Path optimization method for material distribution of multi-objective discrete assembly workshop

Abstract

The invention discloses a path optimization method for material distribution of a multi-objective discrete assembly workshop. The method comprises the following steps: taking distribution equipment transportation time consumption, distribution service satisfaction and distribution equipment load as problem optimization objectives, and carrying out overall modeling on a discrete assembly workshop material distribution path optimization process; encoding the material distribution paths of the N distribution devices with the load constraints, and randomly generating a material distribution path initial solution of the target model; performing local search on the neighborhood space of the initial solution by using a probability-based multi-neighborhood random local search method, and solving amaterial distribution path optimal solution of the target model; carrying out tabu operation on the search process by utilizing a tabu table, and recording a searched neighborhood space structure; and repeating the steps until a search termination condition is met, and outputting an optimal workshop material distribution path scheme. The method is short in operation time in a target problem optimization process, and can deal with a problem scene with real-time state update changes.

Description

technical field [0001] The invention relates to the technical field of scheduling optimization of production resources in discrete assembly workshops, in particular to a path optimization method for material delivery in multi-objective discrete assembly workshops. Background technique [0002] The products produced by the discrete assembly workshop are assembled and processed by a series of parts. The production process has key features such as discretization of the production process and scattered physical locations of production stations, so that the processing and assembly of different modules of the product are carried out at different locations. Further, the line-side inventory of each production station is also scattered in different locations along with the production station. In order to ensure the smooth and orderly production activities of the discrete assembly workshop, it is necessary to manage the production material resources in the workshop reasonably, and eff...

AI Technical Summary

Problems solved by technology

[0003] At present, although heuristic algorithms represented by genetic algorithms, improvements and variants of genetic algorithms, and various swarm intelligence algorithms represented by ant colony algorithms and fish swarm algorithms have considerable Good algorithm performance, and can also achieve good optimization results for different optimization objectives, but the time consumption required for these algorithm operations is usually ignored by researchers, and the optimization process of the target problem often takes a lot of computing time, which leads to In more cases, it is impossible to deal with problem scenarios with real-time status updates, which restricts the application of various intelligent optimization algorithms.

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