An agent-based model for resource allocation during relief distribution

Abstract

Purpose – The purpose of this paper is to propose a model for allocating resources in various zones after a large-scale disaster. This study is motivated by the social dissatisfaction caused by inefficient relief distribution. Design/methodology/approach – This study introduces an agent-based model (ABM) framework for integrating stakeholders’ interests. The proposed model uses the TOPSIS method to create a hierarchy of demand points for qualitative and quantitative parameters. A decomposition algorithm has been proposed to solve fleet allocation. Findings – Relief distribution based on the urgency of demand points increases social benefit. A decomposition approach generates higher social benefit than the enumeration approach. The transportation cost is lower in the enumeration approach. Research limitations/implications – This study does not consider fleet contracts explicitly, but rather assumes a linear cost function for computing transportation costs. Practical implications – The outcomes of this study can be a valuable tool for relief distribution planning. This model may also help reduce the social dissatisfaction caused by ad hoc relief distribution. Originality/value – This study introduces an ABM for humanitarian logistics, proposes a decomposition approach, and explores the ontology of stakeholders of humanitarian logistics specific to last-mile distribution.