Exact and heuristic solution algorithms for efficient emergency evacuation in areas with vulnerable populations. (October 2019) - Dulebenets, Maxim A.; Pasha, Junayed; Abioye, Olumide F.; Kavoosi, Masoud; Ozguven, Eren E.; Moses, Ren; Boot, Walter R.; Sando, Thobias
Abstract: Proper emergency evacuation planning is a key to ensure safety and efficiency of transportation networks in the event of approaching natural hazards. A sound evacuation plan can save human lives and avoid congestion. In order to develop effective emergency evacuation plans, this study presents a mixed-integer programming model that assigns individuals, including vulnerable population groups, to emergency shelters through evacuation routes during the available time periods. The objective of the mathematical model is to minimize the total travel time of individuals leaving an evacuation zone. Unlike many emergency evacuation models presented in the literature, the proposed mathematical model directly accounts for the effects of socio-demographic characteristics of evacuees, evacuation route characteristics, driving conditions, and traffic characteristics on the travel time of evacuees. An exact optimization approach and a set of heuristic approaches are applied to yield solutions for the developed model. The numerical experiments are conducted for emergency evacuation of Broward County (Florida, United States). The results show that the exact optimization approach cannot tackle the large-size problem instances. On the other hand, the proposed heuristic algorithms are able to provide good-quality solutions within a reasonable computational time. Therefore, the developed mathematical model and heuristic algorithms can further assist the appropriate agencies with efficient and timely emergency evacuation planning. Highlights: A mathematical model is developed for emergency evacuation. The objective aims to minimize the total travel time of individuals. A wide range of factors is captured by the adopted travel time function. The exact and heuristic solution algorithms are proposed to solve the model. Numerical experiments demonstrate important managerial insights.