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A novel variable neighborhood strategy adaptive search for SALBP-2 problem with a limit on the number of machine’s types

Author

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  • Rapeepan Pitakaso

    (Ubon Ratchathani University)

  • Kanchana Sethanan

    (Khon Kaen University)

  • Ganokgarn Jirasirilerd

    (Ubon Ratchathani University)

  • Paulina Golinska-Dawson

    (Poznan University of Technology)

Abstract

This paper presents the novel method variable neighbourhood strategy adaptive search (VaNSAS) for solving the special case of assembly line balancing problems type 2 (SALBP-2S), which considers a limitation of a multi-skill worker. The objective is to minimize the cycle time while considering the limited number of types of machine in a particular workstation. VaNSAS is composed of two steps, as follows: (1) generating a set of tracks and (2) performing the track touring process (TTP). During TTP the tracks select and use a black box with neighborhood strategy in order to improve the solution obtained from step (1). Three modified neighborhood strategies are designed to be used as the black boxes: (1) modified differential evolution algorithm (MDE), (2) large neighborhood search (LNS) and (3) shortest processing time-swap (SPT-SWAP). The proposed method has been tested with two datasets which are (1) 128 standard test instances of SALBP-2 and (2) 21 random datasets of SALBP-2S. The computational result of the first dataset show that VaNSAS outperforms the best known method (iterative beam search (IBS)) and all other standard methods. VaNSAS can find 98.4% optimal solution out of all test instances while IBS can find 95.3% optimal solution. MDE, LNS and SPT-SWAP can find optimal solutions at 85.9%, 83.6% and 82.8% respectively. In the second group of test instances, we found that VaNSAS can find 100% of the minimum solution among all methods while MDE, LNS and SPT-SWAP can find 76.19%, 61.90% and 52.38% of the minimum solution.

Suggested Citation

  • Rapeepan Pitakaso & Kanchana Sethanan & Ganokgarn Jirasirilerd & Paulina Golinska-Dawson, 2023. "A novel variable neighborhood strategy adaptive search for SALBP-2 problem with a limit on the number of machine’s types," Annals of Operations Research, Springer, vol. 324(1), pages 1501-1525, May.
  • Handle: RePEc:spr:annopr:v:324:y:2023:i:1:d:10.1007_s10479-021-04015-1
    DOI: 10.1007/s10479-021-04015-1
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    References listed on IDEAS

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    1. Lili Wang & Min Li & Guanbin Kong & Haiwen Xu, 2024. "Joint decision-making for divisional seru scheduling and worker assignment considering process sequence constraints," Annals of Operations Research, Springer, vol. 338(2), pages 1157-1185, July.

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