Title: Relief Supply Chain Planning under Uncertainty: Fair allocation of in-kind donations in post-disaster phase
Date: 8th of November , 2024 (Friday)
Time: 13:30-14:30
Place: EA409
Abstract
Disaster response aims to address the immediate needs of the affected populations quickly in highly uncertain circumstances. In disaster relief supply chains, the demand comes from disaster victims (typically considered as internally displaced populations), while the supply mainly consists of in-kind donations. This study focuses on finding a fair mechanism to distribute a scarce relief item among a set of demand points under donation uncertainty.
The problem is cast as a multi-period location-inventory problem with a coverage constraint. The uncertain supply parameters are integrated into the model using a multi-stage stochastic programming (MSSP) approach. A set of mobile facilities, called points of distribution (PoDs), is used to distribute the collected supply. In particular, two decisions are made for every period of the planning horizon: (i) where to locate a limited number of mobile PoDs and (ii) what quantity to deliver to each demand node from each PoD.
In search for fair solutions, the so-called deprivation cost is considered, which measures the “suffering” of a population lacking a critical item. A modeling framework is proposed for the problem and tested using instances built from real data available in the literature. Overall, the results show that the proposed models can better support the decision-making process when fairness is of relevance.
Short bio
Zehranaz Dönmez Varol received her Ph.D in 2024 from Bilkent University, Department of Industrial Engineering. Her dissertation, titled “Fair Allocation of In-Kind Donations in Post-Disaster Phase”, addresses the problem of equitably distributing scarce resources with uncertain availability in the aftermath of a disaster through the use of mobile points of distribution. Her research interests include humanitarian logistics problems, equitable resource allocation, and stochastic programming.