Workshop layout scheduling optimization method based on multi-objective non-dominated sorting

Abstract

The invention discloses a workshop layout scheduling optimization method based on multi-objective non-dominated sorting. The method comprises the following steps: 1, collecting data related to production resources in an enterprise production process; 2, encoding equipment, layout and processes of a production workshop, and establishing an optimization model; 3, generating a uniform reference point set, and randomly initializing a population Pt of which the scale is N; 4, performing selection, crossover and variation on the population Pt to obtain a filial generation population Qt with the scale of N, and merging the parent population and the filial generation population to obtain a population Rt with the scale of 2N; 5, after the Rt is subjected to non-dominated sorting, dividing the Rt into a plurality of layers such as F1, F2 and the like; 6, indicating that St = F1 union, F2 union... Fl, and until '2jeemaa2' St '2jeemaa2' is greater than or equal to N; 7, normalizing individuals: normalizing all individual targets in the St; 8, setting a clustering operator, and defining the niche number of the ith reference point as rho i; 9, selecting K individuals from the F1 by using the niche number and putting the K individuals into Pt + 1; 10, judging whether the maximum number of iterations is met or not; and 11, generating layout codes and equipment allocation codes.

Description

technical field [0001] The invention relates to a workshop layout scheduling optimization method. Background technique [0002] In actual engineering optimization problems, most of them are multi-objective optimization problems, that is, to optimize the maximum and minimum values ​​of multiple objectives in a specific field such as transportation, factory production, and logistics scheduling. Compared with single-objective optimization problems, multi-objective optimization is often more complicated and difficult to solve, and the optimization goals are usually in conflict with each other. At the same time, it is difficult to find a perfect solution. In most cases, some trade-offs, decision-making, and cooperation are required. High-efficiency algorithms, etc. to obtain better results, and better results generally have a certain degree of robustness. [0003] Evolutionary methods are mostly inspired by biological survival or evolution in nature. Different from traditional ...

AI Technical Summary

Problems solved by technology

The existing workshop scheduling and layout schemes are mostly based on orders and experience, lacking the support of certain theoretical data

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