithm.
And locally searching the parent population based on the neighborhood to obtain a second population, comprising the following steps of:
randomly selecting a plurality of positions on a machine selection part segment of an initial chromosome in a parent population, and changing the selected machine into a machine with the shortest processing time or a machine with the shortest setting time;
randomly selecting genes at multiple positions on the machine selection part segment of the initial chromosome in the parent population, and reordering the genes at the multiple positions.
In one embodiment, the flexible job shop scheduling method based on the hybrid evolution algorithm comprises the following steps:
the adopted coding mode is segmented integer coding. Segmentation coding is to process procedure information separately according to problem description, and for the classic FJSP problem, two sub-problems of procedure allocation and machine selection are generally divided. Therefore, a segmented integer coding strategy is adopted, and the segmented representation chromosome is divided into a plurality of parts, and genes on each part are different and carry different information. Considering that MTCFJSP is a sub-problem of allocation of more considered persons, this problem is considered in decoding, and in order to reduce the complexity of the chromosome, MTCFJSP problem chromosome coding is divided into two parts: a machine selection part (MS) and an operations sequencing part (OS), and has a total length of 2 × T0。T0Indicates the total number of work processes.
Namely, it is
The specific decoding process of the hybrid multi-objective evolutionary algorithm is as follows.
1. Decoding the machine selected portion, reading the MS segments sequentially from left to right and converting to a machine order matrix JMAnd processing the sameTime matrix T1Setting a time matrix T2. Wherein JM(i, J) denotes a workpiece JiStep (i) of (i) OijMachining the selected machine; t is1(i, j) represents a step OijThe time required for processing on the selected machine; t is a unit of2(i, j) represents a step OijIt sets the size of the time if it is needed on the selected machine. J. the design is a squareM(i，j)、T1(i, j) and T2The relationship between (i, j) is one-to-one.
2. The process arrangement part is decoded, and the OS fragments are read sequentially from left to right. The machine sequence matrix J corresponding to the step 1MTime matrix T of machining1Setting a time matrix T2Obtaining each work procedure O in turnijCorresponding processing machine MkMachining time TijkAnd set time Stijk。
Initializing the population, which comprises the following specific steps:
part OS. The process sequence part is generated in the same manner as the random initialization method.
MS part. The machine selection section determines each machine position by the process arrangement section based on the machine load.
When population initialization is completed, crossover operations are performed to generate new chromosomes. Because the algorithm adopts the sectional coding strategy, the sectional coding strategy causes different chromosome segments on the same chromosome to carry different genes, and if the different genes are crossed, the information carried by the chromosome is damaged, so different segments adopt different crossing modes, namely, the different segments of the chromosome are separated when crossed, and the different segments are spliced together to form the complete chromosome for heredity.
The MS part adopts two-point crossing, and the MS part crosses:
(1): sequentially selecting two chromosomes from the population according to the cross probability;
(2): intercepting MS segments of the two chromosomes, and selecting parts of the two chromosomes to be crossed;
(3): performing cross operation to obtain a new individual;
(4): the resulting child 1 and child 2 replace the MS portion in parent 1 and parent 2.
The OS part employs a POX crossover operator. Since the new chromosome generated by the process arrangement part may not conform to the actual chromosome if two-point crossing is used as in the MS part, for example, a certain workpiece process is missing or increased, a collision detection operation needs to be added, which results in a complicated crossing, and the POX crossing step is:
(1): sequentially selecting two chromosomes from the population according to the cross probability;
(2): intercepting OS fragments of the two chromosomes, and selecting parts of the two chromosomes to be crossed;
(3): dividing the workpiece set into a proper subset, and reserving genes contained in the proper subset in the parent 1 and the parent 2 to the corresponding positions of the child 1 and the child 2;
(4): copying genes which are not contained in the true subsets in the parent 2 and the parent 1 to the child 1 and the child 2 in sequence;
(5): child 1 and child 2 replace the portion of the OS in parent 1 and parent 2.
The mutation operator based on the neighborhood search comprises the following specific steps:
1: selecting a chromosome from the population according to the variation probability;
2: randomly selecting r positions on the selected chromosome, determining that workpieces represented on the r positions are different, and then generating a neighborhood solution of the workpieces;
3: calculating the fitness values of all neighborhood solutions, and selecting the optimal individual as a descendant;
4: the resulting child OS fragment replaces the parent MS fragment.
