An efficient hybrid evolutionary algorithm for scheduling with setup times and weighted tardiness minimization

We confront the job shop scheduling problemwith sequence dependent setup times and weighted tardiness minimization. To solve this problem, we propose a hybrid metaheuristic that combines the intensification capability of tabu search with the diversification capability of a genetic algorithm which plays the role of long term memory for tabu search in the combined approach. We define and analyze a new neighborhood structure for this problem which is embedded in the tabu search algorithm. The efficiency of the proposed algorithm relies on some elements such as neighbors filtering and a proper balance between intensification and diversification of the search. We report results from an experimental study across conventional benchmarks, where we analyze our approach and demonstrate that it compares favorably to the state-of-the-art methods. A preliminary version of this work was presented at the 14th Conference of the Spanish Association for Artificial Intelligence, CAEPIA2011, held in La Laguna, Spain, in 2011 (González et al (2011)). Miguel Ángel González Computing Technologies Group. Department of Computing Artificial Intelligence Center, University of Oviedo, Spain, Campus of Viesques, 33271 Gijón E-mail: [email protected] Camino R. Vela Computing Technologies Group. Department of Computing Artificial Intelligence Center, University of Oviedo, Spain, Campus of Viesques, 33271 Gijón E-mail: [email protected] Ramiro Varela Computing Technologies Group. Department of Computing Artificial Intelligence Center, University of Oviedo, Spain, Campus of Viesques, 33271 Gijón E-mail: [email protected] Inés González-Rodrı́guez Dept. of Mathematics, Statistics and Computing, University of Cantabria, Spain, E-mail: [email protected]