Optimization method for job shop scheduling
By proposing an optimization method in the operation workshop scheduling, including initial coding, establishing machine working schedule, finding the machine that ends the latest processing, recording the machine idle time, selecting the idle time according to the idle time schedule, advanced processing process, and updating the time schedule, combining the championship method, elite retention strategy, improving the POX cross operator and the mutant operator based on neighborhood optimization, the problem of low search efficiency of existing algorithms is solved, and more efficient operation workshop scheduling and shortening of manufacturing cycles is achieved.
Patent Information
- Application Number
- CN202411661396.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-20
- Publication Date
- 2025-04-18
AI Technical Summary
The existing algorithms have low search efficiency in the operation workshop scheduling, resulting in slow convergence speed. As the problem dimension increases, the search space increases exponentially, resulting in a sharp decline in the algorithm's search efficiency and is prone to falling into local optimality.
An optimization method for operation workshop scheduling is proposed, including initial coding, establishing a machine working schedule, finding the machine that ends the latest processing, recording the machine idle time, selecting the idle time according to the idle time schedule, pre-processing process, and updating the time schedule. At the same time, the championship method, elite retention strategy, improved POX crossover operators and neighborhood optimization-based mutant operators are adopted to improve the convergence and efficiency of the algorithm.
Through optimization methods, under the constraints of existing resources, we can shorten the manufacturing cycle, improve economic benefits, reduce invalid genetics, and improve the performance and convergence capabilities of the algorithm.
Smart Images
Figure CN119849784A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of workshop scheduling, and in particular to an optimization method for job shop scheduling. Background Art
[0002] The job shop scheduling problem is the most classic job shop scheduling problem, which is widely present in the manufacturing process. In existing industrial production, how to reasonably arrange the order of workpiece processing to minimize the completion time is the key to the single-objective scheduling problem. In existing intelligent evolutionary algorithms, due to the randomness of the search, the algorithm has shortcomings such as slow convergence speed, and as the dimension of the problem increases, the search space grows exponentially, resulting in a sharp drop in the algorithm's search efficiency. With the increase in the number of iterations, some search operations may cause local areas of the search space to be over-explored, which can easily cause the algorithm to fall into a local optimum. Summary of the invention
[0003] In view of this, the purpose of the present invention is to propose an optimization method for job shop scheduling to solve the problem of low search efficiency of existing algorithms.
[0004] Based on the above purpose, the present invention provides an optimization method for job shop scheduling, comprising:
[0005] S1, initialization coding, obtaining the initial population;
[0006] S2, establish machine work schedule;
[0007] S3, respectively obtain the processing time and machine number of each process, arrange the process on the corresponding machine, and update the machine work schedule. Find the machine that finishes processing the latest in the machine work schedule, and set this time as the final completion time of the scheduling;
[0008] S4, according to the machine work schedule established in the decoding process, record the idle time on each machine and establish a machine idle schedule;
[0009] S5, select an idle time according to the idle time table sequence, find the corresponding machine, and search for subsequent processes from the idle time in sequence. If there is a subsequent process that satisfies any of the following two conditions,
[0010] (1) a≤b and c≥d;
[0011] (2) a>b and c-(ab)≥d;
[0012] Among them: a is the completion time of the previous process of the workpiece, b is the start time of the idle time, c is the idle time, and d is the processing time of the current process of the workpiece;
[0013] Advance the process to the idle time period, update the machine work schedule and machine idle schedule, otherwise proceed to the next idle time search;
[0014] In this way, a job shop scheduling method is obtained.
[0015] As a further improvement of the present application, the initialization coding and obtaining the initial population include establishing a coding string of a feasible solution, [J 1,1 ,J 1,2 ,...,J 1,m ,J 2,1 ,J 2,2 ,...,J 2,m ,...,J n,1 ,J n,2 ,...,J n,m ],
[0016] Among them J 1,1 Indicates the first process of workpiece 1, J n,m represents the mth process of workpiece n,
[0017] The encoding string of feasible solutions is called a chromosome.
[0018] The chromosomes are randomly shuffled using a random method to obtain an initial population.
[0019] As a further improvement of the present application, the establishment of a machine work schedule includes importing the chromosome, determining the number of machines, the number of workpieces, the number of processes for each workpiece, and the processing time of each process, and then establishing a machine work schedule.
[0020] As a further improvement of this application,
[0021] The job shop scheduling method also includes improving the convergence of the algorithm, including
[0022] The tournament method is used for selection and combined with the elite retention strategy to select outstanding individuals from the parent population to enter the daughter population. The initial population is the first-generation parent population.
