Hybrid differential evolution optimization for no-wait flow-shop scheduling with sequence-dependent setup times and release dates

Authors:
Bin Qian;Hua-Bin Zhou;Rong Hu;Feng-Hong Xiang
Affiliations:
Department of Automation, Kunming University of Science and Technology, Kunming, China;Department of Automation, Kunming University of Science and Technology, Kunming, China;Department of Automation, Kunming University of Science and Technology, Kunming, China;Department of Automation, Kunming University of Science and Technology, Kunming, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Year:
2011

Citing 4
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A discrete differential evolution algorithm for the permutation flowshop scheduling problem

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A DE-based approach to no-wait flow-shop scheduling

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A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems

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A differential evolution approach for NTJ-NFSSP with SDSTs and RDs

ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications

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Abstract

In this paper, a hybrid algorithm based on differential evolution (DE), namely HDE, is proposed to minimize the total completion time criterion of the no-wait flow-shop scheduling problem (NFSSP) with sequence-dependent setup times (SDSTs) and release dates (RDs), which is a typical NP-hard combinatorial optimization problem with strong engineering background. Firstly, to make DE suitable for solving flow-shop scheduling problem, a largest-order-value (LOV) rule is used to convert the continuous values of individuals in DE to job permutations. Secondly, a speed-up evaluation method is developed according to the property of the NFSSP with SDSTs and RDs. Thirdly, after the DE-based exploration, a problem-dependent local search is developed to emphasize exploitation. Due to the reasonable balance between DE-based global search and problem-dependent local search as well as the utilization of the speed-up evaluation, the NFSSP with SDSTs and RDs can be solved effectively and efficiently. Simulation results and comparisons demonstrate the superiority of HDE in terms of searching quality, robustness, and efficiency.