A Genetic Algorithm and Tabu Search for Multi Objective Flexible Job Shop Scheduling Problems

Abstract

Flexible job shop scheduling problem (FJSP) is an important extension of the classical job shop scheduling problem, where the same operation could be processed on more than one machine. Owing to the high computational complexity, it is quite difficult to achieve an optimal solution with traditional optimization approaches. An improved genetic algorithm combined with tabu search is proposed to solve the multi objective FJSP in this paper. An external memory of non-dominated solutions is adopted to save and update the non-dominated solutions during the optimization process. Benchmark problems are used to evaluate and study the performance of the proposed algorithm. Computational results show that the proposed algorithm is efficient and effective approach for the multi objective FJSP.