Mestrado em Modelagem e Otimização - PPGMO

URI Permanente para esta coleção

https://repositorio.ufcat.edu.br/handle/tede/40
O Mestrado em Modelagem e Otimização tem um papel essencial no desenvolvimento de novas tecnologias. Atualmente, se faz muito intensamente o uso de modelos matemáticos, simulações avançadas e sofisticados desenvolvimentos computacionais na pesquisa científica em geral.

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Navegando Mestrado em Modelagem e Otimização - PPGMO por Autor "Camargo, Victor Claudio Bento de"

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    Aplicação de uma abordagem robusta no problema de localização de ambulâncias com estudo de caso na cidade de Catalão - Goiás
    (Universidade Federal de Goiás, 2016-07-05) Marques, Raina Ribeiro; Queiroz, Thiago Alves de; http://lattes.cnpq.br/8041183668335400; Queiroz, Thiago Alves de; Camargo, Victor Claudio Bento de; Borges, Romes Antônio
    The robust optimization techniques can be used in problems subject to uncertainty in order to obtain robust solutions, that is, solutions that are less sensitive to the problem variations. Problems such as the facility location, specifically, the location of ambulances, have uncertainty in your data. Thus, an integer linear programming model for allocation of ambulances and stations is investigated considering that the service time is an uncertainty parameter, since this parameter is influenced by the nature of the call, traffic, or distance traveled, for example. It is proposed a model considering the application of a robust approach that controls the amount of uncertainty parameters related with the service time. A case study with real data provided by the fire department of the city of Catalão, Goiás, is performed on the models and the results show that the number of ambulances is greater than the current need, as pointed by the model without uncertainty. However, the results on the robust model show that the real number of ambulances in the city is able to serve a limited amount of demand, so for a maximum variation of the demand, the number of available ambulances are not able to support it. The model had worked well for the first two scenarios among the three ones tested, in which for the last scenario the model was quite sensitive to changes on the uncertainty parameters.