Abstract
As a typical NP-hard combination optimization problem, the hybrid flow shop widely exists in manufacturing systems. In this paper, a mathematical model of hybrid flow shop is formulated, and then a new encoding and decoding method based on matrix is designed, together with Self-Adaptive Cuckoo Search(SACS) algorithm to minimize the makespan of this problem. The main contribution of this paper is to develop a new approach hybridizing CS with bottleneck heuristic method to fully exploit the bottleneck stage, and then bring in a self-adaptive parameter adjusting strategy along with iterations to enhance the ability to jump out of local extreme value and maintain the evolution energy. furthermore, elite learning strategies and some local search methods are applied to enhance the local search ability. The comparison between the proposed algorithm and several effective algorithms show that the SACS algorithm is feasible and practical.
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