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A bi-objective robust possibilistic programming model for blood supply chain design in the mass casualty event response phase: a M/M/1/K queuing model with real world application

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  • Mahsa Pouraliakbari-Mamaghani
  • Mohammad Mohammadi
  • Alireza Arshadi-Khamseh
  • Bahman Naderi

Abstract

This paper presents a bi-objective blood supply chain design model for disaster relief. The model aims to simultaneously minimise the total costs of the supply chain, the trade-off between the total expected waiting times of patients in hospitals and the average hospital idle-time probability, unsatisfied demands and the average delivery time from mobile blood facilities to healthcare centres as the first, second, third and fourth objective functions. Since the critical parameters are tainted with great degree of epistemic uncertainty, basic chance constraint programming (BCCP) and robust fuzzy chance constraint programming (RFCCP) are utilised to deal with the uncertain nature of the supply chain. In order to solve the proposed model, two different multiple objective decision making approaches are used. The applicability of the proposed model for earthquake response phase is demonstrated via a real case study in a region of Iran. Useful managerial insights are also provided through conducting some sensitivity analyses.

Suggested Citation

  • Mahsa Pouraliakbari-Mamaghani & Mohammad Mohammadi & Alireza Arshadi-Khamseh & Bahman Naderi, 2021. "A bi-objective robust possibilistic programming model for blood supply chain design in the mass casualty event response phase: a M/M/1/K queuing model with real world application," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 42(2), pages 229-275.
    • Handle: RePEc:ids:ijores:v:42:y:2021:i:2:p:229-275
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    Cited by:

    1. Ali Ala & Morteza Yazdani & Mohsen Ahmadi & Aida Poorianasab & Mahdi Yousefi Nejad Attari, 2023. "An efficient healthcare chain design for resolving the patient scheduling problem: queuing theory and MILP-ASA optimization approach," Annals of Operations Research, Springer, vol. 328(1), pages 3-33, September.

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