Resource allocation for handling emergencies considering dynamic variations and urban spaces

Abstract

Emergency Response Services (ERS) in the developing countries face a dual challenge of lack of resources and selection of optimal locations to distribute the available resources. Moreover, the exclusion of urban space details in the previous models leads to unfeasible solutions. Due to variations in the travel time and coverage demand throughout the day, static location problem is not sufficient to provide good solutions. Hence, we propose an approach to incorporate dynamic aspects like demand, travel time, and coverage area in developing an asset location model. We also incorporate the influence of urban settlement elements like built-up compactness etc. in the model to find the suitable locations for building mini and large fire stations. Our formulated a mixed integer program tries to maximize the empirical demand coverage by firefighting vehicles. The solution is applied to the southern region of Mumbai. When compared to the existing scenario an approximate increase of 10 to 15% increase in the demand coverage by various vehicles is achieved. It is observed that increasing the resources further increases the demand coverage.