Optimized positioning of pre-disaster relief force and assets
Tean, Ee Shen.
Lewis, Theodore G.
MetadataShow full item record
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.
Approved for public release; distribution is unlimited
Showing items related by title, author, creator and subject.
Farlow, Charles R. (Monterey, California. Naval Postgraduate School, 2011-03);The Sacramento region is prone to flooding disasters. This thesis uses an optimization model to recommend where to preposition and/or expand warehouses, health-care personnel, ramp space, and transportation vehicle capacity. ...
Salmeron, Javier; Apte, Aruna (2010);A key strategic issue in pre-disaster planning for humanitarian logistics is the pre-establishment of adequate capacity and resources that enable efficient relief operations. This paper develops a two-stage stochastic ...
Li, Hung-xin (Monterey, California: Naval Postgraduate School, 2015-06);Taiwan is prone to many natural disasters, especially typhoons. This thesis adapts an existing stochastic prepositioning optimization model to create a tool for Taiwan military disaster recovery planners, and then uses ...