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A reactive phase against disruptions for designing a proactive platelet supply network

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  • Samani, Mohammad Reza Ghatreh
  • Hosseini-Motlagh, Seyyed-Mahdi
  • Homaei, Shamim

Abstract

Previous studies in blood supply chain network design often follow a commonly used approach in protecting the chain against disruptions, considering the effects of disruptions on the designing phase. However, in many real-world situations, disruptions cannot be adequately measured in advance. Moreover, using disruptions in the designing phase through the common two-stage stochastic programming models impose high costs on the network, since they cannot be updated based on unpredicted disruptions. This paper proposes an updatable two-phase approach which deals with disruptions in the operational phase, not in the strategic design phase. In the first step, called the proactive phase, a nominal platelet supply chain network is designed under operational uncertainty, using the whole-blood collection method. In the event of disruptions, the second step, called the reactive phase, is applied, and the tailored network is updated based on the realized data, using apheresis as the collection mechanism. The operational risks are captured using a fuzzy programming approach in the model. Based on the real data from Fars province of Iran, we compare the performance of the two-phase approach with the commonly used approaches in the literature, resulting in more flexible decisions, and consequently, less conservatism degree rather than the existing approaches.

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  • Samani, Mohammad Reza Ghatreh & Hosseini-Motlagh, Seyyed-Mahdi & Homaei, Shamim, 2020. "A reactive phase against disruptions for designing a proactive platelet supply network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).
  • Handle: RePEc:eee:transe:v:140:y:2020:i:c:s1366554520306591
    DOI: 10.1016/j.tre.2020.102008
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    7. Sohrabi, Mahnaz & Zandieh, Mostafa & Shokouhifar, Mohammad, 2023. "Sustainable inventory management in blood banks considering health equity using a combined metaheuristic-based robust fuzzy stochastic programming," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
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