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Heuristic approach on dynamic lot-sizing model for durable products with end-of-use constraints

Author

Listed:
  • Yongjian Li

    (Nankai University)

  • Xiaoqiang Cai

    (The Chinese University of Hong Kong)

  • Lei Xu

    (Nankai University
    Tianjin University of Technology)

  • Wenxia Yang

    (Nankai University)

Abstract

A version of the dynamic lot-sizing (DLS) problem involving durable products with end-of-use constraints is analyzed in this paper. First, we mathematically formulate this problem, then certain properties are derived to construct the structure of the optimal solution. Next, based on these properties, a recursive optimization algorithm is proposed for a single-item problem. Moreover, an approximate algorithm is designed on the basis of the optimization algorithm, with linear computational complexity. A heuristic approach is proposed for solving the two-item DLS problem. The difficulty in solving this problem lies in its decomposition into item-level subproblems while ensuring the feasibility of the solution. The proposed technique aims to resolve this issue by combining the capabilities of Lagrangian relaxation to decompose the problem into smaller subproblems, and a genetic algorithm (GA) is used to update the Lagrangian multipliers. Further, the computational results obtained using the proposed approach are enumerated to demonstrate its effectiveness. Finally, the conclusion and remarks are given to discuss the possible future works.

Suggested Citation

  • Yongjian Li & Xiaoqiang Cai & Lei Xu & Wenxia Yang, 2016. "Heuristic approach on dynamic lot-sizing model for durable products with end-of-use constraints," Annals of Operations Research, Springer, vol. 242(2), pages 265-283, July.
    • Handle: RePEc:spr:annopr:v:242:y:2016:i:2:d:10.1007_s10479-013-1526-x
    DOI: 10.1007/s10479-013-1526-x
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    Cited by:

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    2. Zhou, Shenghan & Zhou, Yuliang & Zuo, Xiaorong & Xiao, Yiyong & Cheng, Yang, 2018. "Modeling and solving the constrained multi-items lot-sizing problem with time-varying setup cost," Chaos, Solitons & Fractals, Elsevier, vol. 116(C), pages 202-207.
    3. Kannan Govindan, 2016. "Evolutionary algorithms for supply chain management," Annals of Operations Research, Springer, vol. 242(2), pages 195-206, July.

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