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Branch, bound and remember algorithm for two-sided assembly line balancing problem

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  • Li, Zixiang
  • Kucukkoc, Ibrahim
  • Zhang, Zikai

Abstract

This research presents a new branch, bound and remember (BBR) algorithm to minimize the number of mated-stations in two-sided assembly lines. The proposed methodology modifies the Hoffman heuristic to achieve high-quality upper bounds, and employs two new dominance rules, referred to as memory-based maximal load rule and memory-based extended Jackson rule, to prune the sub-problems. The BBR algorithm also employs several other improvements to enhance the performance, including renumbering the tasks and new lower bounds. Computational results demonstrate that BBR achieves the optimal solutions for all the tested instances within 1.0 s on average, including two optimal solutions for the first time. Comparative study shows that BBR outperforms the current best exact method (branch and bound algorithm) and the current best heuristic algorithm (iterated greedy search algorithm). As a consequence, the proposed BBR can be regarded as the state-of-the-art method for TALBP.

Suggested Citation

  • Li, Zixiang & Kucukkoc, Ibrahim & Zhang, Zikai, 2020. "Branch, bound and remember algorithm for two-sided assembly line balancing problem," European Journal of Operational Research, Elsevier, vol. 284(3), pages 896-905.
    • Handle: RePEc:eee:ejores:v:284:y:2020:i:3:p:896-905
    DOI: 10.1016/j.ejor.2020.01.032
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    References listed on IDEAS

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    14. Michels, Adalberto Sato & Lopes, Thiago Cantos & Sikora, Celso Gustavo Stall & Magatão, Leandro, 2019. "A Benders’ decomposition algorithm with combinatorial cuts for the multi-manned assembly line balancing problem," European Journal of Operational Research, Elsevier, vol. 278(3), pages 796-808.
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    Cited by:

    1. Junyong Liang & Shunsheng Guo & Yunfei Zhang & Wenfang Liu & Shengwen Zhou, 2021. "Energy-Efficient Optimization of Two-Sided Disassembly Line Balance Considering Parallel Operation and Uncertain Using Multiobjective Flatworm Algorithm," Sustainability, MDPI, vol. 13(6), pages 1-23, March.
    2. Peng Hu & Feng Chu & Yunfei Fang & Peng Wu, 2022. "Novel distribution-free model and method for stochastic disassembly line balancing with limited distributional information," Journal of Combinatorial Optimization, Springer, vol. 43(5), pages 1423-1446, July.
    3. Santiago Valdés Ravelo, 2022. "Approximation algorithms for simple assembly line balancing problems," Journal of Combinatorial Optimization, Springer, vol. 43(2), pages 432-443, March.
    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. 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.
    6. 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).

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