A stochastic multi-objective model based on the classical optimal search model for searching for the people who are lost in response stage of earthquake

Abstract

Although after an earthquake, the injured person should be equipped with food, shelter, and hygiene activities, before anything must be searched and rescued. However, Disaster Management (DM) has focused heavily on emergency logistics and developing an e ective strategy for search operations has been largely ignored. In this study, we suggest a stochastic multi-objective optimization model to allocate resource and time for searching the individuals who are trapped in disaster regions. Since in disaster conditions, the majority of information is uncertain, our model assumes ambiguity for the locations where the missing people may exist. Fortunately, the suggested model ts nicely into the structure of the classical optimal search model as it uses a stochastic dynamic programming approach to solving this problem. On the other hand, through a computational experiment, we observed that the model needed heavy computation. Therefore, we reformulated the suggested search model for a Multi-Criteria Decision Making (MCDM) problem and employed two e cient MCDM techniques, namely TOPSIS and COPRAS, to tackle the ranking problem. Consequently, the computational e ort signi cantly decreased and a promising solution was achieved.

Solving a new bi-objective model for relief logistics in a humanitarian supply chain by bi-objective meta-heuristic algorithms

“…This model was solved with a heuristic method in conjunction with the Lp-metric method and implemented on a flood case study in an urban district in Iran. Sotoudeh-Anvari et al [37] suggested a stochastic multi-objective optimization model for trapped people in disaster regions with uncertainty for the locations, in which any survivor may be found. This model was tried to solve with a stochastic dynamic programming approach.…”

A framework for Earthquake Emergency Response in Iran

“…[38] uses statistical techniques to analyze public awareness about natural disasters and propose several methods for reducing casualties. The last group of studies such as [40,41] focuses on affected people rescue. They attempt to use a mathematical model to improve the performance of resources utilized for searching trapped in disaster regions or the time required for rescuing injured people.…”

A new MCDM-based approach using BWM and SAW for optimal search model

“…Search for lost or hidden objects happens for various real cases. Widespread applications are detection of secret hostile targets, detection of land mine and rescuing lost people (Kriheli et al, 2016;Sotoudeh-Anvari et al, 2018). ST has various models and among them, optimal search model (Ross, 1983) is one of the most broadly used forms (Benkoski et al, 1991).…”

“…But usually these approaches are complex and need significant amount of computations (Benkoski et al, 1991;Kadane, 2015;Stone, 1976;Levner, 1994). On the other hand, first, Sotoudeh-Anvari et al (2018) formulated the optimal search problem as an MCDM problem. The aim of MCDM model of search problem is to rank the locations in the presence of some decision factors such as search cost, search time, the probability of detecting and overlook probability.…”