In this thesis, we propose an optimization model to assist the Region of Waterloo Emergency Medical Services (EMS) to meet the new provincial land ambulance response time standard. The new land standard requires multiple response time thresholds which are based on the acuity of the patient determined at the time the 911 call is made. The performance of an EMS system is affected by many factors, including the number of ambulances deployed, their locations, and the dispatching strategy that is employed. The number of ambulances available over the course of the day varies when ambulance crews start and end their shifts, and when ambulance crews are called out or return from a call. In order to maintain coverage, it is therefore desirable to locate ambulances in stations as a function of how many are available, and the geography and frequency of potential calls. This may result in relocation of ambulances whenever there is a change in the number of available vehicles. This research provides a compliance table indicating how many ambulances to locate at each station when the number of available ambulances is given. We explore two main objectives: 1) maximizing the expected coverage for all patients, and 2) maximizing the coverage for the most acutely ill patients. Constraints include the number of available ambulances, the response time requirements, and service level constraints for each acuity level. In this study, we conducted an empirical analysis of ambulance response times, travel times to a hospital, and time spend at the hospital. We used two years of EMS data from July 2006 to June 2008 for the Region of Waterloo (ROWEMS). Based on this study, we show that using the binomial distribution to represent the number of busy ambulances suggested by Gendreau et al. (2006) is only valid for low utilization rates. The problem of allocating available ambulances among candidate stations is formulated as a Mixed Integer Non-linear Problem (MINLP) model that includes the priority of calls and multiple daytime periods. Computational results using the ROWEMS data will be presented. A detailed comparison shows that the predictions obtained from our model are often as good as the Approximate Hypercube (AH) model, but with a simpler and quicker procedure. The model proposed in this thesis can also be used as a planning tool to find promising candidate locations for new ambulance stations.

In this thesis, we propose an optimization model to assist the Region of Waterloo Emergency Medical Services (EMS) to meet the new provincial land ambulance response time standard. The new land standard requires multiple response time thresholds which are based on the acuity of the patient determined at the time the 911 call is made. The performance of an EMS system is affected by many factors, including the number of ambulances deployed, their locations, and the dispatching strategy that is employed. The number of ambulances available over the course of the day varies when ambulance crews start and end their shifts, and when ambulance crews are called out or return from a call. In order to maintain coverage, it is therefore desirable to locate ambulances in stations as a function of how many are available, and the geography and frequency of potential calls. This may result in relocation of ambulances whenever there is a change in the number of available vehicles. This research provides a compliance table indicating how many ambulances to locate at each station when the number of available ambulances is given. We explore two main objectives: 1) maximizing the expected coverage for all patients, and 2) maximizing the coverage for the most acutely ill patients. Constraints include the number of available ambulances, the response time requirements, and service level constraints for each acuity level. In this study, we conducted an empirical analysis of ambulance response times, travel times to a hospital, and time spend at the hospital. We used two years of EMS data from July 2006 to June 2008 for the Region of Waterloo (ROWEMS). Based on this study, we show that using the binomial distribution to represent the number of busy ambulances suggested by Gendreau et al. (2006) is only valid for low utilization rates. The problem of allocating available ambulances among candidate stations is formulated as a Mixed Integer Non-linear Problem (MINLP) model that includes the priority of calls and multiple daytime periods. Computational results using the ROWEMS data will be presented. A detailed comparison shows that the predictions obtained from our model are often as good as the Approximate Hypercube (AH) model, but with a simpler and quicker procedure. The model proposed in this thesis can also be used as a planning tool to find promising candidate locations for new ambulance stations.

Made available in DSpace on 2016-06-02T19:50:24Z (GMT). No. of bitstreams: 1 TeseAPI.pdf: 1231033 bytes, checksum: 51120574caaa9608efc8725f12321b14 (MD5) Previous issue date: 2005-03-11 / Financiadora de Estudos e Projetos / The purpose of this study is to develop effective methods to analyze the configuration and operation of the emergency medical systems (EMS) on highways. Due to the stochastic nature of these systems, especially in the arrival and assistance processes of the emergency calls, we apply the Hypercube Queuing Model to evaluate the performance measures of the system. This is a well-known model in the location literature, which is based on spatially distributed queuing theory. The EMS on highways operate within a particular dispatching policy which considers that only some ambulances in the system can travel to certain regions (partial backup) and multiple dispatch of ambulances to respond to certain calls. In this study we extend the Hypercube model to deal with these situations. Since the Hypercube model is a descriptive model, we also develop a Hypercube embedded genetic algorithm to create a prescriptive approach to optimize the configuration and operation of EMS on highways. This approach can support decisions at the strategic level, for example, the location of ambulances along the highway and the primary response area to each ambulance, as well as, decisions on the operational level, for example, the optimal dispatch policy of ambulances to respond to the emergency calls and the coverage area to each ambulance (if the system configuration can be modified according to the operational conditions of the week or the day). In order to evaluate the performance of the proposed approach, we conducted experiments using the data of two realsystems: the EMS Anjos do Asfalto (Presidente Dutra highway) and EMS Centrovias (portions of the highways Washington Luis, Eng. Paulo Nilo Romano e Comandante João Ribeiro de Barros) in São Paulo State. The results show that the approach is effective to support planning and operation decisions in such systems. / O objetivo deste trabalho é desenvolver métodos efetivos para analisar a configuração e operação de sistemas de atendimento emergencial (SAEs) em rodovias. Devido às características estocásticas de tais sistemas, principalmente nos processos de chegada e atendimento dos chamados de emergência, aplicamos o modelo Hipercubo para analisar as medidas de desempenho do sistema. Este modelo, conhecido na literatura de localização de sistemas de emergência, é baseado em teoria de filas espacialmente distribuídas. Os SAEs em rodovia operam com uma política de despacho particular, a qual admite que apenas algumas ambulâncias do sistema possam viajar a determinadas regiões (backup parcial) e utiliza múltiplo despacho de ambulâncias para atender a certas chamadas. Neste trabalho estendemos o modelo Hipercubo para analisar tais situações. Como o modelo Hipercubo é descritivo, combinamos estas extensões do modelo Hipercubo com um algoritmo genético para obter uma abordagem prescritiva capaz de otimizar a configuração e operação de SAEs em rodovias. Tal abordagem pode ser útil para apoiar decisões no plano estratégico, por exemplo, a localização das bases das ambulâncias ao longo da rodovia e o dimensionamento das regiões de cobertura de cada base. Assim como apoiar decisões no plano operacional, por exemplo, a escolha da política de despacho das ambulâncias para atender chamados de urgência e a determinação das áreas de cobertura de cada servidor (quando a configuração do sistema puder ser alterada de acordo com as condições operacionais de uma semana ou de um dia). Para analisar o desempenho desta abordagem, realizamos estudos de casos com dados reais do sistema Anjos do Asfalto (rodovia Presidente Dutra) e da concessionária Centrovias (trechos das rodovias Washington Luis, Eng. Paulo Nilo Romano e Comandante João Ribeiro de Barros), no interior de São Paulo. Os resultados mostram que a abordagem é efetiva para apoiar decisões relacionadas ao planejamento e operação destes sistemas.