A Multiple-objective Particle Swarm Optimization for Flexible Job Shop Scheduling Problem

Abstract

In this paper, a hybrid particle-swarm optimization algorithm is proposed for the flexible job shop scheduling problem with multiple objectives. The optimization objectives are considered to be the production cycle time, total machine load and the maximal single machine load, respectively. A discrete particle swarm combined with variable neighborhood search is proposed to solve the problem based on Maximin fitness. The main benefits of the method are the improvements of both global and local searching ability. Compared to other algorithms on benchmark problems, the simulation results show the effectiveness of the algorithm.