Operation workshop scheduling modeling method based on genetic algorithm

Abstract

The invention discloses an operation workshop scheduling modeling method based on a genetic algorithm. The method comprises the steps of JSP genetic algorithm design of reverse cross of a stored gene segment, eM-Plant simulation modeling, data collection, improvement of mutation operator and obtaining of an optimized scheme; the JSP genetic algorithm design of the reverse cross of the stored gene segment comprises the steps of randomly generating an initial group according to a sequence code, calculating the fitness of the initial group, judging whether the cycling times is satisfied, outputting an optimal result and program running time if the cycling times is satisfied, drawing an algorithm performance trace diagram, drawing an optimal scheduling trace diagram, selecting through a roulette wheel if the cycling times cannot be satisfied, reversely crossing the stored gene segment, randomly mutating the gene segment, calculating the fitness of a novel population, re-inserting a filial-generation population to the parental population, and recording the performance of the optimal result trace algorithm. By adopting the method, the running of the production workshop can be optimized and coordinated, the design effect is good, the process is simple, and the production danger and production cost can be reduced.

Description

technical field [0001] The invention belongs to the technical field of job shop scheduling modeling, in particular to a genetic algorithm-based job shop scheduling modeling method. Background technique [0002] The methods of job shop scheduling problem mainly include: mathematical programming method, artificial intelligence technology, artificial neural network technology, domain search technology (including simulated annealing method, genetic algorithm and tabu search, etc.), rule scheduling and other methods. However, as pointed out by Professor Reha Uzsoy, an expert in the field of workshop scheduling, the field of scheduling is currently divided into two camps, one is the theoretical camp, which is very interested in theory; the other is the practical camp, which believes that articles from the theoretical camp are worthless and focus on research practicality. Among the various methods mentioned above, the actual application is mostly rule-based scheduling or knowledge...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the embodiment of the present invention is to provide a genetic algorithm-based job shop scheduling modeling method, which aims to solve the problems of poor design effect, complicated process, high risk and high cost in traditional job shop scheduling modeling

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