Distribution of deficient resources in disaster response situation using particle swarm optimization

Abstract Large scale natural disasters have wreaked havoc by claiming countless life along with causing massive destruction to infrastructure, despite all the technological advances, globally. The infrastructural issues in the post-disaster scenario lead to the requirement of immediate evacuation and dissemination of relief/rescue to the victims. This process in turn would help to reduce the fatality and hence, would be effective to reduce risks associated with a natural disaster. However, most of the time, it has been observed that there is a lack of coordination among different relief organizations in resource distribution which begs for the need of an efficiently coordinated resource distribution model. The required model optimally serves all the affected sites with a part of the resource amount always reserved in the Resource pool (RP) to cater emergency needs. In the proposed work, a Resource Allocation Problem (RAP) has been considered for designing such an allocation model for scarce resources like food, water, clothes, medical equipment, and rescue members. The objective is to provide the intended number of resources to all the affected sites while preserving a pre-computed amount of resources in RP for utilizing in the future. The proposed RAP has been implemented for a case study using popular evolutionary techniques like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE) and two other versions of PSO, i.e., PSO_TVAC and APSO. The performances of these five techniques are evaluated considering two factors: a) the quality of optimization and b) time efficacy to reach an optimal/near optimal solution. Finally, out of the five approaches, it has been observed that PSO achieves the best allocation results with maximum convergence speed.