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In the context of customization and personalization, the multistage fine-manufacturing system has become emerging in modern manufacturing industries. In this study, the scheduling problem of such a system called distributed hybrid differentiation flowshop scheduling problem is addressed for the first time to minimize makespan. The manufacturing process has three dedicated stages, including distributed fabrication for jobs, assembly from jobs to products, and further differentiation for products to meet customized requirements. Due to the scheduling complexity of the problem, the evolutionary algorithm is designed. First, the population is initialized heuristically and divided into three subpopulations with different identities based on the quality. The identity will be transited dynamically along with evolution. Second, a distributed heterogeneous global exploration strategy is designed to coevolve three subpopulations. According to identity differences, different subpopulations will choose their suitable learning operators and learning strengths to make full use of superior search knowledge. Third, to enhance search intensification, a problem-specific local exploitation strategy consisting of a variable neighborhood local search and a random block local search is devised and carried out adaptively. Fourth, by organizing the global exploration and local exploitation, a novel distributed heterogeneous co-evolutionary algorithm (DHCA) is proposed. The effect of parameter setting on DHCA is investigated by design-of-experiment and computational experiments are carried out to evaluate the algorithm. The results validate the effectiveness of special designs and show that DHCA are more effective and efficient than the compared state-of-the-art algorithms in solving the considered problem.
Zhang et al. (Fri,) studied this question.