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A robust optimization model for production planning of perishable products

Author

Listed:
  • S C H Leung

    (City University of Hong Kong)

  • K K Lai

    (City University of Hong Kong
    Hunan University)

  • W-L Ng

    (City University of Hong Kong)

  • Y Wu

    (University of Southampton)

Abstract

In this study, a robust optimization model is developed to solve production planning problems for perishable products in an uncertain environment in which the setup costs, production costs, labour costs, inventory costs, and workforce changing costs are minimized. Using the concept of postponement, the production process for perishable products is differentiated into two phases to better utilize the resources. By adjusting penalty parameters, decision-makers can determine an optimal production loading plan and better utilize resources while considering different economic growth scenarios. A case from a Hong Kong plush toy company is studied and the characteristics of perishable products are discussed. Numerical results demonstrate the robustness and effectiveness of the proposed model. An analysis of the trade-off between solution robustness and model robustness is also presented.

Suggested Citation

  • S C H Leung & K K Lai & W-L Ng & Y Wu, 2007. "A robust optimization model for production planning of perishable products," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(4), pages 413-422, April.
    • Handle: RePEc:pal:jorsoc:v:58:y:2007:i:4:d:10.1057_palgrave.jors.2602159
    DOI: 10.1057/palgrave.jors.2602159
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. Varas, Mauricio & Maturana, Sergio & Pascual, Rodrigo & Vargas, Ignacio & Vera, Jorge, 2014. "Scheduling production for a sawmill: A robust optimization approach," International Journal of Production Economics, Elsevier, vol. 150(C), pages 37-51.
    3. Jahani, Hamed & Abbasi, Babak & Alavifard, Farzad & Talluri, Srinivas, 2018. "Supply chain network redesign with demand and price uncertainty," International Journal of Production Economics, Elsevier, vol. 205(C), pages 287-312.
    4. Borodin, Valeria & Bourtembourg, Jean & Hnaien, Faicel & Labadie, Nacima, 2016. "Handling uncertainty in agricultural supply chain management: A state of the art," European Journal of Operational Research, Elsevier, vol. 254(2), pages 348-359.
    5. Jabbarzadeh, Armin & Haughton, Michael & Pourmehdi, Fahime, 2019. "A robust optimization model for efficient and green supply chain planning with postponement strategy," International Journal of Production Economics, Elsevier, vol. 214(C), pages 266-283.
    6. Behzadi, Golnar & O’Sullivan, Michael Justin & Olsen, Tava Lennon & Zhang, Abraham, 2018. "Agribusiness supply chain risk management: A review of quantitative decision models," Omega, Elsevier, vol. 79(C), pages 21-42.
    7. Shuihua Han & Yue Jiang & Ling Zhao & Stephen C. H. Leung & Zongwei Luo, 2020. "Weight reduction technology and supply chain network design under carbon emission restriction," Annals of Operations Research, Springer, vol. 290(1), pages 567-590, July.

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