Collaborative scheduling method of high-end equipment manufacturing process, based on hybrid differential genetic algorithm

Abstract

The invention provides a collaborative scheduling method of high-end equipment manufacturing process, based on a hybrid differential genetic algorithm. The collaborative scheduling method includes thesteps: obtaining hybrid differential evolution and genetic algorithm parameters; defining Q chromosomes, wherein each chromosome includes a plurality of genes to initialize a population vector; performing code correction operation based on the Q chromosomes; according to the corrected gene, arranging workpieces to each manufacturer for production processing, and calculating the manufacturing spanof each scheme; screening the chromosome with the smallest fitness value from the Q chromosomes, and obtaining the current global optimal solution gbest; updating the population vector by using Bernoulli crossover operation; and updating the population vector by means of an immigration strategy until the global optimal solution gbest is output as the optimal height scheme. The collaborative scheduling method of high-end equipment manufacturing process, based on a hybrid differential genetic algorithm can successfully solve the collaborative scheduling problem of production and transportationin the distributed manufacturing process, and is beneficial to improving the overall efficiency and service level of the production system.

Description

technical field [0001] The invention relates to the technical field of high-end equipment-oriented production scheduling, in particular to a method for collaborative scheduling of high-end equipment manufacturing processes based on a hybrid differential genetic algorithm. Background technique [0002] Distributed production for high-end equipment is a decentralized production method that is different from traditional manufacturing methods. The production process is usually completed by different manufacturers distributed in different regions. Therefore, in the entire production process, different manufacturers The coordinated scheduling between different production links that the supplier is responsible for will have an important impact on improving the overall production efficiency, saving production, transportation and storage costs, optimizing product quality, and so on. However, most of the existing research and solutions focus on the optimization of a single manufacture...

AI Technical Summary

Problems solved by technology

However, most of the existing research and solutions focus on the optimization of a single manufacturer or production link in the production process. In a distributed production environment, each production link is subject to regional distribution, time-consuming production, transportation costs, etc. Due to the influence of multiple factors, it is difficult for existing solutions to obtain the optimal solution for coordinated scheduling within a limited time

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