Abstract
Article history: Received April 4 2016 Received in Revised Format April 27 2016 Accepted May 12 2016 Available online May 14 2016 In this paper, a multi-period model for blood supply chain in emergency situation is presented to optimize decisions related to locate blood facilities and distribute blood products after natural disasters. In disastrous situations, uncertainty is an inseparable part of humanitarian logistics and blood supply chain as well. This paper proposes a robust network to capture the uncertain nature of blood supply chain during and after disasters. This study considers donor points, blood facilities, processing and testing labs, and hospitals as the components of blood supply chain. In addition, this paper makes location and allocation decisions for multiple post disaster periods through real data. The study compares the performances of “p-robust optimization” approach and “robust optimization” approach and the results are discussed. © 2016 Growing Science Ltd. All rights reserved
Highlights
Natural disasters like earthquake, flood, and famine cause many problems around the world annually
In disaster management, mathematical modeling approach was used for marine disasters in 1980s
Recent disasters have shown that blood supply chain and its effective operation services are affected by outer disruption (Jabbarzadeh et al, 2014)
Summary
Flood, and famine cause many problems around the world annually. One of the most useful applications in this respect is mathematical modeling approach that has helped affected countries’ governments during natural disasters (Sheu, 2007). Recent disasters have shown that blood supply chain and its effective operation services are affected by outer disruption (Jabbarzadeh et al, 2014). By considering aforesaid uncertain and dynamic nature of blood demand, this study develops a dynamic optimization model by using robust stochastic approach for determining the number and the location of blood facilities, and specifying inventory levels in hospitals at the end of each period. While our model has considered real situations, it will help decision makers implement location and allocation decisions during disasters. The last section presents concluding and remarks some directions for future researches in respect
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