Research On Flexible Workshop Scheduling Problem Based On Five-element Loop Optimization Algorithm

As people's living standard have improved,material needs have gradually increased,which has promoted the advancement of science and technology and the abundance of social resources.Therefore,the effective improvement of productivity has become a must for enterprises,which puts forward new requirements for the modernization and technologicalization of production workshops.Among them,the research of workshop scheduling problem has received extensive attention from domestic and foreign scholars due to its important practical value.This paper focuses on four main optimization goals,such as maximum completion time,production cost,consumption of energy and processing quality.And the five-element circle optimization algorithm is used to solve the problem about the m ulti-objective flexible job-shop scheduling.The main research contents are as follows:First of all,in the problem of flexible job-shop scheduling,traditional genetic algorithm(GA)and particle swarm optimization(PSO)have problems such as slow search speed and insufficient local optimization ability caused by the characteristics of easy prematurity.In order to improve these shortcomings,drawing on the laws of the five elements of gold,wood,water,fire,and soil,and optimize the iterative method of these elements.All elements are restricted and promote each other at the same time,forming a dynamic and balanced relationship.The improved five-element ring optimization algorithm effectively solves the shortcomings of traditional genetic algorithm and particle swarm algorithm in the iterative process,such as slow search speed and poor local optimization ability.And the result calculated by the improved five-element ring optimization algorithm is more accurate and the running time is less.Secondly,considering that the workshop scheduling problem often has more than one limiting condition in the actual working environment.Firstly,the multi-objective task model,stepwise adaptation of weights(SAW)is used instead of the single-objective model.In the multi-objective model,although the traditional SAW model is easy to solve relatively,the setting of preference for different objectives has a non-negligible effect on the results.In response to this problem,adaptability is introduced in the main process to solve multi-objective problems.The Pareto principle of function and aggregation distance is used to select the non-dominant solution method.In order to evaluate the degree of coding,the analytic hierarchy process,also called AHP is added to multiply the output value of each module of each individual by the randomly generated weight coefficient.The two linear additions are summed to obtain the total fitness function of the individual.Then select from the largest to the smallest to obtain the population,and need to use the aggregation strategy to screen individuals with the same adaptability.Comparing the two selection strategies,applying the more excellent Pareto optimization based on the dynamic aggregation distance distribution maintenance strategy to the flexible workshop scheduling problem in the multi-objective model can accurately obtain the target results and reduce the running time of the algorithm.And the number of iterations,to more reasonably solve the multi-objective flexible job-shop scheduling problem under actual conditions.Finally,MATLAB platform was used to build the working environment required for the paper,and the improved five-element ring optimization algorithm was simulated and tested at the single-objective and multi-objective models.The results show the feasibility and superiority of the improved five-element ring optimization algorithm in solving the multi-objective flexible workshop scheduling problem...