The problem of covering location of relief centers in behavior of humanitarian logistic management with UAV-based rescue operations under conditions of uncertainty

Subject Areas : Behavioral Studies in Management

amin  foroughi ¹ (PhD student, Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Babak  Farhang Moghaddam ² (Associate Professor, Institute for Management and Planning Studies (IMPS), Tehran, Iran)
Mohammad Hassan  Behzadi ³ (Associate Professor, Department of Statistics, Science and Research Unit, Islamic Azad University, Tehran, Iran)
Farzad  Movahedi Sobhani ⁴ (Assistant Professor, Department of Industrial Engineering, Science and Research Unit, Islamic Azad University, Tehran, Iran.)

Received: 2023-02-15 Accepted : 2023-03-15 Published : 2023-06-22

Keywords: Uncertainty, humanitarian logistics management, UAV-based rescue operations,

Abstract :

Due to the nature of the unexpected and the magnitude of the events, the variety and the amount of supplies and services needed by the victims, natural disasters require immediate mobilization and action of multiple stakeholders. Crisis relief operations focus on planning the transportation of first aid materials, food, equipment and rescue personnel from supply points to a large number of destination nodes that are geographically dispersed throughout the crisis area and the rapid evacuation and transfer of people affected by the crisis to centers. Medicine and safe shelters. Therefore, one of the most important and efficient issues in humanitarian transportation is the issue of routing the relief vehicle. In this article, the location problem of dense fuzzy facilities with multi-level capacity in UAV-based rescue operations is studied. The purpose of this problem is to determine the best places to set up and refuel the stations so that the waiting time of the victims to receive the needed assistance does not exceed a certain threshold. Due to the non-deterministic nature of the crisis, fuzzy demand is considered in the proposed model, then this fuzzy model is converted into a deterministic model using the Jimenez method. Finally, the proposed mathematical model was solved using NSGA-II algorithm and the results were presented.