Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm
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Multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. This study addresses a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan of the first agent and the total tardiness of the second agent. In the investigated problem, the normal processing time of jobs is a random variable, and the actual processing time of jobs is a linear function of their normal processing time and starting time. To solve this problem efficiently, this study proposes a hybrid multi-objective evolutionary algorithm which is a combination of an evolutionary algorithm and a local search method. It maintains two populations and one archive. The two populations are utilized to execute the global and local searches, where one population employs an evolutionary algorithm to explore the whole solution space, and the other applies a local search method to exploit the promising regions. The archive is used to guide the computation resource allocation in the search process. Some special techniques, i.e., evolutionary methods, local search methods and information exchange strategies between two populations, are designed to enhance the exploration and exploitation ability. Comparing with the classical and popular multi-objective evolutionary algorithms on some test instances, the experimental results show that the proposed algorithm can produce satisfactory solution for the investigated problem.
KeywordsFlow shop scheduling Deteriorating scheduling Multi-objective multi-agent scheduling Multi-objective evolutionary algorithm Multipopulation
This work is partly supported by the National Science Foundation for Distinguished Young Scholars of China under Grant Nos. 71325002, 61525302; Major International Joint Research Project of NSFC under Grant No. 71620107003; the Foundation for Innovative Research Groups of National Science Foundation of China under Grant No. 61621004; the National Nature Science Foundation of China under Grant Nos. 61703220, 71671032, 51775238; Shandong Provincial Natural Science Foundation, China under Grant No ZR2016FP02; China Postdoctoral Science Foundation Funded Project under Grant No. 2017M610407; Qingdao Postdoctoral Research Project under Grant No. 2016027.
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