A Biased Random-Key Genetic Algorithm for the Rescue Unit Allocation and Scheduling Problem

Abstract

The occurrence of a disaster brings about damages, destruction, ecological disruption, loss of human life, human suffering, deterioration of health and health service of sufficient magnitude to require external assistance, demanding the mobilization and deployment of emergency rescue units within the affected area, in order to reduce casualties and economic losses. The scheduling of those units is one of the key issues in the emergency response phase and can be seen as a generalization of the unrelated parallel machine scheduling problem with sequence and machine dependent setup. The objective is to minimize the total weighted completion time of the incidents to be attended, where the weight correspond to its severity level. We propose a biased random-key genetic algorithm to tackle this problem, considering fuzzy required processing times for the incidents, and compare the solutions with those generated by a constructive heuristic, from the literature, developed to deal with this problem. Our results show that the genetic algorithm's solutions are 2.17% better than those obtained with the constructive heuristic when applied to instances with up to 40 incidents and 40 rescue units.