Title:

Dealing with Ambiguity in Humanitarian Decision-Making

Abstract:

One of the major challenges humanitarian organizations face in response planning is managing the inherent ambiguity and uncertainty of disaster situations. In post-disaster contexts, information from various sources (assessing both the needs of affected populations and the extent of damage in the impacted area) often contains missing elements and inconsistencies, which can hinder effective decision-making. In this talk, I will present a new methodological framework that combines graph clustering and stochastic optimization to support humanitarian decision-makers in analyzing the implications of divergent estimates from multiple data sources and in efficiently integrating these estimates into the decision-making process. The usefulness of the proposed approach is demonstrated through a realistic case study on shelter location planning for internally displaced people (IDPs) in a conflict setting, specifically the Syrian civil war. We use needs assessment data from two reliable sources toestimate shelter requirements in Idleb, a district of Syria. Our case study shows that the framework enables decision-makers to assess the degree of ambiguity in the data and the level of consensus across sources, ultimately supporting better-informed decisions and more effective planning for the delivery of humanitarian aid.

Bio:

Walter Rei is a Professor of Operations Research in the Department of Analytics, Operations, and Information Technologies at the École des Sciences de la Gestion, Université du Québec à Montréal, Canada. He currently holds the Canada Research Chair in Stochastic Optimization of Transport and Logistics Systems and is also a member of the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT). His research focuses on the development of efficient solution methodologies for integer programs and combinatorial optimization models relevant to transportation and logistics problems involving uncertainty.