Research on Flexible Job Scheduling Based on Genetic Prohibited Search Algorithm

Abstract

In order to improve the efficiency of flexible job workshop scheduling and production with the goal of maximum completion time, this paper analyzes the encoding and decoding methods of genetic algorithm, the method of population initialization, and sets the corresponding genetic operator parameters. Use prohibited search algorithm which rely on the initial solution to eliminate repetitive work and jump out of the local optimal solution. Firstly, the initial population uses genetic algorithm to search in the global solution space quickly and parallelly. After the iteration ends, prohibited conditions are set in the local region to search again. The hybrid genetic prohibited search algorithm is used to test benchmark sample data, and the results show that the algorithm can reduce the shop completion time within the specified number of iterations, which verifies the feasibility of the algorithm.