Abstract
Abstract In recent years, there have been an increasing number of extreme weather events that have had major impacts on the built environment and particularly on people living in urban areas. As the frequency and intensity of such events are predicted to increase in the future, innovative response strategies to cope with potential emergency conditions, particularly evacuation planning and management, are becoming more important. Although mass transit evacuation of populations at risk is recognized to play a potentially important role in reducing injury and mortality rates, there is relatively little research in this area. In answering the need for more research in this increasingly important and relatively new field of research, this study proposes a hybrid simulation–optimization approach to maximize the number of evacuees moved from disaster-affected zones to safe locations. In order to improve the efficiency of the proposed optimization approach, a novel multipopulation differential evolution approach based on an opposition-based learning concept is developed. The results indicate that even for large populations the proposed approach can produce high-quality options for decision makers in reasonable computational times. The proposed approach enables emergency decision makers to apply the procedure in practice to find the best strategies for evacuation, even when the time for decision making is severely limited.
Highlights
The past century has witnessed significant changes in Earth’s climate system (Tong & Ebi, 2019)
In answering the need for more research in this increasingly important and relatively new field of research, this study proposes a hybrid simulation–optimization approach to maximize the number of evacuees moved from disaster-affected zones to safe locations
Simheuristics is an innovative and potentially efficient method that integrates simulation into metaheuristicdriven frameworks to take account of uncertainties presented by real-world evacuation problems. This method has never been used in an evacuation context. To address this gap in the extreme weather events (EWEs) disaster management and evacuation research literature and find a fast method in order to reach the most appropriate decision in reasonable time, this paper proposes a new approach for evacuation planning by using a novel framework that was proposed by Juan et al (2015) to take advantages of both simulation and metaheuristic algorithm
Summary
The past century has witnessed significant changes in Earth’s climate system (Tong & Ebi, 2019). The vulnerability of cities to EWEs and the associated challenges are increasingly being recognized (Luu, von Meding, & Mojtahedi, 2019). During the past five decades, the world’s population has doubled and the proportion of people living in urban areas has increased from 36% to 55%. It is estimated by the United Nations that this rate will reach 66% by 2050 (Mandache, 2013). A large number of cities have been built or developed in disaster-prone regions (Handayani, Fisher, Rudiarto, Sih Setyono, & Foley, 2019), and in recent decades natural disasters from EWEs such as Hurricanes Katrina, Rita, and Wilma
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