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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Akpınar, Ş. (2017). Large neighbourhood search algorithm for type-II assembly line balancing problem. Pamukkale University Journal of Engineering Sciences, 23(4), 444–450. https://doi.org/10.5505/pajes.2016.75975
Álvarez-Miranda, E., Chace, S., & Pereira, J. (2020). Assembly line balancing with parallel workstations. International Journal of Production Research. https://doi.org/10.1080/00207543.2020.1818000
Amous, M., Toumi, S., Jarboui, B., & Eddaly, M. (2017). A variable neighborhood search algorithm for the capacitated vehicle routing problem. Electronic Notes in Discrete Mathematics, 58, 231–238. https://doi.org/10.1016/j.endm.2017.03.030
Becker, C., & Scholl, A. (2006). A survey on problems and methods in generalized assembly line balancing. European Journal of Operational Research, 168(3), 694–715. https://doi.org/10.1016/j.ejor.2004.07.023
Blum, C. (2011). Iterative beam search for simple assembly line balancing with a fixed number of work stations. Sort, 35(2), 145–164.
Çil, Z. A., Mete, S., Özceylan, E., & Ağpak, K. (2017). A beam search approach for solving type II robotic parallel assembly line balancing problem. Applied Soft Computing, 61, 129–138. https://doi.org/10.1016/j.asoc.2017.07.062
Gutjahr, A. L., & Nemhauser, G. L. (1964). An Algorithm for the line balancing problem. Management Science, 11(2), 308–315. https://doi.org/10.1287/mnsc.11.2.308
Lei, D., & Guo, X. (2016). Variable neighborhood search for the second type of two-sided assembly line balancing problem. Computers and Operations Research, 72(1), 183–188. https://doi.org/10.1016/j.cor.2016.03.003
Li, Y., Wang, H., & Yang, Z. (2019). Type II assembly line balancing problem with multi-operators. Neural Computing and Applications, 31(s1), 347–357. https://doi.org/10.1007/s00521-018-3834-1
Li, Z., Janardhanan, M. N., Tang, Q., & Nielsen, P. (2018). Mathematical model and metaheuristics for simultaneous balancing and sequencing of a robotic mixed-model assembly line. Engineering Optimization, 50(5), 877–893. https://doi.org/10.1080/0305215X.2017.1351963
Li, Z., Kucukkoc, I., & Zhang, Z. (2020). Branch, bound and remember algorithm for two-sided assembly line balancing problem. European Journal of Operational Research, 284(3), 896–905. https://doi.org/10.1016/j.ejor.2020.01.032
Lotfi, R., Mehrjerdi, Y. Z., Pishvaee, M. S., Sadeghieh, A., & Weber, G. W. (2019). A robust optimization model for sustainable and resilient closed-loop supply chain network design considering conditional value at risk. Numerical Algebra, Control & Optimization. https://doi.org/10.3934/naco.2020023
Mete, S., Çil, Z. A., Özceylan, E., Ağpak, K., & Battaïa, O. (2018). An optimisation support for the design of hybrid production lines including assembly and disassembly tasks. International Journal of Production Research, 56(24), 7375–7389. https://doi.org/10.1080/00207543.2018.1428774
Mladenović, N., & Hansen, P. (1997). Variable neighborhood search. Computers & operations research, 24(11), 1097–1100. https://doi.org/10.1016/S0305-0548(97)00031-2
Nanthamroeng, N. (2010). Development of meta-heuristics for multiobjective and multi stage location routing problem. Thesis for Doctor of Philosophy in Industrial Engineering, Ubon Ratchathani University.
Nearchou, A. C. (2005). A differential evolution algorithm for simple assembly line balancing. In IFAC proceedings volumes (IFAC-PapersOnline) (Vol. 16). Doi: https://doi.org/10.3182/20050703-6-cz-1902.01463
Otto, A., & Otto, C. (2014). How to design effective priority rules: Example of simple assembly line balancing. Computers & Industrial Engineering, 69, 43–52. https://doi.org/10.1016/j.cie.2013.12.013
Phonganant, S. (2017). Expert system for mixed-model assembly line balancing. Journal of Thonburi University., 1(1), 37–46.
