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论文类型：期刊论文

发表时间：2018-01-01

发表刊物：INTERNATIONAL JOURNAL OF LOGISTICS MANAGEMENT

收录刊物：SSCI、Scopus

卷号：29

期号：4,SI

页面范围：1279-1305

ISSN号：0957-4093

关键字：North America; Metaheuristics; Decision-making; Modelling; Asia; Logistics strategy; Humanitarian logistics; Disaster medical assistance team routing; Computational study

摘要：Purpose The purpose of this paper is to solve a post-disaster humanitarian logistics problem in which medical assistance teams are dispatched and the relief supplies are distributed among demand points.
   Design/methodology/approach A mixed integer-programming model and a two-stage hybrid metaheuristic method are developed to solve the problem. Problem instances of various sizes as well as a numerical example based on the 2016 Kyushu Earthquake in Japan are used to test the proposed model and algorithm.
   Findings Computational results based on comparisons with the state-of-the-art commercial software show that the proposed approach can quickly find near-optimal solutions, which is highly desirable in emergency situations.
   Research limitations/implications Real data of the parameters of the model are difficult to obtain. Future collaborations with organizations such as Red Cross and Federal Emergency Management Agency can be extremely helpful in collecting data in humanitarian logistics research.
   Practical implications The proposed model and algorithm can help governments and non-governmental organizations (NGOs) to effectively and efficiently allocate and coordinate different types of humanitarian relief resources, especially when these resources are limited.
   Originality/value This paper is among the first ones to consider both medical team scheduling (routing) and relief aid distribution as decision variables in the humanitarian logistics field. The contributions include developing a mathematical model and a heuristic algorithm, illustrating the model and algorithm using a numerical example, and providing a decision support tool for governments and NGOs to manage the relief resources in disasters.