An Effective Hybrid Particle Swarm Optimization Algorithm for Multi-objective FJSP

Abstract

Flexible Job shop Scheduling Problem (FJSP) is an extension of the classical job shop scheduling problem. Although the traditional optimization algorithms could obtain preferable results in solving the mono-objective FJSP, they are very difficult to solve multi-objective FJSP very well. In this chapter, a Particle Swarm Optimization (PSO) algorithm and a Tabu Search (TS) algorithm are combined to solve the multi-objective FJSP with several conflicting and incommensurable objectives. PSO which integrates local search and global search scheme possesses high search efficiency. And TS is a meta-heuristic which is designed for finding a near-optimal solution of combinatorial optimization problems. Through reasonably hybridizing the two optimization algorithms, an effective hybrid approach for the multi-objective FJSP has been proposed. The computational results have proved that the proposed hybrid algorithm is an efficient and effective approach to solve the multi-objective FJSP, especially for the problems on a large scale.