Optimized positioning of pre-disaster relief force and assets

Abstract

Recent events in the United States of America and Pakistan have exposed the shortcomings of existing planning in relief and humanitarian assistance in the face of large-scale natural disasters. This thesis develops a two-stage stochastic optimization model to provide guidance in the pre-positioning of relief units and assets, where budget, physical limitations and logistics are taken into account. Stochastic data include the numbers of survivors in each potential affected area (AA), the amount of commodities that needs to be delivered to each AA and the transportation time from each relief location (which reflects sceanrios where, for example, roads are blocked). As first-stage decisions, we consider the expansion of warehouses, medical facilities and their health care personnel, as well as ramp space to facilitate aircraft supply of commodities to the AAs. The second-stage is a logistic problem respresented as a network, where maximizing expected rescued survivors and delivery of required commodities are the driving goals. This is accomplished through land, air and sea transportation means (e.g., CH-53 helicopters configured for rescue missions), as well as relief workers. The model has been successfully assessed on notional scenarios and is expected to be tested on realistic cases by personnel who are involved in relief planning.