Discrete Optimization and Agent-Based Simulation for Regional Evacuation Network Design Problem

Natural disasters and extreme events are often characterized by their violence and unpredictability, resulting in consequences that in severe cases result in devastating physical and ecological damage as well as countless fatalities. In August 2005, Hurricane Katrina hit the Southern coast of the United States wielding serious weather and storm surges. The brunt of Katrina?s force was felt in Louisiana, where the hurricane has been estimated to total more than $108 billion in damage and over 1,800 casualties. Hurricane Rita followed Katrina in September 2005 and further contributed $12 billion in damage and 7 fatalities to the coastal communities of Louisiana and Texas. Prior to making landfall, residents of New Orleans received a voluntary, and then a mandatory, evacuation order in an attempt to encourage people to move themselves out of Hurricane Katrina?s predicted destructive path. Consistent with current practice in nearly all states, this evacuation order did not include or convey any information to individuals regarding route selection, shelter availability and assignment, or evacuation timing. This practice leaves the general population free to determine their own routes, destinations and evacuation times independently. Such freedom often results in inefficient and chaotic utilization of the roadways within an evacuation region, quickly creating bottlenecks along evacuation routes that can slow individual egress and lead to significant and potentially dangerous exposure of the evacuees to the impending storm.

One way to assist the over-burdened and over-exposed population during extreme event evacuation is to provide an evacuation strategy that gives specific information on individual route selection, evacuation timing and shelter destination assignment derived from effective, strategic pre-planning. For this purpose, we present a mixed integer linear program to devise effective and controlled evacuation networks to be utilized during extreme event egress. To solve our proposed model, we develop a solution methodology based on Benders Decomposition and test its performance through an experimental design using the Central Texas region as our case study area. We show that our solution methods are efficient for large-scale instances of realistic size and that our methods surpass the size and computational limitations currently imposed by more traditional approaches such as branch-and-cut. To further test our model under conditions of uncertain individual choice/behavior, we create an agent-based simulation capable of modeling varying levels of evacuee compliance to the suggested optimal routes and varying degrees of communication between evacuees and between evacuees and the evacuation authority.

By providing evacuees with information on when to evacuate, where to evacuate and how to get to their prescribed destination, we are able to observe significant cost and time increases for our case study evacuation scenarios while reducing the potential exposure of evacuees to the hurricane through more efficient network usage. We provide discussion on scenario performance and show the trade-offs and benefits of alternative batch-time evacuation strategies using global and individual effectiveness measures. Through these experiments and the developed methodology, we are able to further motivate the need for a more coordinated and informative approach to extreme event evacuation.