Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study
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Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study Soheyl Khalilpourazari1,5 · Shima Soltanzadeh2 · Gerhard‑Wilhelm Weber3,6 · Sankar Kumar Roy4
© Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract In recent years, attention to blood supply chain in disaster circumstances has significantly increased. Disasters, especially earthquakes, have adverse consequences such as destruction, loss of human lives, and undermining the effectiveness of health services. This research considers a six-echelon blood supply chain which consists of donors, blood collection centers (permanent and temporary), regional blood centers, local blood centers, regional hospitals, and local hospitals. For the first time, we considered that helicopters could carry blood from regional hospitals to local hospitals and return injured people that cannot be treated in local hospitals to regional hospitals due to the limited capacity. In addition to the above, different transportations with limited capacities regarded, where the optimal number of required transportations equipment determined after the solution process. This research aims to avoid the worst consequences of a disaster using a neural-learning process to gain from past experiences to meet new challenges. For this aim, this article considers three objective functions that are minimizing total transportation time and cost while minimizing unfulfilled demand. The model implemented based on a real-world case study from the most recent earthquake in the Iran–Iraq border which named the deadliest earthquake of 2017. Based on our results, we learned how to design an efficient blood supply chain that can fulfill hospitals blood demand quickly with the lowest cost using simulation and optimization processes. Moreover, we performed in-depth analyses and provided essential managerial insights at last. Keywords Blood supply chain · Disaster management · Humanitarian relief · Lexicographic weighted Tchebycheff method · Neural learning · Operational research · Multi-objective programming
* Soheyl Khalilpourazari [email protected] Extended author information available on the last page of the article
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Vol.:(0123456789)
Annals of Operations Research
1 Introduction Attention to blood supply chain in disaster situations has significantly increased in recent years. Disasters, especially earthquakes, have adverse consequences such as destruction, loss of human lives, and undermining the effectiveness of health services. Earthquake means the perceptible vibrations in the earth’s crust, caused by sudden energy release as a result of rocks’ rupture under underground which leads to seismic waves (Khalilpourazari and Khamseh 2017; Yerlikaya-Özkurt et al. 2014, 2016). A significant challenge after a destructive earthquake is how to provide blood to injured people in hospitals. According to global statistics, Iran is one of the most earthquake-prone countries that faces many destructive earthquakes every
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