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A stochastic production planning problem in hybrid manufacturing and remanufacturing systems with resource capacity planning

Author

Listed:
  • Chang Fang

    (Anhui Normal University
    University of Florida)

  • Xinbao Liu

    (Hefei University of Technology)

  • Panos M. Pardalos

    (University of Florida)

  • Jianyu Long

    (University of Florida
    Chongqing University)

  • Jun Pei

    (University of Florida
    Hefei University of Technology)

  • Chao Zuo

    (Zhejiang Gongshang University)

Abstract

Hybrid manufacturing and remanufacturing systems have become a topic of considerable interest in the advanced manufacturing industry due in part to the profit and cost saving by reusing remaufacturable parts in the end-of-use products. In this paper, we investigate a production planning problem in such a hybrid system with the integration of resource capacity planning that is shared by both manufacturing and remanufacturing processes. Due to the uncertain nature in practice, both new and remanufactured product demands are stochastic. Taking a scenario-based approach to express the stochastic demands according to the historical data, we formulate the stochastic aggregate production planning problem as a mixed integer linear programming (MILP) model. Based on the Lagrangian relaxation (LR) technique, the MILP model is decomposed into four sets of sub-problems. For these sub-problems, four heuristic procedures are developed, respectively. Then, a LR based heuristic for the main problem is proposed and further tested on a large set of problem instances. The results show that the algorithm generates solutions very close to optimums in an acceptable time. At last, the impact of demands uncertainty on the solution is analyzed by the sensitivity analysis on a number of scenarios.

Suggested Citation

  • Chang Fang & Xinbao Liu & Panos M. Pardalos & Jianyu Long & Jun Pei & Chao Zuo, 2017. "A stochastic production planning problem in hybrid manufacturing and remanufacturing systems with resource capacity planning," Journal of Global Optimization, Springer, vol. 68(4), pages 851-878, August.
    • Handle: RePEc:spr:jglopt:v:68:y:2017:i:4:d:10.1007_s10898-017-0500-6
    DOI: 10.1007/s10898-017-0500-6
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    References listed on IDEAS

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