Abstract
No-wait flowshop scheduling problems are widespread in industries. Though processing times of jobs are traditionally assumed to be constant, they are variable because of learning and deteriorating effects in practical manufacturing processes which make the problems much more difficult. In this paper, we propose a metaheuristic, self-adaptive memetic algorithm (SAMA for short), for no-wait flowshops with variable processing times which have never been considered yet. The balance between intensification and diversification of the algorithm is adaptively controlled by evolutionary operators. Local search is performed on better individuals with higher probabilities to dynamically adjust diversification. Experimental results show that the proposed SAMA outperforms existing algorithms for similar problems.
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