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

• Published in: Annals of operations research Vol. 324; no. 1-2; pp. 1501 - 1525
• Main Authors: Pitakaso, Rapeepan, Sethanan, Kanchana, Jirasirilerd, Ganokgarn, Golinska-Dawson, Paulina
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2023; Springer; Springer Nature B.V
• Subjects: Adaptive search techniques; Assembly lines; Assembly-line balancing; Black boxes; Business and Management; Combinatorics; Cycle time; Datasets; Evolutionary algorithms; Evolutionary computation; Iterative methods; Machinery; Magneto-electric machines; Mathematical optimization; Neighborhoods; Operations research; Operations Research/Decision Theory; Original Research; Searching; Theory of Computation; Workstations; Thailand; Large neighbourhood search; Modified differential evolution algorithm; Assembly line balancing problem type 2; Variable neighbourhood strategy adaptive search
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1007/s10479-021-04015-1

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.