Integrating an Agent-Based Model with a Multi-Objective Evolutionary Algorithm to Warn Consumers in a Water Contamination Event Using Emergency Vehicles

Many protective response actions are available to protect consumers affected by a water distribution contamination event. Routing a fleet of emergency vehicles to disseminate water warnings is considered in this study. Identifying optimal trips to effectively notice consumers is difficult as a water contamination event is a complex and dynamic system and vehicle routing problems are categorized as NP-hard domain. A novel framework is developed by coupling an agent-based model with an optimization algorithm to search for optimality in a warning dissemination. Solutions representing an optimal front are explored to demonstrate the relationship between the number of exposed consumers and the maximum travel time as conflicting objectives. NSGA-II is used with a tree-based representation. The developed framework is applied to a virtual case study, Mesopolis.