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  1. Opioid overdose rescue is very time-sensitive. Hence, drone-delivered naloxone has the potential to be a transformative innovation due to its easily deployable and flexible nature. We formulate a Markov Decision Process (MDP) model to dispatch the appropriate drone after an overdose request arrives and to relocate the drone to its next waiting location after having completed its current task. Since the underlying optimization problem is subject to the curse of dimensionality, we solve it using ad-hoc state aggregation and evaluate it through a simulation with higher granularity. Our simulation-based comparative study is based on emergency medical service data from themore »state of Indiana. We compare the optimal policy resulting from the scaled-down MDP model with a myopic policy as the baseline. We consider the impact of drone type and service area type on outcomes, which offers insights into the performance of the MDP suboptimal policy under various settings.« less
  2. Emergency medical service must be time sensitive. However, in many cases, satisfactory service cannot be ensured due to inconvenient logistics. For its easily deployable and widely accessible nature, unmanned aerial vehicles (UAVs) have the potential to improve the service, especially in areas that are traditionally under-served. In this paper, we develop a service network optimization problem for locating UAV bases, staffing a UAV fleet at each constructed base, and zoning demand nodes. We formulate a location-allocation optimization model with numerically simulated waiting times for the service zones as the objective. We adapt a genetic algorithm to solve the optimization model.more »We test our network optimization approach on instances of traumatic injury cases. By comparing our approach to a two-phase method in Boutilier et al. [1], we suggest an up to 60% reduction in mean waiting time.« less
  3. Trauma continues to be the leading cause of mortality and morbidity among US citizens aged <44 years. Literature suggests that geographical maldistribution of trauma centers (TCs) is associated with increasing fatality rate. Existing models for TC network design do not address the question often raised by trauma decision makers: how many TCs are required to achieve acceptable levels of mistriages? We propose a model to optimize the network of TCs under mistriage constraints. We propose a notional field triage protocol to estimate mistriages (under and over), based on existing guidelines in the trauma literature. Due to the complexity of themore »underlying model, we propose a Particle Swarm Optimization based solution approach. We use 2012 data from the State of Ohio, and model both ground and air transportation modes. Our results show that, for 2012 mistriage levels, it is possible to reduce the number of TCs from 21 to 10 by distributing them appropriately across urban and rural areas. Further, redistributing these 21 TCs can help satisfy the recommendation of under-triage ≤0.05 by the American College of Surgeons. In general, our study provides trauma decision makers an ability to determine a network that could improve care and/or reduce cost.« less