A comprehensive evacuation planning model and genetic solution algorithm

• A multi-criteria macroscopic evacuation planning problem is introduced.
• Both individual and public traffic are considered, as well as location decisions.
• Objectives are the number of shelters, evacuation time, and risk.
• A mixed-integer program and a genetic algorithm are proposed.
• Using real-world instances, the trade-off using data aggregation is discussed.

We consider the problem of evacuating an urban area. Several planning aspects need to be considered in such a scenario, which are usually considered separately. We propose a macroscopic multi-criteria optimization model that includes several such questions simultaneously, and develop a genetic algorithm to solve the problem heuristically. Its applicability is extended by also considering how to aggregate instance data, and how to generate solutions for the original instance starting from a reduced solution. In computational experiments using real-world data, we demonstrate the effectiveness of our approach and compare different levels of data aggregation.