Akademik Veri Yönetim Sistemi
Tezin Türü: Yüksek Lisans
Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü, Türkiye
Tezin Onay Tarihi: 2013
Öğrenci: SİNE TAYMAZ
Eş Danışman: CEM İYİGÜN, ZEYNEP PELİN BAYINDIR
Özet:This study discusses the problem of finding the optimal location of “walk-in clinics” specialized in healthcare along the transportation lines that would enable maximum coverage along the roads for the mobile populations and their related local communities. As the mobile populations are flowing on the routes unremittingly, the problem differs from other location problems. Every member of the mobile population would require a specialized service for their diseases and needs to access these services in a continuous manner along the roads, without any disruption. Therefore, the location of clinics should be adapted regarding these requirements and maximum continuum of care should be ensured for the demand populations. Additionally, as a results of the uncertain nature of the mobile demand, the risk associated with the lack of continuum of care provided to the population is an important component in the problem. While ensuring maximum level of continuum of care, the risk involved in the transportation lines which appears as a variation in mobile demand should not be overlooked. Problem has been solved with the idea emerging from flow interception and coverage problems. Aims of maximizing the intercepted flow and coverage of roads are considered as the objectives of the model. The problem has been developed as Mixed Integer Program and it is shown that model is capable of handling the different requirements resulting from the demand of mobile and static populations. The mathematical formulation is extended for the stochastic case, relaxing the assumption that demand is known and certain. Risk-averse measures are included in the mathematical formulation with the application of Conditional-Value-at-Risk risk measure. It is observed that with a stochastic model, when uncertainties are present in the network, with the help of the risk-averse measure, the risk on the network is kept under control and the amount of demand that is subject to risk is decreased.