Pitakaso, R., & Sethanan, K. (2016). Modified differential evolution algorithm for simple assembly line balancing with a limit on the number of machine types. Engineering Optimization, 48(2), 253–271. https://doi.org/10.1080/0305215X.2015.1005082
Pitakaso, R., Sethanan, K., & Jamrus, T. (2020a). Hybrid PSO and ALNS algorithm for a software and mobile application for transportation in the ice manufacturing industry 3.5. Computers & Industrial Engineering, 144, 106461. https://doi.org/10.1016/j.cie.2020.106461
Pitakaso, R., Sethanan, K., & Theeraviriya, C. (2020b). Variable neighborhood strategy adaptive search for solving green 2-echelon location routing problem. Computers and Electronics in Agriculture, 173, 105406. https://doi.org/10.1016/j.compag.2020.105406
Promseenong, Y. (2015). Variable neighborhood search algorithms for dynamic vehicle routing problem with time windows. Thesis M.Eng in Management Engineering, Naresuan University.
Ropke, S., & Pisinger, D. (2006). An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transportation Science, 40(4), 455–472. https://doi.org/10.1287/trsc.1050.0135
Roshani, A., & Giglio, D. (2015). A simulated annealing approach for multi-manned assembly line balancing problem type II. IFAC-PapersOnLine, 28(3), 2299–2304. https://doi.org/10.1016/j.ifacol.2015.06.430
Sadeghi, P., Rebelo, R. D., & Ferreira, J. S. (2018). Balancing mixed-model assembly systems in the footwear industry with a variable neighbourhood descent method. Computers & Industrial Engineering, 121, 161–176. https://doi.org/10.1016/j.cie.2018.05.020
Scholl, A. (1993). Data of assembly line balancing problem. https://assembly-line-balancing.de/.
Scholl, A., & Becker, C. (2006). State-of-the-art exact and heuristic solution procedures for simple assembly line balancing. European Journal of Operational Research, 168(3), 666–693. https://doi.org/10.1016/j.ejor.2004.07.022
Shaw, P. (1998). Using constraint programming and local search methods to solve vehicle routing problems. In M. Maher & J.-F. Puget (Eds.), BT—Principles and practice of constraint programming—CP98. Springer.
Sikora, C. G. S., Lopes, T. C., Lopes, H. S., & Magatão, L. (2016). Genetic algorithm for type-2 assembly line balancing. 2015 Latin-America Congress on Computational Intelligence, LA-CCI 2015, (November 2016). Doi: https://doi.org/10.1109/LA-CCI.2015.7435951.
Sivasankaran, P., & Shahabudeen, P. (2014). Literature review of assembly line balancing problems. International Journal of Advanced Manufacturing Technology, 73(9–12), 1665–1694. https://doi.org/10.1007/s00170-014-5944-y
Storn, R., & Price, K. (1997). Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359. https://doi.org/10.1023/A:1008202821328
Wu, C., Hu, X., Zhang, Y., & Wang, P. (2019). A modified Monte-Carlo tree search algorithm for two-sided assembly line balancing problem. IFAC-PapersOnLine, 52(13), 1920–1924. https://doi.org/10.1016/j.ifacol.2019.11.483
Zare Mehrjerdi, Y., & Lotfi, R. (2019). Development of a mathematical model for sustainable closed-loop supply chain with efficiency and resilience systematic framework. International Journal of Supply and Operations Management, 6(4), 360–388. https://doi.org/10.22034/2019.4.6
Zhang, H. Y. (2017). An improved immune algorithm for simple assembly line balancing problem of type 1. Journal of Algorithms & Computational Technology, 11(4), 317–326. https://doi.org/10.1177/1748301817710924
Zheng, Q., Li, M., Li, Y., & Tang, Q. (2013). Station ant colony optimization for the type 2 assembly line balancing problem. International Journal of Advanced Manufacturing Technology, 66(9–12), 1859–1870. https://doi.org/10.1007/s00170-012-4465-9
Acknowledgements
This research was supported by the Department of Industrial Engineering, Ubon Ratchathani University and the Research Unit on System Modelling for Industry (Grant No. SMI.KKU 63004), Faculty of Engineering, Khon Kaen University, Thailand. The authors would also like to thank Mr. Ian Thomas for critical review of the manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pitakaso, R., Sethanan, K., Jirasirilerd, G. et al. A novel variable neighborhood strategy adaptive search for SALBP-2 problem with a limit on the number of machine’s types. Ann Oper Res 324, 1501–1525 (2023). https://doi.org/10.1007/s10479-021-04015-1
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-021-04015-1