Identifying the best design configuration for a first-response team is important for minimizing total operation time and reducing the human cost of natural and manmade disasters. This paper presents ongoing research that focuses on a disaster multiagent coordination simulation (DMCsim) system that is able to optimally design the first-response team and evaluate the team design configuration before initiation of a search and rescue operation. We developed an agent-based simulation system that uses machine learning techniques and design of experiments methods to test different configuration setups and determine the effects of various factors on operation completion time. The evaluation of a team design for a disaster-response operation revealed that some design factors have a significant effect on operation outcome. Removing the effects of uncontrollable factors, such as damage level and robot reliability, yielded a robust team design that could function in a particular disaster environment regardless of the effects of such factors. The DMCsim assists decision makers to evaluate an emergency-response operation, revise the current strategy based on resources on hand, redesign the available team, and visually track operation performance before launching the actual team in the disaster field. This research extends previous disaster response coordination systems by proposing a new simulation model and evaluating a first-response team design. K E Y W O R D S agent-based simulation, decision support system, design of experiments, first-response team design, machine learning, system optimization, system performance 322

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