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A stochastic programming approach for multi-site aggregate production planning

Author

Listed:
  • S C H Leung

    (City University of Hong Kong)

  • Y Wu

    (University of Southampton)

  • K K Lai

    (City University of Hong Kong
    Hunan University)

Abstract

Production planning problems play a vital role in the supply chain management area, by which decision makers can determine the production loading plan—consisting of the quantity of production and the workforce level at each production plant—to fulfil market demand. This paper addresses the production planning problem with additional constraints, such as production plant preference selection. To deal with the uncertain demand data, a stochastic programming approach is proposed to determine optimal medium-term production loading plans under an uncertain environment. A set of data from a multinational lingerie company in Hong Kong is used to demonstrate the robustness and effectiveness of the proposed model. An analysis of the probability distribution of economic demand assumptions is performed. The impact of unit shortage costs on the total cost is also analysed.

Suggested Citation

  • S C H Leung & Y Wu & K K Lai, 2006. "A stochastic programming approach for multi-site aggregate production planning," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(2), pages 123-132, February.
    • Handle: RePEc:pal:jorsoc:v:57:y:2006:i:2:d:10.1057_palgrave.jors.2601988
    DOI: 10.1057/palgrave.jors.2601988
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Tang, Christopher S. & Davarzani, Hoda & Sarkis, Joseph, 2015. "Quantitative models for managing supply chain risks: A reviewAuthor-Name: Fahimnia, Behnam," European Journal of Operational Research, Elsevier, vol. 247(1), pages 1-15.
    2. Donya Rahmani & Arash Zandi & Sara Behdad & Arezou Entezaminia, 2021. "A light robust model for aggregate production planning with consideration of environmental impacts of machines," Operational Research, Springer, vol. 21(1), pages 273-297, March.
    3. M S Sodhi & C S Tang, 2011. "Determining supply requirement in the sales-and-operations-planning (S&OP) process under demand uncertainty: a stochastic programming formulation and a spreadsheet implementation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 526-536, March.
    4. Caner TaskIn, Z. & Tamer Ünal, A., 2009. "Tactical level planning in float glass manufacturing with co-production, random yields and substitutable products," European Journal of Operational Research, Elsevier, vol. 199(1), pages 252-261, November.
    5. Sodhi, ManMohan S. & Tang, Christopher S., 2009. "Modeling supply-chain planning under demand uncertainty using stochastic programming: A survey motivated by asset-liability management," International Journal of Production Economics, Elsevier, vol. 121(2), pages 728-738, October.
    6. M Kazemi Zanjani & M Nourelfath & D Ait-Kadi, 2011. "Production planning with uncertainty in the quality of raw materials: a case in sawmills," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(7), pages 1334-1343, July.
    7. Shih-Pin Chen & Wen-Lung Huang, 2014. "Solving Fuzzy Multiproduct Aggregate Production Planning Problems Based on Extension Principle," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2014, pages 1-18, August.
    8. Sun, Jing & Yamamoto, Hisashi & Matsui, Masayuki, 2020. "Horizontal integration management: An optimal switching model for parallel production system with multiple periods in smart supply chain environment," International Journal of Production Economics, Elsevier, vol. 221(C).
    9. Gupta, Narain & Dutta, Goutam & Fourer, Robert, 2014. "A Multi-Period Two Stage Stochastic Programming Based Decision Support System for Strategic Planning in Process Industries: A Case of an Integrated Iron and Steel Company," IIMA Working Papers WP2014-04-04, Indian Institute of Management Ahmedabad, Research and Publication Department.
    10. Huo, Baofeng & Gu, Minhao & Jiang, Bin, 2018. "China-related POM research: Literature review and suggestions for future research," International Journal of Production Economics, Elsevier, vol. 203(C), pages 134-153.

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