Risk approaches for delivering disaster relief supplies

Authors:
Pamela C. Nolz;Frédéric Semet;Karl F. Doerner
Affiliations:
Ecole des Mines de Saint-Etienne, CMP Georges Charpak, Gardanne, France 13541 and Department of Business Administration, University of Vienna, Vienna, Austria 1210;Ecole Centrale de Lille, LAGIS, Cité Scientifique, Villeneuve d'Ascq, France 59651;Department of Business Administration, University of Vienna, Vienna, Austria 1210 and Institute of Production and Logistics Management, Johannes Kepler University Linz, Linz, Austria 4040
Venue:
OR Spectrum
Year:
2011

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Abstract

We consider the problem of designing the logistic system to assure adequate distribution of relief aid in a post-natural-disaster situation, when damages to infrastructure may disrupt the delivery of relief aid. The problem is formulated as a multi-objective optimization problem, encompassing three objective functions of central interest in such problems. The first objective function is a measure of risk (various forms of such risk are analyzed). The second objective function measures the coverage provided by the logistic system in the distribution of relief aid to disaster victims. The third objective function represents total travel time. We focus on the risk of delivery tours for relief supplies, where risk here captures the threat that potential tours become impassable after the natural hazard event. In order to cope with a range of different natural disasters and policy objectives, we develop five approaches emphasizing different measures of tour-dependent risk. To cover both earthquake and flood risks, we consider correlated as well as uncorrelated risk measures. We develop a two-phase solution approach to reflect the dictates of real-world disaster relief motivating this analysis. The first phase generates potentially Pareto-optimal solutions to the overall multi-objective logistic design problem with respect to three objectives. For any given risk measure, the first-phase design problem is formulated as a multi-objective integer program and a memetic algorithm is proposed as the solution approach. The second phase is an enrichment procedure to generate a broader range of potentially Pareto-optimal alternatives. The suggested approach is tested on real-world data from the province of Manabí in Ecuador and the results associated with the different risk measures are analyzed to illustrate the value of the proposed approach for the design of disaster relief networks.