Enhancing evacuation response to extreme weather disasters using public transportation systems: A novel simheuristic approach

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.