Dissertation (Ph.D.)--University of South Florida, 2009. / Title from PDF of title page. Document formatted into pages; contains 295 pages. Includes vita. Includes bibliographical references.

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.

Augmented reality (AR) is an emerging technology with immense potential for enhancing human-to-human interaction tasks, particularly in high-risk environments such as mass casualty incident (MCI) tri-age. However, developing practical and effective AR tools for this purpose necessitates a meticulous user-centered design (UCD) process, thoughtfully crafted and validated through iterative testing with first responders in increasingly contextually relevant simulations. In academic circles, the perceived complexity and time requirements of such a process might discourage its adoption within the constraints of traditional publishing cycles. This is likely due, in part, to a lack of representative applied UCD examples. This work addresses this challenge by presenting a scholarly UCD framework tailored specifically for MCI triage, which progresses seamlessly from controlled, context-free laboratory settings to virtual patient simulations and finally to realistic patient (actor) scenarios. Moreover, MCIs and triage are under-served areas, likely due to their high intensity and risk. This means developers need to 'get it right' as quickly as possible. UCD and evaluation alone are not an efficient means to developing these complex and dangerous work domains. Thus, this research also delves into a cognitive work analysis, offering a comprehensive breakdown of the MCI triage domain and how those findings inform future AR sup-ports. This analysis serves to fortify the foundation for future UCD endeavors in this critical space. Finally, it is imperative to recognize that MCI triage fundamentally involves human-to-human interaction supported by AR technology. Therefore, UCD efforts must encompass a diverse array of study stimuli and participants to ensure that the technology functions as intended across all demographic groups. It is established that racial bias exists in emergency room triage, creating worse outcomes for patients of color. Consequently, this study also investigates the potential impact of racial biases on MCI triage efficacy. This entire body of work has implications for UCD evaluation methodology, the development of future AR support tools, and the potential to catch racially biased negative performance before responders ever hit the field. / Doctor of Philosophy / Augmented reality (AR) is uniquely situated to make work within high-risk work environments, like mass casualty incident (MCI) response, safer and more effective. This is because AR augments the user's reali-ty with context-relevant information, like by providing a temperature gauge for firefighters that is always in their visual field. Development of such AR tools for a sensitive arena like MCIs requires several rigor-ous steps before those tools can be deployed in the field. It is crucial to engage in a user-centered design (UCD) process in partnership with actual emergency responders so they can help us understand what help they need. We outline that UCD process in Chapter 2. Once we understand what responders say they need help with, we then need to evaluate those pinch points in the broader context of their work. This means that we evaluate how their job process creates the situation where responders need the kind of help they are asking for. Understanding this helps us create solutions that address the responder's needs while we minimize any new problems created with implementing a new tool into the job. What we learned from examining the work domain is described in Chapter 3. Once we have this firm foundational understanding of responder needs and work and we have designed an AR support tool, we need to evaluate that tool for effectiveness. It is too dangerous to put the AR tool straight into the field, so Chapter 4 explores how we can create simulations of an MCI scenario to study our AR support tool. Finally, after evaluating our AR tool within the scenarios and the scenarios themselves, we evaluate (in Chapter 5) other facets of the job that may be impacting MCI response. In our case, we explore how racial bias may be impacting patient care. It is important to study bias as it has implications for future MCI training and AR tool development. Perhaps future work can explore an AR tool that offsets bias-based performance, or a training that helps catch bias before responders ever get to the real field.

In this thesis, we explore three critical emergency logistics problems faced by healthcare and humanitarian relief service providers for short-term post-disaster management. In the first manuscript, we investigate various integration mechanisms (fully integrated horizontal-vertical, horizontal, and vertical resource sharing mechanisms) following a natural disaster for a multi-type whole blood-derived platelets, multi-patient logistics network. The goal is to reduce the amount of shortage and wastage of multi-blood-group of platelets in the response phase of relief logistics operations. To solve the logistics model for a large scale problem, we develop a hybrid exact solution approach involving an augmented epsilon-constraint and Lagrangian relaxation algorithms and demonstrate the model's applicability for a case study of an earthquake. Due to uncertainty in the number of injuries needing multi-type blood-derived platelets, we apply a robust optimization version of the proposed model which captures the expected performance of the system. The results show that the performance of the platelets logistics network under coordinated and integrated mechanisms better control the level of shortage and wastage compared with that of a non-integrated network. In the second manuscript, we propose a two-stage casualty evacuation model that involves routing of patients with different injury levels during wildfires. The first stage deals with field hospital selection and the second stage determines the number of patients that can be transferred to the selected hospitals or shelters via different routes of the evacuation network. The goal of this model is to reduce the evacuation response time, which ultimately increase the number of evacuated people from evacuation assembly points under limited time windows. To solve the model for large-scale problems, we develop a two-step meta-heuristic algorithm. To consider multiple sources of uncertainty, a flexible robust approach considering the worst-case and expected performance of the system simultaneously is applied to handle any realization of the uncertain parameters. The results show that the fully coordinated evacuation model in which the vehicles can freely pick up and off-board the patients at different locations and are allowed to start their next operations without being forced to return to the departure point (evacuation assembly points) outperforms the non-coordinated and non-integrated evacuation models in terms of number of evacuated patients. In the third manuscript, we propose an integrated transportation and hospital capacity model to optimize the assignment of relevant medical resources to multi-level-injury patients in the time of a MCI. We develop a finite-horizon MDP to efficiently allocate resources and hospital capacities to injured people in a dynamic fashion under limited time horizon. We solve this model using the linear programming approach to ADP, and by developing a two-phase heuristics based on column generation algorithm. The results show better policies can be derived for allocating limited resources (i.e., vehicles) and hospital capacities to the injured people compared with the benchmark. Each paper makes a worthwhile contribution to the humanitarian relief operations literature and can help relief and healthcare providers optimize resource and service logistics by applying the proposed integration and coordination mechanisms.

This dissertation investigates the value of deep reinforcement learning (DRL) within an agent-based model (ABM) of a large open-air venue. The intent is to reduce civilian casualties in an active shooting incident (ASI). There has been a steady increase of ASIs in the United States of America for over 20 years, and some of the most casualty-producing events have been in open spaces and open-air venues. More research should be conducted within the field to help discover policies that can mitigate the threat of a shooter in extremis. This study uses the concept of dynamic signage, controlled by a DRL policy, to guide civilians away from the threat and toward a safe exit in the modeled environment. It was found that a well-trained DRL policy can significantly reduce civilian casualties as compared to baseline scenarios. Further, the DRL policy can assist decision makers in determining how many signs to use in an environment and where to place them. Finally, research using DRL in the ASI space can yield systems and policies that will help reduce the impact of active shooters during an incident.