ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
A memetic algorithm for the multi-objective flexible job shop scheduling problem
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Journal of Parallel and Distributed Computing
Path-relinking Tabu search for the multi-objective flexible job shop scheduling problem
Computers and Operations Research
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Finding realistic schedules for flexible job shop problems has attracted many researchers recently due to its nondeterministic polynomial time (NP) hardness. In this paper, we present an efficient approach for solving the multiple-objective flexible job shop by combining evolutionary algorithm and guided local search (GLS). Instead of applying random local search to find neighboring solutions, we introduce a GLS procedure to accelerate the process of convergence to Pare to-optimal solutions. The main improvement of this combination is to help diversify the population toward the Pareto front. A branch and bound algorithm for finding the lower bounds of multiple-objective solutions is also proposed. Experimental results indicate that the multiple-objective Pareto-optimal solutions of our algorithms dominate previous designs for solving the same benchmarks while incurring less computational time.