Browsing by Subject "Traffic congestion"
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Item Open Access Comparison of the formulations for a hub-and-spoke network design problem under congestion(Elsevier, 2016) Kian, Ramer; Kargar, KamyarIn this paper, we study the hub location problem with a power-law congestion cost and propose an exact solution approach. We formulate this problem in a conic quadratic form and use a strengthening method which rests on valid inequalities of perspective cuts in mixed integer nonlinear programming. In a numerical study, we compare two well known types of mathematical modeling in the hub-location problems which are solved with different branch and cut strategies. The strength and weakness of the formulations are summarized based on an extensive numerical study over the CAB data set. © 2016 Elsevier LtdItem Open Access Learning traffic congestion by contextual bandit problems for optimum localization(IEEE, 2017) Şahin, Ümitcan; Yücesoy, V.; Koç, A.; Tekin, CemOptimum localization problem, which has a wide range of application areas in real life such as emergency services, command and control systems, warehouse localization, shipment planning, aims to find the best location to minimize the arrival, response or return time which might be vital in some applications. In most of the cases, uncertainty in traffic is the most challenging issue and in the literature generally it is assumed to obey a priori known stochastic distribution. In this study, problem is defined as the optimum localization of ambulances for emergency services and traffic is modeled to be Markovian to generate context data. Unlike the solution methods in the literature, there exists no mutual information transfer between the model and solution of the problem; thus, a contextual multi-armed bandit learner tries to determine the underlying traffic with simple assumptions. The performance of the bandit algorithm is compared with the performance of a classical estimation method in order to show the effectiveness of the learning approach on the solution of the optimum localization problem.