Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22875
Title: A hybrid evolutionary algorithm to solve the job shop scheduling problem
Authors: Cheng, TCE 
Peng, B
Lu, Z
Keywords: Evolutionary algorithm
Job shop scheduling
Population updating
Recombination operator
Issue Date: 2016
Publisher: Springer
Source: Annals of operations research, 2016, v. 242, no. 2, p. 223-237 How to cite?
Journal: Annals of operations research 
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.
URI: http://hdl.handle.net/10397/22875
ISSN: 0254-5330
EISSN: 1572-9338
DOI: 10.1007/s10479-013-1332-5
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