This paper presents a Hybrid Evolutionary Algorithm (HEA) to solve the Job Shop Scheduling Problem (JSP). Incorporating a tabu search procedure into the framework of an evolutionary algorithm, the HEA embraces several distinguishing features such as a longest common sequence based recombination operator and a similarity-and-quality based replacement criterion for population updating. The HEA is able to easily generate the best-known solutions for 90 % of the tested difficult instances widely used in the literature, demonstrating its efficacy in terms of both solution quality and computational efficiency. In particular, the HEA identifies a better upper bound for two of these difficult instances.
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We would like to thank the anonymous referees for their helpful comments that help to improve our paper. We appreciate Dr. Chaoyong Zhang’s kind help to explain the TS algorithm for JSP to us.
The research was supported in part by the Hong Kong Scholars Programme and the National Natural Science Foundation of China under grant number 61100144 and 61272014.
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Cheng, T.C.E., Peng, B. & Lü, Z. A hybrid evolutionary algorithm to solve the job shop scheduling problem. Ann Oper Res 242, 223–237 (2016). https://doi.org/10.1007/s10479-013-1332-5