Resumo: O estudo de Sistemas de Atendimento Emergencial -SAE visa encontrar meios de fornecer serviços de
Abstract:The study of EMS aims to find ways to provide effective health services and improve the quality of life of the population while respecting the limitations of available resources. In this context, this paper aims to show the potential of application of the hypercube queueing model using queue priorities with more than one preferential server without using partial backup on SAMU, where the workload is relatively low. To do so, were done some experiments with the hypercube queueing model and future scenario prospection by a case study on the SAMU system from Bauru, Brazil. It was evaluated the impacts of demand increase over the system and the acquisition of a new ambulance was evaluated considering the best options to locate it. Main results show that a 50% demand increase can double mean response times. In contrast, minor increases have a smaller impact over the system, as observed on 5.71% and 13.57% demand increases, where the mean response times raised 5% and 16% respectively. The acquisition of a new ambulance was evaluated in terms of mean response times also. The best location had a 3% lower mean response time, on average. Financial support: CAPES and FAPESP.
We improve the shift-scheduling process by using nonstationary queueing models to evaluate schedules and two heuristics to generate schedules. Firstly, we improved the fitness function and the initial population generation method for a benchmark genetic algorithm in the literature. We also proposed a simple local search heuristic. The improved genetic algorithm found solutions that obey the delay probability constraint more often. The proposed local search heuristic also finds feasible solutions with a much lower computational expense, especially under low arrival rates. Differently from a genetic algorithm, the local search heuristic does not rely on random choices. Furthermore, it finds one final solution from one initial solution, rather than from a population of solutions. The developed local search heuristic works with only one well-defined goal, making it simple and straightforward to implement. Nevertheless, the code for the heuristic is simple enough to accept changes and cope with multiple objectives.
The hypercube model is a useful descriptive tool to evaluate emergency services such as firefighters, police, and emergency medical services where geographically distributed vehicles and personnel serve users in emergencies. This study proposes an extension of the hypercube model to represent a dispatch policy in which advanced equipped servers serve solely life-threatening calls (called dedicated servers). The proposed approach is applied to two case studies of public medical emergency services in two different cities in Brazil and validated with discrete-event simulations. The computational experiments show the proposed model as more sensitive to respond to more life-threatening requests than other hypercube models in the literature, serving more of these requests under increased demand. In addition, to reduce the number of equilibrium equations and, consequently, the computational effort of the hypercube model, an aggregate model is shown based on the grouping of homogeneous servers located in the same station. The aggregation policy does not generate additional losses in the accuracy of the model, as shown through several experiments.
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