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http://dx.doi.org/10.11627/jkise.2021.44.1.026

A Mixed-Integer Programming Model for Effective Distribution of Relief Supplies in Disaster  

Kim, Heungseob (Department of Industrial and Systems Engineering/Department of Smart Manufacturing Engineering Changwon National University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.44, no.1, 2021 , pp. 26-36 More about this Journal
Abstract
The topic of this study is the field of humanitarian logistics for disaster response. Many existing studies have revealed that compliance with the golden time in response to a disaster determines the success or failure of relief activities, and logistics costs account for 80% of the disaster response cost. Besides, the agility, responsiveness, and effectiveness of the humanitarian logistics system are emphasized in consideration of the disaster situation's characteristics, such as the urgency of life-saving and rapid environmental changes. In other words, they emphasize the importance of logistics activities in disaster response, which includes the effective and efficient distribution of relief supplies. This study proposes a mathematical model for establishing a transport plan to distribute relief supplies in a disaster situation. To determine vehicles' route and the amount of relief for cities suffering a disaster, it mainly considers the urgency, effectiveness (restoration rate), and uncertainty in the logistics system. The model is initially developed as a mixed-integer nonlinear programming (MINLP) model containing some nonlinear functions and transform into a Mixed-integer linear programming (MILP) model using a logarithmic transformation and piecewise linear approximation method. Furthermore, a minimax problem is suggested to search for breakpoints and slopes to define a piecewise linear function that minimizes the linear approximation error. A numerical experiment is performed to verify the MILP model, and linear approximation error is also analyzed in the experiment.
Keywords
Humanitarian Logistics; Disaster Response; Mixed-Integer Programming Model; Linear Approximation; Weapon-Target Assignment;
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