Emergency managers are often faced with the toughest decisions that can ever be made, people's lives hang in the balance. Nevertheless, these tough decisions have to be made, and made quickly. There is usually too much information to process to make the best decisions. Decision support systems can relieve a significant amount of this onus, making decision while considering the complex interweaving of constraints and resources that define the boundary of the problem. We study these complex emergency management, approaching the problem with discrete optimization. Using our operational research knowledge to model mass casualty incidents, we seek to provide solutions and insights for the emergency managers. This dissertation proposes a novel deterministic model to optimize the casualty transportation and treatment decisions in response to a MCI. This deterministic model expands on current state of the art by; (1) including multiple dynamic resources that impact the various interconnected decisions, (2) further refining a survival function to measure expected survivors, (3) defining novel objective functions that consider competing priorities, including maximizing survivors and balancing equity, and finally (4) developing a MCI response simulation that provides insights to how optimization models could be used as decision-support mechanisms. / Doctor of Philosophy / Emergency managers are often faced with the toughest decisions that can ever be made, people's lives hang in the balance. Nevertheless, these tough decisions have to be made, and made quickly. But to make the best decisions, there is usually too much information to process. Computers and support tools can relieve a significant amount of this onus, making decision while considering the complex interweaving of constraints and resources that define the boundary of the problem. This dissertation provides a mathematical model that relates the important decisions made during a MCI response with the limited resources of the surrounding area. This mathematical model can be used to determine the best response decisions, such as where to send casualties and when to treat them. This model is also used to explore ideas of fairness and equity in casualty outcomes and examine what may lead in unfair response decisions. Finally, this dissertation uses a simulation to understand how this model could be used to not only plan the response, but also update the plan as you learn new information during the response roll-out.