A Job Scheduling Method for Workshop Production Based on Clustering Niche Genetic Algorithm

Abstract

The invention discloses a production operation scheduling method based on a clustering ecological niche genetic algorithm, and the method comprises the following steps: S1, building a multi-objectivefunction, proposing a multi-constraint condition, and building a production operation scheduling optimization mathematic model; S2, performing weighting processing on the multi-objective function based on a particle swarm weight optimization method, and converting the multi-objective model into a problem of a single-objective function; S3, dividing the population into K clusters according to a K-means mean clustering algorithm, and determining a clustering center; S4, performing selection, self-adaptive crossover, self-adaptive variation and niche elimination operation; and S5, judging whethera termination condition is met or not to obtain a final production scheduling scheme. The method aims at solving the problems that existing multiple targets in scheduling are difficult to solve and prone to falling into a local optimal solution. According to the scheduling method based on the clustering niche genetic algorithm, the three processes of determining the weight of the multi-objectivefunction, the radius of the niche and crossover and mutation operators are improved, the production scheduling capacity is effectively and remarkably improved, and the production cost is effectively saved.

Description

technical field [0001] The invention relates to the technical field of workshop production operation scheduling, in particular to a workshop production operation scheduling method based on a clustering niche genetic algorithm. Background technique [0002] With the successive proposals of "German Industry 4.0", "American Industrial Internet" and "Made in China 2025", the transformation from traditional manufacturing to intelligent manufacturing is being promoted. How to reduce costs and improve product quality while ensuring processing time requirements and product quality has recently become a research hotspot. Applying intelligent optimization technology to study more effective, scientific and convenient job scheduling methods is to change the current job scheduling optimization problem. [0003] In recent years, with the increasing demand of customers for product personalization and diversification, the pace of product upgrading is also getting faster and faster. The tr...

AI Technical Summary

Problems solved by technology

As the demand of the factory gradually increases, new problems arise in the traditional intelligent optimization algorithm: not only the production cost, but also the equipment consumption cost and processing time must be considered at the same time. Optimum production scheduling plan and satisfying complex constraints have become the focus of new issues

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