[0023] As a further improvement of the present application, the job shop scheduling method also includes a crossover operation, by improving the POX crossover operator, inheriting the high-quality genes of the parent generation, and the offspring population size reaches a preset value, thereby obtaining a target offspring population after crossover improvement.
[0024] As a further improvement of the present application, the job shop scheduling method also includes a mutation operation, which guides the individual progress of the improved target offspring population through a mutation operator based on neighborhood optimization to obtain the target offspring population after mutation crossover improvement.
[0025] Beneficial effects of the invention: The present application provides an optimization method for job shop scheduling that can optimize the production process through an intelligent algorithm under existing resource constraints, thereby achieving the purpose of shortening the manufacturing cycle and improving economic benefits.
[0026] This application also adds the search and utilization of machine idle time. The improved crossover operation can better inherit the excellent genes of the parent generation. A new mutation method and a search strategy based on a dynamic gene library are designed to reduce invalid inheritance and improve the performance of the algorithm. The selection operation and elite retention strategy are combined to further increase the convergence ability of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS
[0027] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0028] Figure 1 A scheduling Gantt chart of an optimization method for job shop scheduling according to an embodiment of the present invention;
[0029] Figure 2 A diagram of finding and utilizing idle time in an optimization method for job shop scheduling according to an embodiment of the present invention;
[0030] Figure 3 This is a comparison diagram of convergence curves of different crossover methods in a test of an optimization method for job shop scheduling according to an embodiment of the present invention;
[0031] Figure 4 A comparison diagram of convergence curves of different variation methods of an optimization method for job shop scheduling according to an embodiment of the present invention;
[0032] Figure 5 A comparison is made between an optimization method for job shop scheduling according to an embodiment of the present invention and the optimal solutions obtained by other algorithms. DETAILED DESCRIPTION
[0033] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.
[0034] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.
[0035] like Figure 1-5 As shown, an optimization method for job shop scheduling includes:
[0036] S1, initialization coding, obtain the initial population, including
[0037] Establish the encoding string of feasible solution, [J 1,1 ,J 1,2 ,...,J 1,m ,J 2,1 ,J 2,2 ,...,J 2,m ,...,J n,1 ,J n,2 ,...,J n,m ],
[0038] Among them J 1,1 Indicates the first process of workpiece 1, J n,m represents the mth process of workpiece n,
[0039] The encoding string of feasible solutions is called a chromosome.
[0040] The chromosomes are randomly shuffled using a random method to obtain an initial population.
[0041] S2, establishing a machine work schedule, including importing the chromosome, determining the number of machines, the number of workpieces, the number of processes for each workpiece and the processing time of each process, and then establishing a machine work schedule.
[0042] S3, respectively obtains the processing time and machine number of each process, arranges the process on the corresponding machine, and updates the machine work schedule. In the machine work schedule, finds the machine that finishes processing the latest and sets this time as the final completion time of the schedule.
[0043] S4, according to the machine work schedule established in the decoding process, record the idle time on each machine and establish a machine idle schedule;
[0044] S5, select an idle time according to the idle time table sequence, find the corresponding machine, and search for subsequent processes from the idle time in sequence. If there is a subsequent process that satisfies any of the following two conditions,
[0045] (1) a≤b and c≥d;
[0046] (2) a>b and c-(ab)≥d;
[0047] Among them: a is the completion time of the previous process of the workpiece, b is the start time of the idle time, c is the idle time, and d is the processing time of the current process of the workpiece;
[0048] Advance the process to the idle time period, update the machine work schedule and machine idle schedule, otherwise proceed to the next idle time search;
[0049] In this way, a job shop scheduling method is obtained.
[0050] The job shop scheduling method also includes improving the convergence of the algorithm, including
[0051] The tournament method is used for selection and combined with the elite retention strategy to select outstanding individuals from the parent population to enter the daughter population. The initial population is the first-generation parent population.
[0052] Specifically, the individual with the shortest completion time in the previous generation population is recorded, and the individual with the shortest completion time is selected from the current generation population. The two are compared. If the best individual in this generation population is not as good as the previous generation, the best individual of the previous generation is directly moved into the offspring population. Otherwise, the best individual in this generation population is moved into the offspring population.
[0053] Randomly select two individuals from the parent population and compare their completion times. The individual with the shorter completion time will be moved into the next generation population. Repeat the above steps until the population size reaches the preset value.
[0054] The job shop scheduling method also includes a crossover operation, which is to improve the POX crossover operator, inherit the high-quality genes of the parent generation, and achieve a preset size of the offspring population, thereby obtaining a target offspring population after crossover improvement.
[0055] Specific:
[0056] Step 1: Set the crossover probability, randomly select two individuals a and b from the parent generation, and use random numbers to determine whether to perform a crossover operation. If the result is true, go to step 2, otherwise go to step 5.