Since the overall search capability of the NSGA-II algorithm is strong but the local search capability is weak, the local search designed by the algorithm is performed after the crossover and mutation are completed, which is the expression of the algorithm for the hybrid algorithm. A specific local search consists of the neighborhood structure described below.
1. Randomly selecting 1-3 positions in the MS section of the initial chromosome, and then changing the selected machine into the machine with the shortest processing time;
2. in the MS section of the initial chromosome, 1-3 positions are randomly selected, and the selected machine is changed into the machine which starts to process the fastest, namely, the shortest machine time is set or the time which does not need to be set;
3. in the OS fragment of the initial chromosome, three positions were randomly selected, and the genes at the three positions were reordered.
When the population undergoes crossing, mutation and local search operations, a batch of new individuals is obtained, and the algorithm proceeds to another step, namely an elite retention operation. And (4) enabling the algorithm to iterate, and realizing the optimization solution of the algorithm in the continuous iteration process.
The invention also provides a flexible job shop scheduling system based on the hybrid evolution algorithm, which comprises the following components: the workshop scheduling model building module is used for building a flexible job workshop scheduling model based on multi-time constraint of setting time and processing time and setting constraint conditions; the encoding and decoding module is used for carrying out segmented integer encoding and decoding operations on a machine selection part and a process arrangement part of workpiece processing; the initialization module is used for initializing the population of the hybrid evolution algorithm to obtain a parent population; the population processing module is used for performing cross operation and mutation operation 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; the elite reservation operation module is used for performing elite reservation operation on the first population and the second population to obtain an elite population and judging whether the hybrid evolution algorithm meets an iteration termination condition; and the flexible job shop scheduling module is used for solving the flexible job shop scheduling model according to the elite group and scheduling the flexible job shop when the hybrid evolution algorithm meets the iteration termination condition.
The invention also provides a flexible job shop scheduling system based on the hybrid evolution algorithm, which comprises the following components: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the flexible job shop scheduling method based on the hybrid evolution algorithm.
The processor and memory may be connected by a bus or other means.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
It should be noted that the flexible job shop scheduling system based on the hybrid evolution algorithm in this embodiment may include a service processing module, an edge database, a server version information register, and a data synchronization module, and when the processor executes a computer program, the hybrid evolution algorithm-based flexible job shop scheduling method applied to the flexible job shop scheduling system based on the hybrid evolution algorithm is implemented.
The above described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
Furthermore, an embodiment of the present invention also provides a computer-readable storage medium, which stores computer-executable instructions, which are executed by a processor or a controller, for example, by a processor in the terminal embodiment, and can make the processor execute the flexible job shop scheduling method based on the hybrid evolution algorithm in the embodiment.
One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, 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 includes any information delivery media as known to those skilled in the art.
While the preferred embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that the foregoing and various other changes, omissions and deviations in the form and detail thereof may be made without departing from the scope of this invention.
The above-described embodiments of the present invention should not be construed as limiting the scope of the present invention. Any other corresponding changes and modifications made according to the technical idea of the present invention should be included in the protection scope of the claims of the present invention.
Claims (10)
1. A flexible job shop scheduling method based on a hybrid evolution algorithm is characterized by comprising the following steps:
constructing a multi-time constraint flexible job shop scheduling model based on the set time and the processing time, and setting constraint conditions;
performing segmented integer encoding and decoding operations on a machine selection part and a process arrangement part of the workpiece processing;
initializing the population of the hybrid evolution algorithm to obtain a parent population;
performing cross operation and mutation operation 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 reservation operation on the first population and the second population to obtain an elite population, and judging whether the hybrid evolution algorithm meets an iteration termination condition;
and when the hybrid evolution algorithm meets the iteration termination condition, solving the flexible job shop scheduling model according to the elite group, and scheduling the flexible job shop.
2. The flexible job shop scheduling method based on the hybrid evolution algorithm according to claim 1, wherein the step of judging whether the hybrid evolution algorithm meets the iteration termination condition comprises the steps of:
and calculating the fitness of the elite population, and judging whether the hybrid evolution algorithm meets the iteration termination condition according to the fitness of the elite population.
3. The method for scheduling the flexible job shop based on the hybrid evolution algorithm according to claim 1, wherein the optimization objective function of the flexible job shop scheduling model is as follows:
wherein CiFinishing time for the workpiece, i: an index of the workpiece;
the minimum total setup time function is:
i＝1,2,K,n；j＝1,2,K,ni；s＝1,2,K,w
PTt thereinijsFinish time for workpiece, j: index of procedure s: the index of the operator.
