An agent-based model for resource allocation during relief distribution

Rubel Das, Shinya Hanaoka

    Research output: Contribution to journalArticlepeer-review

    19 Citations (Scopus)

    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.

    Original languageEnglish
    Pages (from-to)265-285
    Number of pages21
    JournalJournal of Humanitarian Logistics and Supply Chain Management
    Volume4
    Issue number2
    DOIs
    Publication statusPublished - 2014 Oct 7

    Keywords

    • Agent-based model
    • Decomposition approach
    • Humanitarian logistics
    • Resource allocation
    • TOPSIS

    ASJC Scopus subject areas

    • Management Information Systems
    • Management Science and Operations Research

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