Multi-Criteria Decision Making in Disaster Preparedness and Response
Sibel Salman, Department of Industrial Engineering, Koç University
Disaster preparedness and response operations are characterized by uncertainty, large-scale impact, and the involvement of multiple decision-makers having multiple criteria. In pre-disaster preparedness, decisions involving the location of emergency response facilities, design of a relief distribution system, prepositioning of relief aid inventory, and strengthening of infrastructure networks need to be taken under uncertainty. In post-disaster response, decisions involve dispatching and routing of first responders, transporting casualties to medical emergency units as well as vital first-aid commodities and emergency personnel to disaster-affected areas, evacuation of people, and clearance of debris in a dynamic setting under evolving information. The objectives optimized are classified under efficiency, effectiveness, and equity. Equity, which aims for the equal distribution of benefits or disutilities, is a major concern in response services where resources are scarce, and timeliness is critical. In the disaster response context, equity attributes such as demand satisfaction rate, arrival time, deprivation cost, travel distance, accessibility, and service level have received attention in the literature. As an example, we consider a stochastic shelter location problem arising in the preparedness stage. We take both efficiency and inequity objectives into account by minimizing a linear combination of (i) the mean distance between opened shelter locations and the locations of the individuals assigned to them, and (ii) Gini’s Mean Absolute Difference of these distances. Furthermore, a chance constraint is defined on the total cost of opening the shelters and their capacity expansion. We develop a stochastic programming model with scenarios for uncertain demand and disruptions in the transportation network, and a Genetic Algorithm (GA) that utilizes a mixed-integer programming subproblem. The GA yields small optimality gaps in a short time for benchmark instances. We run the GA also on Istanbul data to drive insights to guide decision-makers.
Sibel Salman is a Professor at the Industrial Engineering Department of the College of Engineering at Koç University in Istanbul, Turkey. Prior to joining Koç University, she held a faculty position at the Krannert School of Management, Purdue University, USA. She got her Ph.D. in Operations Research from Carnegie Mellon University, USA, and her M.Sc. and B.Sc. degrees from Bilkent University, Turkey. She has published in the areas of disaster and health care logistics, supply chain management, facility location, vehicle routing, network design, production scheduling, approximation algorithms, and combinatorial optimization. She is a department editor for OR Spectrum and an associate editor for Sustainable Analytics and Modeling journals and has been/is in the editorial board of Computers and Operations Research and Production and Operations Management journals. She is on the board of EURO working group on Humanitarian Operations (HOpe) and a member of the EURO WISDOM Forum. Her current research interests are in humanitarian logistics, disaster management, healthcare logistics, facility location, vehicle routing.
Friday, November 5, 2021, 4.00 pm - Zoom Meeting