A robust optimization model for blood supply chain in emergency situations

Abstract

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.Recent disasters have shown that blood supply chain and its effective operation services are affected by outer disruption (Jabbarzadeh et al., 2014). For example, for the case of Bam earthquake, because of 1. the number and the location of permanent and mobile facilities, 2. the allocation of facilities to donation points, 3. the allocation of hospitals to labs, 4. The blood inventory in each hospital.This section is divided into two parts. First, we present a robust optimization formulation and its related model that incorporates different disaster scenarios for the values of critical input data and then, in the second part, we introduce the p-robust model which incorporates different scenarios for possible disruptions after earthquake occurrence.

Incorporating a Bayesian Network into Two-Stage Stochastic Programming for Blood Bank Location-Inventory Problem in Case of Disasters

“…Rahmani [25] proposes a robust model for the BSN design by using p-criterion technique to protect the solution against the risk of disruptions. Similar works can refer to Shishebori and Babadi [26], Zahiri et al [27], Fereiduni and Shahanaghi [28], Ramezanian and Behboodi [29], Samani and Hosseini-Motlagh [30], and Kamyabniya et al [31]. These works indicate that RO can provide robust solutions to unexpected disasters.…”

“…RO, which does not require a probabilistic distribution, only considers extreme scenarios and thus may lead to conservative decisions. Recently, only few BLN-related works, such as Fereiduni and Shahanaghi [28], Fahimnia et al [20], and Salehi et al [24], notice the importance of considering multiple disasters-related factors. However, the uncertainties considered in these works are still limited and their relationships are also ignored.…”

Fuzzy Emergency Model and Robust Emergency Strategy of Supply Chain System under Random Supply Disruptions

“…In recent years, scholars and practitioners have devoted study to the emergency management issues of supply chain [1][2][3][4][5][6][7][8][9]. Fereiduni and Shahanaghi [1] presented a multiperiod model for the blood supply chain in an emergency situation to optimize decisions related to locating blood facilities and distributing blood products after natural disasters. Li [5] set up an evaluation system for responding capacity of the emergency supply chain.…”

“…0 and 1 are Distributor 2's safety inventory and expected inventory, respectively. Let 1 1 = 2 1 = 1 1 , 3 1 = 4 1 = 2 1 , 1 2 = 3 2 = 1 2 , 2 2 = 4 2 = 2 2 , 0 = 10, 1 = 50, 0 = 15 and 1 = 60 (×10 5 ton). Figure 2 shows there exists one MORG called 1 (including 1 , 2 , 3 and 4 ).…”

A dynamic leader-follower model based on lack of central authority in emergency situations

“…Both human and financial loss resulted from natural disasters led scholars to come up with a branch in management called disaster management. In 1980s, the first steps to mathematically formulate marine disasters were taken (Fereiduni & Shahanaghi, 2016). Humanitarian logistics received a lot of attention right after starting the twenty-first century.…”