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Collaborative hospital supply chain network design problem under uncertainty

Author

Listed:
  • Khouloud Dorgham

    (Universite d’Artois)

  • Issam Nouaouri

    (Universite d’Artois)

  • Jean-Christophe Nicolas

    (Universite d’Artois)

  • Gilles Goncalves

    (Universite d’Artois)

Abstract

Since 2016, hospitals in France have met to form Territorial Hospital Groups (THGs) in order to modernize their health care system. The main challenge is to allow an efficient logistics organization to adopt the new collaborative structure of the supply chain. In our work, we approach the concept of logistics pooling as a form of collaboration between hospitals in THGs. The aim is to pool and rationalize the storage of products in warehouses and optimize their distribution to care units while reducing logistics costs (transportation, storage, workforce, etc.). Besides, due to the unavailability and the incompleteness of data in real-world situations, several parameters embedded in supply chains could be imprecise or even uncertain. In this paper, a Fuzzy chance-constrained programming approach is developed based on possibility theory to solve a network design problem in a multi-supplier, multi-warehouse, and multi-commodity supply chain. The problem is designed as a minimum-cost flow graph and a linear programming optimization model is developed considering fuzzy demand. The objective is to meet the customers’ demand and nd the best allocation of products to warehouses. Different instances were generated based on realistic data from an existing territorial hospital group, and several tests were developed to reveal the benefits of collaboration and uncertainty handling.

Suggested Citation

  • Khouloud Dorgham & Issam Nouaouri & Jean-Christophe Nicolas & Gilles Goncalves, 2022. "Collaborative hospital supply chain network design problem under uncertainty," Operational Research, Springer, vol. 22(5), pages 4607-4640, November.
  • Handle: RePEc:spr:operea:v:22:y:2022:i:5:d:10.1007_s12351-022-00724-y
    DOI: 10.1007/s12351-022-00724-y
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    References listed on IDEAS

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    1. Mohamadreza Fazli-Khalaf & Soheyl Khalilpourazari & Mohammad Mohammadi, 2019. "Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design," Annals of Operations Research, Springer, vol. 283(1), pages 1079-1109, December.
    2. Groothedde, Bas & Ruijgrok, Cees & Tavasszy, Lóri, 2005. "Towards collaborative, intermodal hub networks: A case study in the fast moving consumer goods market," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 41(6), pages 567-583, November.
    3. Wanke, Peter F. & Saliby, Eduardo, 2009. "Consolidation effects: Whether and how inventories should be pooled," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(5), pages 678-692, September.
    4. Joon-Seok Kim & Saif Benjaafar, 2002. "On the Benefits of Inventory-Pooling in Production-Inventory Systems," Manufacturing & Service Operations Management, INFORMS, vol. 4(1), pages 12-16.
    5. Shuangyan Li & Xialian Li & Dezhi Zhang & Lingyun Zhou, 2017. "Joint Optimization of Distribution Network Design and Two-Echelon Inventory Control with Stochastic Demand and CO2 Emission Tax Charges," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-22, January.
    6. Quddus, Md Abdul & Chowdhury, Sudipta & Marufuzzaman, Mohammad & Yu, Fei & Bian, Linkan, 2018. "A two-stage chance-constrained stochastic programming model for a bio-fuel supply chain network," International Journal of Production Economics, Elsevier, vol. 195(C), pages 27-44.
    7. Lin, Cheng-Chang & Wang, Tsai-Hsin, 2011. "Build-to-order supply chain network design under supply and demand uncertainties," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1162-1176, September.
    8. Xuejie Bai & Yankui Liu, 2016. "Robust optimization of supply chain network design in fuzzy decision system," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1131-1149, December.
    9. Elçi, Özgün & Noyan, Nilay, 2018. "A chance-constrained two-stage stochastic programming model for humanitarian relief network design," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 55-83.
    10. K Iwamura & B Liu, 1998. "Chance constrained integer programming models for capital budgeting in fuzzy environments," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 49(8), pages 854-860, August.
    11. A. Charnes & W. W. Cooper, 1959. "Chance-Constrained Programming," Management Science, INFORMS, vol. 6(1), pages 73-79, October.
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