Parallel tabu search algorithm for the permutation flow shop problem with criterion of minimizing sum of job completion times

This paper deals with an intelligent algorithm dedicated for the use in manufacturing systems. Particularly, it develops the fast parallel tabu search algorithm to minimize sum of job completion times in the flow shop scheduling problem. So called multimoves are used, that consist in performing several independent moves simultaneously, which allow one to guide very quickly the search process to promising areas of the solutions space, where good solutions can be found. Besides, an adaptable dynamic tabu list and varying neighborhood are proposed to avoid being trapped at a local optimum. The proposed algorithms are experimentally evaluated on a personal computer with duo-core processor and found to be relatively more effective in finding solutions of quality better than other leading approaches, and also it makes in a much shorter time. The presented ideas can be extended to cover search methods for other hard problems.