An Operations Research Approach for Daily Emergency Management

Emergency services play a vital role in society by providing help to affected people and minimizing damage to public and private assets as well as the environment during emergencies. However, these organizations deal with problems of increasing demand uncertainty and resource shortage over time. These problems lead to the creation of many other problems, such as longer response times, lower survivability of victims and patients, and more severe damage to properties and the environment. Acquiring more information about future emergency demand, such as factors affecting this demand, can contribute to reduction of the effects of increasing demand uncertainty. The introduction of volunteers as a new type of emergency resource, which has gained attention in the past few years, can be a solution to the problem of increasing resource shortage. The aim of this thesis is to provide operations research-based models and methods that can assist medical emergency services in daily emergency management. The aim is supported by two objectives: 1) to develop a forecasting model and 2) to develop models for the dispatch of volunteers. Three separate studies with a focus on these objectives are conducted, and the results are described in three papers. In the first paper, a forecasting model for predicting the volume of ambulance calls per hour and geographic location for three counties in Sweden is presented. The model takes into consideration geographical zones with few or no population and very low call frequency. Comparative results based on the real data of ambulance calls show that the proposed model performs better than the model that is currently used in some parts of Sweden for operational and tactical planning of emergency medical services. In addition to performance improvement, the proposed model provides information about the factors affecting ambulance demand. In the second paper, the use of volunteers in response to out-of-hospital cardiac arrest (OHCA) cases is considered, and a deterministic optimization model for their dispatch is provided. The model benefits from a survival function for determining dispatch decisions. The effect of arrival times of volunteers on the survivability of patients is also considered. The results show that, in terms of achieved survivability of patient based on the applied survival function, the proposed model performs better than simple decision rules used today. The third paper presents a probabilistic method for the dispatch of volunteers to OHCA cases. This method considers the uncertainties associated with the actions of volunteers once they are assigned a task. The proposed method uses a survival function as the objective of dispatch decisions. The results of the method are compared to the static dispatch method that is currently used in an operational system in Sweden for the utilization of volunteers in OHCA cases. Comparative results based on real data show that, with respect to used survival function, the proposed method contributes to higher survivability of OHCA patients than the static dispatch method. The models and method in this thesis focus on solving real-world problems and use real data for that purpose when available. Some simplifications were considered in the development process. Nevertheless, these models and method have the potential to be beneficial for medical emergency services in practice and can be used as a base for dynamic resource management systems. Such systems can be helpful for both tactical and operational planning of emergency resources