[0057] Step 2: Randomly select the number of divided workpieces as i (1<i<n), randomly divide the number of i workpieces and store them in the set α, and store the undivided workpieces in the set β.
[0058] Step 3: Record the processing order of the artifacts contained in α in the parent individuals a and b, maintain the order of the gene segments, and copy them to the corresponding positions of the offspring a' and b' respectively.
[0059] Step 4: Record the processing order of the artifacts contained in β in the parent individuals a and b, keep the order of the gene segments, and copy them to the corresponding positions of the offspring b' and a' respectively. Go to step 6.
[0060] Step 5: Keep the gene order of a and b unchanged, and copy them to the corresponding positions of the offspring a' and b' respectively. Go to step 6.
[0061] Step 6: Repeat steps 1 to 5 until the offspring population size reaches the preset value.
[0062] The job shop scheduling method also includes a mutation operation, which guides the individual progress of the improved target offspring population through a mutation operator based on neighborhood optimization to obtain the target offspring population after mutation crossover improvement.
[0063] Specific:
[0064] Step 11: Set the mutation probability and use random numbers to determine whether to perform a mutation operation. If the result is true, proceed to step 12, otherwise proceed to the next individual judgment.
[0065] Step 12: Randomly select a gene position and form a neighborhood with this position as the center. The length of the neighborhood should be determined according to the length of the chromosome. 10% of the chromosome length is initially selected as the neighborhood length. The final neighborhood length will be determined through Taguchi experiment in later experiments.
[0066] Step 13: Shuffle the gene order in the neighborhood to form a new chromosome, and perform decoding operations.
[0067] Step 14: If the new chromosome takes less time to complete, it will replace the original chromosome. Otherwise, the Monte Carlo criterion is used to determine whether to accept the new chromosome.
[0068] Step 15: Repeat steps 11 to 14 until all the individuals have completed the operation.
[0069] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present invention (including the claims) is limited to these examples. Under the concept of the present invention, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity.
[0070] The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.
Claims
1. A method for optimizing job shop scheduling, characterized in that: include S1, initialization coding, obtaining the initial population; S2, establish machine work schedule; S3, respectively obtain the processing time and machine number of each process, arrange the process on the corresponding machine, and update the machine work schedule. Find the machine that finishes processing the latest in the machine work schedule, and set this time as the final completion time of the scheduling; S4, according to the machine work schedule established in the decoding process, record the idle time on each machine and establish a machine idle schedule; S5: Select an idle time according to the idle time table order, find the corresponding machine, and search for subsequent processes from the idle time in sequence. If there is a subsequent process that satisfies any of the following two conditions, (1) a≤b and c≥d; (2) a>b and c-(ab)≥d; Among them: a is the completion time of the previous process of the workpiece, b is the start time of the idle time, c is the idle time, and d is the processing time of the current process of the workpiece; Advance the process to the idle time period, update the machine work schedule and machine idle schedule, otherwise proceed to the next idle time search; In this way, a job shop scheduling method is obtained.
2. The optimization method for job shop scheduling according to claim 1, characterized in that: The initialization code obtains the initial population, including Establish the encoding string of feasible solution, [J 1,1 ,J 1,2 ,...,J 1,m ,J 2,1 ,J 2,2 ,...,J 2,m ,...,J n,1 ,J n,2 ,...,J n,m ], Among them J 1,1 Indicates the first process of workpiece 1, J n,m represents the mth process of workpiece n, The encoding string of feasible solutions is called a chromosome. The chromosomes are randomly shuffled using a random method to obtain an initial population.
3. The optimization method for job shop scheduling according to claim 2, characterized in that: The establishing of the machine work schedule includes importing the chromosome, determining the number of machines, the number of workpieces, the number of processes for each workpiece and the processing time of each process, and then establishing the machine work schedule.
4. The optimization method for job shop scheduling according to claim 2, characterized in that: The job shop scheduling method also includes improving the convergence of the algorithm, including The tournament method is used for selection and combined with the elite retention strategy to select outstanding individuals from the parent population to enter the daughter population. The initial population is the first-generation parent population.
5. The optimization method for job shop scheduling according to claim 4, characterized in that: The job shop scheduling method also includes a crossover operation, which is to improve the POX crossover operator, inherit the high-quality genes of the parent generation, and achieve a preset size of the offspring population, thereby obtaining a target offspring population after crossover improvement.
6. The optimization method for job shop scheduling according to claim 5, characterized in that: The job shop scheduling method also includes a mutation operation, which guides the individual progress of the improved target offspring population through a mutation operator based on neighborhood optimization to obtain the target offspring population after mutation crossover improvement.