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Self-crossover genetic algorithm for solving flexible job-shop scheduling problem

A flexible operation and workshop scheduling technology, applied in control/regulation systems, instruments, comprehensive factory control, etc., can solve problems such as infeasible solutions, complex evolution process of genetic algorithm, etc., and achieve the effect of maintaining diversity

Inactive Publication Date: 2017-05-03
SICHUAN YONGLIAN INFORMATION TECH CO LTD
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Problems solved by technology

[0005] The goal of this algorithm is: first. Solve the problem of infeasible solutions in the process of genetic algorithm crossover and mutation; second. Solve the problem that the evolution process of genetic algorithm is becoming more and more complicated

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  • Self-crossover genetic algorithm for solving flexible job-shop scheduling problem
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Embodiment Construction

[0025] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention and not to limit the present invention.

[0026] The invention aims at the deficiencies of the current genetic algorithm in solving the problem of flexible job shop scheduling, improves its encoding mode, crossover and mutation, optimizes the performance of the genetic algorithm, and can be better applied to actual job shop scheduling.

[0027] Further describe this invention below in conjunction with accompanying drawing.

[0028] Flexible Job Shop Scheduling Problem Combination figure 2 An example of a flexible job shop scheduling problem with 3 workpieces and 3 equipment ("-" in the figure indicates that the equipment cannot proces...

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Abstract

The invention provides a self-crossover genetic algorithm for solving a flexible job-shop scheduling problem. The algorithm relates to the field of job-shop scheduling, and particularly relates to the field of flexible job-shop scheduling. The existing genetic algorithms are mostly amphilepsis, the coding mode is complex, crossover and variation are caused to be complex, and a non-feasible solution is easy to acquire. The invention provides monolepsis-based self-crossover whose coding, crossover and variation are performed on a uniparental chromosome. The coding uniparental chromosome is divided into a working procedure portion and an equipment portion, wherein the working procedure portion is coded based on the workpiece number, and the equipment portion represents selected equipment by using the probability. Self-crossover is performed on the working procedure portion, and the equipment portion also performs the same crossover transform along with the working procedure portion. Two types of variation operators are adopted, exchange type variation is adopted for the working procedure portion, and insertion type variation is adopted for the equipment portion. The self-crossover genetic algorithm provided by the invention has the characteristics of high practicability and wide application range.

Description

[0001] Technical field [0002] The invention relates to the field of job shop scheduling, in particular to the field of flexible job shop scheduling. Background technique [0003] Flexible job shop scheduling is an extension of the job shop scheduling problem. Different from the job shop scheduling problem, flexible job shop scheduling can have a group of different equipment for processing a process. In addition to solving the problem of the processing sequence of the process, the flexible job shop scheduling also needs to choose the appropriate equipment for each process. In order to solve the problem of flexible job shop scheduling, many algorithms have been proposed, such as genetic algorithm. In order to improve the performance of genetic algorithm, researchers have successively proposed adaptive genetic algorithm and hybrid genetic algorithm. Adaptive genetic algorithm population size, crossover mutation probability and other genetic parameters are dynamically changing...

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Application Information

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IPC IPC(8): G05B19/418
CPCG05B19/41865G05B2219/32252
Inventor 龚晓慧胡成华
Owner SICHUAN YONGLIAN INFORMATION TECH CO LTD
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