Genetic Algorithm with Simulated Annealing for Resolving Job Shop Scheduling Problem

Abstract

In order to solve the limitation of traditional genetic algorithm to solve the job shop scheduling problem, combined with the advantages of genetic algorithm (GA) and simulated annealing algorithm (SA), this paper proposes a kind of algorithm based on NSGA-II, which inserts simulated annealing algorithm during operation. A hybrid genetic algorithm simulated annealing algorithm (GASA) combining the advantages of the two algorithms is generated. The algorithm not only has the advantages of fast convergence speed of genetic algorithm and wide search area of simulated annealing algorithm, but also overcomes the problem of premature convergence of the former and slow convergence speed of the latter. In the operation details of the algorithm, adaptive function, non-dominated sorting and elite retention strategy are added to effectively improve the effectiveness of job shop scheduling.