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A network-based shortest route model for parallel disassembly line balancing problem

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  • Seda Hezer
  • Yakup Kara

Abstract

Disassembly lines should be balanced efficiently to increase productivity of the line and to reduce disassembly costs. This problem is called disassembly line balancing problem (DLBP). The objective of the DLBP is usually to find the minimum number of disassembly workstations required. This study introduces parallel DLBP (PDLBP) with single-product and proposes a network model based on the shortest route model (SRM) for solving PDLBP. The proposed model is illustrated via numerical examples. A comprehensive experiment is also conducted to evaluate problem-specific features of disassembly lines. To the best of our knowledge, this is the first study dealing with PDLBP. This paper will present a different point of view regarding DLBP.

Suggested Citation

  • Seda Hezer & Yakup Kara, 2015. "A network-based shortest route model for parallel disassembly line balancing problem," International Journal of Production Research, Taylor & Francis Journals, vol. 53(6), pages 1849-1865, March.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:6:p:1849-1865
    DOI: 10.1080/00207543.2014.965348
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    Citations

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    Cited by:

    1. 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.
    2. Ömer Faruk Yılmaz & Büşra Yazıcı, 2022. "Tactical level strategies for multi-objective disassembly line balancing problem with multi-manned stations: an optimization model and solution approaches," Annals of Operations Research, Springer, vol. 319(2), pages 1793-1843, December.
    3. Tian, Xiaoyu & Zhang, Zhi-Hai, 2019. "Capacitated disassembly scheduling and pricing of returned products with price-dependent yield," Omega, Elsevier, vol. 84(C), pages 160-174.
    4. Jianhua Cao & Xuhui Xia & Lei Wang & Zelin Zhang & Xiang Liu, 2019. "A Novel Multi-Efficiency Optimization Method for Disassembly Line Balancing Problem," Sustainability, MDPI, vol. 11(24), pages 1-16, December.
    5. 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).
    6. Süleyman Mete & Faruk Serin & Zeynel Abidin Çil & Erkan Çelik & Eren Özceylan, 2023. "A comparative analysis of meta-heuristic methods on disassembly line balancing problem with stochastic time," Annals of Operations Research, Springer, vol. 321(1), pages 371-408, February.
    7. Liu, Kanglin & Zhang, Zhi-Hai, 2018. "Capacitated disassembly scheduling under stochastic yield and demand," European Journal of Operational Research, Elsevier, vol. 269(1), pages 244-257.
    8. 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.
    9. Fang, Yilin & Liu, Quan & Li, Miqing & Laili, Yuanjun & Pham, Duc Truong, 2019. "Evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations," European Journal of Operational Research, Elsevier, vol. 276(1), pages 160-174.
    10. 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.
    11. Ziyan Zhao & Pengkai Xiao & Jiacun Wang & Shixin Liu & Xiwang Guo & Shujin Qin & Ying Tang, 2023. "Improved Brain-Storm Optimizer for Disassembly Line Balancing Problems Considering Hazardous Components and Task Switching Time," Mathematics, MDPI, vol. 12(1), pages 1-19, December.
    12. Diri Kenger, Zülal & Koç, Çağrı & Özceylan, Eren, 2021. "Integrated disassembly line balancing and routing problem with mobile additive manufacturing," International Journal of Production Economics, Elsevier, vol. 235(C).

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