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A comparison of piecewise linear programming formulations for stochastic disassembly line balancing

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  • F. Tevhide Altekin

Abstract

Recently, several mathematical programming formulations and solution approaches have been developed for the stochastic disassembly line balancing problem (DLBP). This paper aims at finding optimal solutions for the stochastic DLBP. Two second-order cone programming (SOCP1 and SOCP2) models and five piecewise linear mixed integer programming (PwLP) models are presented. The PwLP formulations involve two specially ordered sets of type 2 (S1 and S2) models and three convex combination (CC1, CC2 and CC3) models. In each modelling category, the latter models strengthen the initial S1 and CC1 models. Our computational analysis of a total 240 instances of ten problems demonstrates that all the seven models can be used to solve practical-sized DLBP problems to optimality using GUROBI. The SOCP2 model and the strengthened S2 and CC2 models lead to lower computation times, compared to SOCP1, S1, CC1 and CC3, respectively. Using the strengthened S2 and CC2 formulations, the CPU times of the CC3 model available in the literature can be reduced by 50 and 40%, respectively. Besides analysing the optimal solutions and the differences of the computation times, we present insights gained from our results.

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  • F. Tevhide Altekin, 2017. "A comparison of piecewise linear programming formulations for stochastic disassembly line balancing," International Journal of Production Research, Taylor & Francis Journals, vol. 55(24), pages 7412-7434, December.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:24:p:7412-7434
    DOI: 10.1080/00207543.2017.1351639
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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. 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).
    4. Junkai He & Feng Chu & Feifeng Zheng & Ming Liu, 2021. "A green-oriented bi-objective disassembly line balancing problem with stochastic task processing times," Annals of Operations Research, Springer, vol. 296(1), pages 71-93, January.
    5. Yicong Gao & Shanhe Lou & Hao Zheng & Jianrong Tan, 2023. "A data-driven method of selective disassembly planning at end-of-life under uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 565-585, February.
    6. Lixia Zhu & Zeqiang Zhang & Yi Wang & Ning Cai, 2020. "On the end-of-life state oriented multi-objective disassembly line balancing problem," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1403-1428, August.
    7. Yusha Zhou & Xiuping Guo & Dong Li, 2022. "A dynamic programming approach to a multi-objective disassembly line balancing problem," Annals of Operations Research, Springer, vol. 311(2), pages 921-944, April.
    8. He, Junkai & Chu, Feng & Dolgui, Alexandre & Anjos, Miguel F., 2024. "Multi-objective disassembly line balancing and related supply chain management problems under uncertainty: Review and future trends," International Journal of Production Economics, Elsevier, vol. 272(C).

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