A hybrid evolutionary algorithm to solve the job shop scheduling problem

T. C. E. Cheng (), Bo Peng () and Zhipeng Lü ()
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T. C. E. Cheng: The Hong Kong Polytechnic University
Bo Peng: Huazhong University of Science and Technology
Zhipeng Lü: Huazhong University of Science and Technology

Annals of Operations Research, 2016, vol. 242, issue 2, No 3, 223-237

Abstract: Abstract 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.

Keywords: Job shop scheduling; Evolutionary algorithm; Recombination operator; Population updating (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s10479-013-1332-5

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