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Job Shop Scheduling Method with Cache Constraints Based on Improved Genetic Algorithm

An improved genetic algorithm and job shop technology, applied in the field of job shop scheduling with cache constraints, can solve problems such as few researches, and achieve the effect of improving efficiency and convergence ability

Active Publication Date: 2021-06-25
SOUTHWEST JIAOTONG UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0003] At present, there has been little research on the solution method of the job shop scheduling problem with cache constraints, and the solution idea is to focus on the coarse-grained workpiece level. At the same time, because the complexity of the problem exceeds the traditional job shop scheduling problem, heuristics are basically used. Algorithms solve problems

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  • Job Shop Scheduling Method with Cache Constraints Based on Improved Genetic Algorithm
  • Job Shop Scheduling Method with Cache Constraints Based on Improved Genetic Algorithm
  • Job Shop Scheduling Method with Cache Constraints Based on Improved Genetic Algorithm

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Embodiment

[0117] The present invention selects a standard job shop benchmark example to test the performance of the algorithm. Based on the standard calculation examples La01~La20, the percentage of the machine cache capacity and the number of processed workpieces is constructed. For example, the scale of the calculation example La01 is 10x5, indicating that the number of workpieces is 10 and the number of machines is 5; the percentage of the machine cache capacity and the number of processed workpieces is 10 %, it means that the cache capacity of each machine is 1.

[0118] (1) Experimental parameter settings

[0119] The initial population size, cross-mutation probability, and number of iterations of the improved genetic algorithm will all affect the convergence of the algorithm. In this paper, the population size is set at 50-200, and the number of iterations is 50-200 for multiple experimental comparisons. When the population size PopSize=100 , The convergence of the algorithm is b...

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Abstract

The invention discloses a job shop scheduling method with cache constraints based on an improved genetic algorithm. Specifically, firstly, a mathematical model of job shop scheduling with cache constraints is established; then, the improved genetic algorithm is used to optimize the solution, and the traditional The double-layer coding of the job shop scheduling process and machine, and a reasonable process adjustment method are designed for decoding; the algorithm is improved by using the adaptive crossover mutation probability combined with the improved crossover operator, and the solution model is optimized. The invention can obtain a solution with higher precision under the same cache capacity, and improve the efficiency and convergence ability of the algorithm.

Description

technical field [0001] The invention belongs to the field of job shop scheduling, in particular to a job shop scheduling method with cache constraints based on an improved genetic algorithm. Background technique [0002] The job shop scheduling problem is one of the most common scheduling problems, and many scholars have conducted in-depth and extensive research in this field for a long time. In the current research, when constructing the scheduling model, the cache capacity of the machine is generally set to be infinite, and the impact of the cache capacity on job scheduling is ignored. In the actual production process, this assumption leads to the inapplicability of many job shop mathematical models. Inter-machine buffering can hold product parts after processing an equipment unit or supply the next equipment unit in an adjacent process step, so in-production buffering is critical to mitigate sudden changes in the production line. However, in the pursuit of "zero invento...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/20G06Q10/06G06N3/12
CPCG06F30/20G06Q10/0631G06N3/126G06Q10/06G06N5/01G06F9/5027
Inventor 张剑江磊罗焕胡明珠郑婷娟
Owner SOUTHWEST JIAOTONG UNIV
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