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Friday, March 8, 2019 at 12:15pm
Emergency disaster managers are concerned with responding to disasters in a timely and effective manner. The effectiveness of response operations can be improved by prepositioning relief supplies in anticipation of disasters. We study the problem of determining the location and amount of disaster relief supplies to be prepositioned. These supplies are stocked when the locations of affected areas and the amount of relief items needed are uncertain. Furthermore, a proportion of the prepositioned inventory, which is also uncertain, might be damaged by the disaster.
We propose a two-stage robust optimization model. The location and amount of prepositioned relief supplies are decided in the first stage before a disaster occurs. In the second stage, a limited amount of relief supplies can be procured post-disaster and prepositioned supplies are distributed to affected areas. The objective is to minimize the total cost of supplying disaster relief materials. We solve the proposed robust optimization model using a column-and-constraint generation algorithm. Two optimization criteria are considered: total absolute cost and regret. A case study of the hurricane season in the southeast US is used to gain insights on the effects of optimization criteria and critical model parameters to relief supply prepositioning strategy.
Maria E. Mayorga is a Professor of Personalized Medicine in the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University. She received her M.S. and Ph.D. degrees in industrial engineering and operations research from the University of California, Berkeley. Her research interests include predictive models in health care, health care operations management, emergency response, and humanitarian logistics. She has authored over 65 publications in archival journals and refereed proceedings. Her research has been supported by NIH and NSF, among others. She received the distinguished National Science Foundation CAREER Award for her work to incorporate patient choice into predictive models of health outcomes. She is a member of INFORMS and the Institute of Industrial & Systems Engineers, and serves on the editorial board for the journals IISE Transactions on Healthcare Systems Engineering and INFORMS Journal on Computing.