Title: A new stochastic model in emergency location problem

Authors: Farshid Esmaeili Kakhki; Zahra Naji-Azimi; Alireza Pooya; Ahmad Tavakoli

Addresses: Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad (FUM), Mashhad, Iran ' Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad (FUM), Mashhad, Iran ' Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad (FUM), Mashhad, Iran ' Deceased, formerly of Ferdowsi University of Mashhad (FUM), Iran

Abstract: The problem of emergency location in natural disasters, especially in earthquakes, has been considered in recent decades. In this paper, we consider the demand for emergency shelters as a stochastic parameter and we propose a new hybrid approach for emergency location-allocation problem. This new approach incorporates GIS, system dynamics, Coburn and Spence model, stochastic programming and Monte Carlo simulation. The proposed hybrid model is implemented in a real example of Mashhad City. The scenarios of earthquake have been built based on the maximum speed of the important faults and the time of occurrence. By considering the seismic cycle in Mashhad City, a four year-time horizon for earthquake occurrence has been considered. The results of this hybrid model show that if the south fault of Mashhad is activated in the greatest severity, it has the highest possible casualties, in which more than 45% of the residents will lose their lives. Furthermore, 223 emergency locations have been specified for survivors. Moreover, the results indicate that the solutions of Monte Carlo simulation are improved and converged by increasing the number of scenarios.

Keywords: emergency location; two-stage stochastic programming; system dynamics; Monte Carlo simulation; Coburn and Spence model; GIS.

DOI: 10.1504/IJOR.2024.137134

International Journal of Operational Research, 2024 Vol.49 No.3, pp.285 - 310

Received: 19 Sep 2020
Accepted: 02 Jun 2021
Published online: 04 Mar 2024 *

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