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A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect

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  • Hamta, Nima
  • Fatemi Ghomi, S.M.T.
  • Jolai, F.
  • Akbarpour Shirazi, M.

Abstract

This paper addresses multi-objective (MO) optimization of a single-model assembly line balancing problem (ALBP) where the operation times of tasks are unknown variables and the only known information is the lower and upper bounds for operation time of each task. Three objectives are simultaneously considered as follows: (1) minimizing the cycle time, (2) minimizing the total equipment cost, and (3) minimizing the smoothness index. In order to reflect the real industrial settings adequately, it is assumed that the task time is dependent on worker(s) (or machine(s)) learning for the same or similar activity and sequence-dependent setup time exists between tasks. Finding an optimal solution for this complicated problem especially for large-sized problems in reasonable computational time is cumbersome. Therefore, we propose a new solution method based on the combination of particle swarm optimization (PSO) algorithm with variable neighborhood search (VNS) to solve the problem. The performance of the proposed hybrid algorithm is examined over several test problems in terms of solution quality and running time. Comparison with an existing multi-objective evolutionary computation method in the literature shows the superior efficiency of our proposed PSO/VNS algorithm.

Suggested Citation

  • Hamta, Nima & Fatemi Ghomi, S.M.T. & Jolai, F. & Akbarpour Shirazi, M., 2013. "A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect," International Journal of Production Economics, Elsevier, vol. 141(1), pages 99-111.
    • Handle: RePEc:eee:proeco:v:141:y:2013:i:1:p:99-111
    DOI: 10.1016/j.ijpe.2012.03.013
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    1. Lai, Tsung-Chyan & Sotskov, Yuri N. & Dolgui, Alexandre, 2019. "The stability radius of an optimal line balance with maximum efficiency for a simple assembly line," European Journal of Operational Research, Elsevier, vol. 274(2), pages 466-481.
    2. Jonathan Oesterle & Lionel Amodeo & Farouk Yalaoui, 2019. "A comparative study of Multi-Objective Algorithms for the Assembly Line Balancing and Equipment Selection Problem under consideration of Product Design Alternatives," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1021-1046, March.
    3. Akpinar, Sener & Elmi, Atabak & Bektaş, Tolga, 2017. "Combinatorial Benders cuts for assembly line balancing problems with setups," European Journal of Operational Research, Elsevier, vol. 259(2), pages 527-537.
    4. Boysen, Nils & Schulze, Philipp & Scholl, Armin, 2022. "Assembly line balancing: What happened in the last fifteen years?," European Journal of Operational Research, Elsevier, vol. 301(3), pages 797-814.
    5. Abolfazl Jafari Asl & Maghsud Solimanpur & Ravi Shankar, 2019. "Multi-objective multi-model assembly line balancing problem: a quantitative study in engine manufacturing industry," OPSEARCH, Springer;Operational Research Society of India, vol. 56(3), pages 603-627, September.
    6. Olcay Polat & Can B. Kalayci & Özcan Mutlu & Surendra M. Gupta, 2016. "A two-phase variable neighbourhood search algorithm for assembly line worker assignment and balancing problem type-II: an industrial case study," International Journal of Production Research, Taylor & Francis Journals, vol. 54(3), pages 722-741, February.
    7. Ibrahim Kucukkoc & Kadir Buyukozkan & Sule Itir Satoglu & David Z. Zhang, 2019. "A mathematical model and artificial bee colony algorithm for the lexicographic bottleneck mixed-model assembly line balancing problem," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2913-2925, December.
    8. Li, Mingxing & Huang, George Q., 2021. "Production-intralogistics synchronization of industry 4.0 flexible assembly lines under graduation intelligent manufacturing system," International Journal of Production Economics, Elsevier, vol. 241(C).
    9. Jia Liu & Shuwei Wang, 2017. "Balancing Disassembly Line in Product Recovery to Promote the Coordinated Development of Economy and Environment," Sustainability, MDPI, vol. 9(2), pages 1-15, February.
    10. Emel Kızılkaya Aydoğan & Yılmaz Delice & Uğur Özcan & Cevriye Gencer & Özkan Bali, 2019. "Balancing stochastic U-lines using particle swarm optimization," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 97-111, January.
    11. Jietao Dong & Linxuan Zhang & Tianyuan Xiao, 2018. "A hybrid PSO/SA algorithm for bi-criteria stochastic line balancing with flexible task times and zoning constraints," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 737-751, April.
    12. Lai, Tsung-Chyan & Sotskov, Yuri N. & Dolgui, Alexandre & Zatsiupa, Aksana, 2016. "Stability radii of optimal assembly line balances with a fixed workstation set," International Journal of Production Economics, Elsevier, vol. 182(C), pages 356-371.
    13. Zixiang Li & Mukund Nilakantan Janardhanan & S. G. Ponnambalam, 2021. "Cost-oriented robotic assembly line balancing problem with setup times: multi-objective algorithms," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 989-1007, April.
    14. Parvaneh Samouei & Mahsa Sobhishoja, 2023. "Robust counterpart mathematical models for balancing, sequencing, and assignment of robotic U-shaped assembly lines with considering failures and setup times," OPSEARCH, Springer;Operational Research Society of India, vol. 60(1), pages 87-124, March.
    15. Battaïa, Olga & Dolgui, Alexandre, 2022. "Hybridizations in line balancing problems: A comprehensive review on new trends and formulations," International Journal of Production Economics, Elsevier, vol. 250(C).
    16. Kucukkoc, Ibrahim & Zhang, David Z., 2014. "Mathematical model and agent based solution approach for the simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines," International Journal of Production Economics, Elsevier, vol. 158(C), pages 314-333.
    17. De Vincenzo, Ilario & Massari, Giovanni F. & Giannoccaro, Ilaria & Carbone, Giuseppe & Grigolini, Paolo, 2018. "Mimicking the collective intelligence of human groups as an optimization tool for complex problems," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 259-266.

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