4. The hybrid evolution algorithm-based flexible job shop scheduling method according to claim 1, wherein the segmented integer coding and decoding operation is performed on the machine selection part and the process arrangement part of the workpiece processing, comprising the steps of:
carrying out sectional integer coding operation on a machine selection part and a 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 setting time matrix;
and reading a process arrangement part for workpiece processing, and obtaining a processing machine, processing time and setting time corresponding to each workpiece process according to the process arrangement part, the machine sequence matrix, the processing time matrix and the setting time matrix.
5. The flexible job shop scheduling method based on the hybrid evolution algorithm according to claim 1, wherein the initializing the population of the hybrid evolution algorithm to obtain a parent population comprises the following steps:
and setting the population size, the iteration times, the cross probability and the mutation probability of the hybrid evolution algorithm.
6. The flexible job shop scheduling method based on the hybrid evolution algorithm according to claim 5, wherein the parent population is subjected to cross operation based on the neighborhood, and the method comprises the following steps:
sequentially selecting two chromosomes from the parent population as a first parent and a second parent according to the cross probability;
intercepting machine selection part segments of the first parent and the second parent, and selecting parts of the first parent and the second parent which need to be crossed;
performing cross operation on the part needing to be crossed to obtain a first filial generation and a second filial generation;
replacing the machine selection part segment of the first parent with the first child, and replacing the machine selection part segment of the second parent with the second child.
7. The flexible job shop scheduling method based on the hybrid evolution algorithm according to claim 5, wherein the parent population is subjected to cross operation based on the neighborhood, and the method comprises the following steps:
sequentially selecting two chromosomes from the parent population as a first parent and a second parent according to the cross probability;
intercepting process arrangement partial fragments of the first parent and the second parent, and selecting a part of the first parent and the second parent which needs to be crossed;
dividing a proper subset from the workpiece set, reserving genes contained in the proper subset in the first parent to corresponding positions of first filial generation, and reserving genes contained in the proper subset in the second parent to corresponding positions of second filial generation;
copying genes in the first parent that are not included in the proper subset to the second child, copying genes in the second parent that are not included in the proper subset to the first child;
replacing the process arrangement partial segment of the first parent with the first child, and replacing the process arrangement partial segment of the second parent with the second child.
8. The flexible job shop scheduling method based on the hybrid evolution algorithm according to claim 5, wherein the parent population is subjected to mutation operation based on the neighborhood, and the method comprises the following steps:
selecting a chromosome from the parent population as a parent chromosome according to the mutation probability;
randomly selecting a plurality of positions on the parent chromosome, wherein the workpieces represented on the positions are different, and generating a neighborhood solution;
calculating the fitness values of all the neighborhood solutions, and selecting the individual with the best fitness value as a child chromosome;
replacing the machine-selected partial segment of the parent chromosome with the process arrangement partial segment of the child chromosome.
9. The flexible job shop scheduling method based on the hybrid evolution algorithm according to claim 5, wherein the parent population is locally searched based on a neighborhood to obtain a second population, comprising the following steps:
randomly selecting a plurality of positions on a machine selection part segment of an initial chromosome in the parent population, and changing the selected machine into a machine with the shortest processing time or a machine with the shortest setting time;
randomly selecting genes at a plurality of positions on the machine-selected partial segment of the initial chromosome in the parent population, and reordering the genes at the plurality of positions.
10. A flexible job shop scheduling system based on a hybrid evolution algorithm is characterized by comprising:
the workshop scheduling model building module is used for building a flexible job workshop scheduling model based on multi-time constraint of setting time and processing time and setting constraint conditions;
the encoding and decoding module is used for carrying out sectional integer encoding and decoding operation on a machine selection part and a process arrangement part of workpiece processing;
the initialization module is used for initializing the population of the hybrid evolution algorithm to obtain a parent population;
the population processing module is used for performing cross operation and mutation operation 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;
the elite reservation operation module is used for performing elite reservation operation on the first population and the second population to obtain an elite population and judging whether the hybrid evolution algorithm meets an iteration termination condition;
and the flexible job shop scheduling module is used for solving the flexible job shop scheduling model according to the elite group and scheduling the flexible job shop when the hybrid evolution algorithm meets the iteration termination condition.
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