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Multi-time scale Markov decision process approach to strategic network growth of reverse supply chains

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  • Wongthatsanekorn, Wuthichai
  • Realff, Matthew J.
  • Ammons, Jane C.

Abstract

This paper addresses a complex set of decisions that surround the growth over time of reverse supply chain networks that collect used products for reuse, refurbishment, and/or recycling by processors. The collection network growth problem is decomposed into strategic, tactical and operational problems. This paper focuses on the strategic problem which is to determine how to allocate capital budget resource effectively to grow the network to meet long term collection targets and collection cost constraints. We model the strategic problem as a Markov decision process which can also be posed as multi-time scale Markov decision problem. The recruitment problem in a tactical level appears as a sub-problem for the strategic model. Using dynamic programming, linear programming and Q-Learning approaches, an heuristic is implemented to solve realistically sized problems. A numerical study demonstrates that the heuristic can obtain a good solution for the large-scale problem in reasonable time which is not possible when trying to obtain the optimal solution with the exact DP approach.

Suggested Citation

  • Wongthatsanekorn, Wuthichai & Realff, Matthew J. & Ammons, Jane C., 2010. "Multi-time scale Markov decision process approach to strategic network growth of reverse supply chains," Omega, Elsevier, vol. 38(1-2), pages 20-32, February.
    • Handle: RePEc:eee:jomega:v:38:y:2010:i:1-2:p:20-32
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    References listed on IDEAS

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    1. Listes, Ovidiu & Dekker, Rommert, 2005. "A stochastic approach to a case study for product recovery network design," European Journal of Operational Research, Elsevier, vol. 160(1), pages 268-287, January.
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    Cited by:

    1. Qiang, Qiang & Ke, Ke & Anderson, Trisha & Dong, June, 2013. "The closed-loop supply chain network with competition, distribution channel investment, and uncertainties," Omega, Elsevier, vol. 41(2), pages 186-194.
    2. Dingyue Zhang & Xuemei Zhang & Bin Shi & Jian Cao & Gengui Zhou, 2018. "Collection and Remanufacturing of Waste Products under Patent Protection and Government Regulation," Sustainability, MDPI, vol. 10(5), pages 1-22, May.
    3. Hazen, Benjamin T. & Overstreet, Robert E. & Jones-Farmer, L. Allison & Field, Hubert S., 2012. "The role of ambiguity tolerance in consumer perception of remanufactured products," International Journal of Production Economics, Elsevier, vol. 135(2), pages 781-790.
    4. Juhong Chen & Di Wu & Peng Li, 2018. "Research on the Pricing Model of the Dual-Channel Reverse Supply Chain Considering Logistics Costs and Consumers’ Awareness of Sustainability Based on Regional Differences," Sustainability, MDPI, vol. 10(7), pages 1-31, June.
    5. Xiaoming Li, 2013. "Managing Dynamic Inventory Systems with Product Returns: A Markov Decision Process," Journal of Optimization Theory and Applications, Springer, vol. 157(2), pages 577-592, May.
    6. Agrawal, Saurabh & Singh, Rajesh K. & Murtaza, Qasim, 2015. "A literature review and perspectives in reverse logistics," Resources, Conservation & Recycling, Elsevier, vol. 97(C), pages 76-92.

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