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Remanufacturing production planning with compensation function approximation method

Author

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  • Wen, Haijun
  • Liu, Mingzhou
  • Liu, Changyi
  • Liu, Conghu

Abstract

Remanufacturing is becoming a strategic emerging industry in China. However, there are many uncertain factors such as remanufacturing rate of recycling products, reprocessing costs, quantity of recycling products during a remanufacturing process. Hence, it is difficult to make an accurate production planning. This paper aims at studying a new remanufacturing production planning model in view of some possible uncertain factors in a remanufacturing enterprise according to the features and characteristics of remanufacturing. Considering the production capacity constraint of recycling, reprocessing and reassembly under the condition of uncertain reprocessing amount, unpredictable reprocessing cost, unknown purchase volume of new parts, and uncertain customer demand, this paper develops a two-stage, multi-period hybrid programming model with compensation function based on uncertainty theory to minimize the total remanufacturing cost. A hybrid intelligent algorithm is designed combined with compensation function approximation, neural network training, and virus particle algorithm to optimize this two-stage uncertain remanufacturing production planning. By use of compensation function approximation method, it is to convert an infinite optimization problem in this algorithm into that of a finite one. Finally, one remanufacturing simulation case is studied to validate the efficiency and rationality of the proposed approach.

Suggested Citation

  • Wen, Haijun & Liu, Mingzhou & Liu, Changyi & Liu, Conghu, 2015. "Remanufacturing production planning with compensation function approximation method," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 742-753.
    • Handle: RePEc:eee:apmaco:v:256:y:2015:i:c:p:742-753
    DOI: 10.1016/j.amc.2015.01.070
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    References listed on IDEAS

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    1. Mukhopadhyay, Samar K. & Ma, Huafan, 2009. "Joint procurement and production decisions in remanufacturing under quality and demand uncertainty," International Journal of Production Economics, Elsevier, vol. 120(1), pages 5-17, July.
    2. Li, Yongjian & Chen, Jian & Cai, Xiaoqiang, 2007. "Heuristic genetic algorithm for capacitated production planning problems with batch processing and remanufacturing," International Journal of Production Economics, Elsevier, vol. 105(2), pages 301-317, February.
    3. Kenné, Jean-Pierre & Dejax, Pierre & Gharbi, Ali, 2012. "Production planning of a hybrid manufacturing–remanufacturing system under uncertainty within a closed-loop supply chain," International Journal of Production Economics, Elsevier, vol. 135(1), pages 81-93.
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    Cited by:

    1. Zhigang Jiang & Ya Jiang & Yan Wang & Hua Zhang & Huajun Cao & Guangdong Tian, 2019. "A hybrid approach of rough set and case-based reasoning to remanufacturing process planning," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 19-32, January.
    2. Felix T.S. Chan & Nan Li & S.H. Chung & Mozafar Saadat, 2017. "Management of sustainable manufacturing systems-a review on mathematical problems," International Journal of Production Research, Taylor & Francis Journals, vol. 55(4), pages 1210-1225, February.
    3. Zhang, Yunrong & Hong, Zhaofu & Chen, Zhixiang & Glock, Christoph H., 2020. "Tax or subsidy? Design and selection of regulatory policies for remanufacturing," European Journal of Operational Research, Elsevier, vol. 287(3), pages 885-900.
    4. Jian Zhou & Yujiao Jiang & Athanasios A. Pantelous & Weiwen Dai, 2023. "A systematic review of uncertainty theory with the use of scientometrical method," Fuzzy Optimization and Decision Making, Springer, vol. 22(3), pages 463-518, September.

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