Decision support for disaster relief: Coordinating spontaneous volunteers

Abstract

In the aftermath of large-scale disasters, the exploitation of often up to thousands of spontaneous volunteers is crucial to meet the need for surge capacity which cannot be met by official responders. However, the coordination of spontaneous volunteers differs in several regards from that of professional and paid relief workers. Based on empirical requirements identified in interviews with the manager of a professional fire department, we suggest a multi-objective mixed-integer linear optimization problem with lexicographically ordered objective functions, which we refer to as spontaneous volunteer coordination problem (SVCP). Acknowledging that disaster situations are unavoidably linked to uncertainty, we consider uncertainty with a sequence of (deterministic) SVCP instances, where each instance depends on the solutions of previous SVCP instances. We conduct comprehensive computational experiments based on real-world data of a flood disaster that the fire department faced. From our computational results, we derive detailed implications for the fire department on how to use our decision support model. We also derive recommendations for all relief organizations which aim at adopting or adapting our model for the coordination of spontaneous volunteers in a broad set of disasters. Our implications include several recommendations for relief organizations in terms of performing extensive computational tests in order to parameterize and instantiate the generic model before its use during the disaster response phase; thereby we also address tasks to be executed during the preparedness phase of a disaster.