Laura A. McLay

Priority dispatching strategies for EMS systems

Emergency medical service (EMS) systems provide urgent medical care and transport. In this study we implement dispatching policies for EMS systems that incorporate the severity of the call in order to increase the survival probability of patients. A simulation model is developed to evaluate the performance of EMS systems. Performance is measured in terms of patients’ survival probability, since survival probability more directly mirrors patient outcomes. Different response strategies are evaluated utilizing several examples to study the nature of the optimal dispatching policy. The results show that dispatching the closest vehicle is not always optimal and dispatching vehicles considering priority of the call leads to an increase in the average survival probability of patients. A heuristic algorithm, that is easy to implement, is developed to dispatch ambulances for large-scale EMS systems. Computational examples show that the dispatching algorithm is valuable in increasing the patients’ survival probability.

Comparison of genetic algorithm to particle swarm for constrained simulation-based optimization of a geothermal power plant

The performance of a genetic algorithm is compared with that of particle swarm optimization for the constrained, non-linear, simulation-based optimization of a double flash geothermal power plant. Particle swarm optimization converges to better (higher) objective function values. The genetic algorithm is shown to converge more quickly and more tightly, resulting in a loss of solution diversity. Particle swarm optimization obtains solutions within 0.1% and 0.5% of the best known optimum in significantly fewer objective function evaluations than the genetic algorithm.

Reducing disparities in large-scale emergency medical service systems

Emergency Medical Service (EMS) systems operate under the pressure of knowing that human lives might be directly at stake. In the public eye there is a natural expectation of efficient response. There is abundant literature on the topic of efficient planning of EMS systems (maximizing expected coverage or minimizing response time). Other objectives have been considered but the literature available is very sparse compared to efficiency-based works. Furthermore, while real size EMS systems have been studied, the use of exact models is usually hindered by the amount of computational time required to obtain solutions. We approach the planning of large-scale EMS systems including fairness considerations using a Tabu Search-based heuristic with an embedded approximation procedure for the queuing submodel. This allows for the analysis of large-scale real systems, extending the approach in which strategic decisions (location) and operative decisions (dispatching) are combined to balance efficiency and fairness.

The minimum p-envy location problem with requirement on minimum survival rate

In location problems for the public sector such as emergency medical service (EMS) systems, the issue of equity is an important factor for facility design. Several measures have been proposed to minimize inequity of a system. This paper considers an extension to the minimum p-envy location model by evaluating the objective of the model based on a survival function instead of on a distance function since survival probability is directly related to patient outcomes with a constraint on minimum survival rate. The model was tested on a real world data set from the EMS system at Hanover County, VA, and also compared to other location models. The results indicate that, not only does the enhanced p-envy model reduce inequity but we also find that more lives can be saved by using the survival function objective. A sensitivity analysis on different quality of service measures (survival probability and traveled distance) and different choices of priority assigned to serving facility is discussed.

Infrastructure Systems, Risk Analysis, and Resilience—Research Gaps and Opportunities

This Special Issue is aimed at risk analysts addressing risks and resilience in infrastructure systems. Modern infrastructure systems offer unprecedented convenience and service levels. They are critical to the economic vitality, security, health, and environmental protection of cities, regions, and nations. However, there are knowledge gaps concerning vulnerabilities, risk, and resilience of these systems. The articles in this Special Issue help fill some of these pressing knowledge gaps. The Special Issue includes the following contributions. Fang et al. describe mathematical models for understanding and preventing cascading failures in infrastructure networks. They are primarily concerned with power transmission networks and consider how to allocate generation to distributors by reorganizing links to maximize network resilience to cascading failure and minimizing investment costs. The combinatorial multiobjective optimization is carried out by a nondominated sorting binary differential evolution (NSBDE) algorithm. For each generator–distributor connection pattern considered in the NSBDE search, a computationally cheap, topological model of failure cascading in a complex system is used to simulate and quantify network resilience to cascading failures initiated by targeted attacks. The results on the 400-kV French power transmission network case study show that the proposed method allows the authors to identify optimal patterns of generator– distributor connection that improve cascading resilience at an acceptable cost. To verify the realism of the results obtained by the NSBDE with an

A maximum expected covering problem for locating and dispatching two classes of military medical evacuation air assets

Military medical evacuation (MEDEVAC) systems respond to casualty incidents and transport the most urgent casualties to a medical treatment facility via multiple types of air ambulance assets. Military MEDEVAC systems are subject to an uncertain number of service calls and each service call demands different system operations depending on type and the priority level. Therefore, military medical planners need an air MEDEVAC asset management system that determines how to dispatch multiple types of air assets to prioritized service calls to maintain a high likelihood of survival of the most urgent casualties. To reach this goal, we propose a novel binary linear programming (BLP) model to optimally locate two types of air assets and construct response districts using a dispatch preference list. Additionally, the BLP model balances the workload among assets and enforces contiguity in the first assigned locations for each air asset. The objective of the BLP model is to maximize the proportion of high-priority casualties responded to within a pre-specified time threshold while meeting performance benchmarks to other types of casualties. A spatial queuing approximation model is derived to provide inputs to the BLP model, which thus reflects the underlying queuing dynamics of the system. We illustrate the model and algorithms with a computational example that reflects realistic military data.