WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 102Citation - Scopus: 119Risk Based Facility Location by Using Fault Tree Analysis in Disaster Management(Pergamon-Elsevier Science Ltd, 2015-04) Akgun, Ibrahim; Gumusbuga, Ferhat; Tansel, BarbarosDetermining the locations of facilities for prepositioning supplies to be used during a disaster is a strategic decision that directly affects the success of disaster response operations. Locating such facilities close to the disaster-prone areas is of utmost importance to minimize response time. However, this is also risky because the facility may be disrupted and hence may not support the demand point(s). In this study, we develop an optimization model that minimizes the risk that a demand point may be exposed to because it is not supported by the located facilities. The purpose is to choose the locations such that a reliable facility network to support the demand points is constructed. The risk for a demand point is calculated as the multiplication of the (probability of the) threat (e.g., earthquake), the vulnerability of the demand point (the probability that it is not supported by the facilities), and consequence (value or possible loss at the demand point due to threat). The vulnerability of a demand point is computed by using fault tree analysis and incorporated into the optimization model innovatively. To our knowledge, this paper is the first to use such an approach. The resulting non-linear integer program is linearized and solved as a linear integer program. The locations produced by the proposed model are compared to those produced by the p-center model with respect to risk value, coverage distance, and covered population by using several test problems. The model is also applied in a real problem. The results indicate that taking the risk into account explicitly may create significant differences in the risk levels. (C) 2014 Elsevier Ltd. All rights reserved.Article Citation - WoS: 2Citation - Scopus: 5Radio Communications Interdiction Problem Under Deterministic and Probabilistic Jamming(Pergamon-Elsevier Science Ltd, 2019-07) Tanerguclu, Turker; Karasan, Oya Ekin; Akgun, Ibrahim; Karasan, EzhanThe Radio Communications Interdiction Problem (RCIP) seeks to identify the locations of transmitters on the battlefield that will lead to a robust radio communications network by anticipating the effects of intentional radio jamming attacks used by an adversary during electronic warfare. RCIP is a sequential game defined between two opponents that target each other's military units in a conventional warfare. First, a defender locates a limited number of transmitters on the defender's side of the battlefield to optimize the relay of information among its units. After observing the locations of radio transmitters, an attacker locates a limited number of radio jammers on the attacker's side to disrupt the communication network of the defender. We formulate RCIP as a binary bilevel (max-min) programming problem, present the equivalent single level formulation, and propose an exact solution method using a decomposition scheme. We enhance the performance of the algorithm by utilizing dominance relations, preprocessing, and initial starting heuristics. To reflect a more realistic jamming representation, we also introduce the probabilistic version of RCIP where a jamming probability is associated at each receiver site as a function of the prevalent jamming to signal ratios leading to an expected coverage of receivers as an objective function. We approximate the nonlinearity in the jamming probability function using a piecewise linear convex function and solve this version by adapting the decomposition algorithm constructed for RCIP. Our extensive computational results on realistic scenarios show the efficacy of the solution approaches and provide valuable tactical insights. (C) 2019 Elsevier Ltd. All rights reserved.Article Citation - WoS: 14Citation - Scopus: 15P-Hub Median Problem for Non-Complete Networks(Pergamon-Elsevier Science Ltd, 2018-07) Akgun, Ibrahim; Tansel, Barbaros C.Most hub location studies in the literature use a complete-network structure as an input in developing optimization models. This starting point is not necessarily from assuming that the underlying real-world network (e.g., physical network such as road and rail networks) on which the hub system will operate is complete. It is implicitly or explicitly assumed that a complete-network structure is constructed from the shortest-path lengths between origin-destination pairs on the underlying real-world network through a shortest-path algorithm. Thus, the network structure used as an input in most models is a complete network with the distances satisfying the triangle inequality. Even though this approach has gained acceptance, not using the real-world network and its associated data structure directly in the models may result in several computational and modeling disadvantages. More importantly, there are cases in which the shortest path is not preferred or the triangle inequality is not satisfied. In this regard, we take a new direction and define the p-hub median problem directly on non-complete networks that are representative of many real-world networks. The proposed problem setting and the modeling approach allow several basic assumptions about hub location problems to be relaxed and provides flexibility in modeling several characteristics of real-life hub networks. The proposed models do not require any specific cost and network structure and allow to use the real-world network and its asociated data structure directly. The models can be used with the complete networks as well. We also develop a heuristic based on the proposed modeling aproach and present computational studies. (C) 2018 Elsevier Ltd. All rights reserved.Article Citation - WoS: 8Citation - Scopus: 13Multiple Allocation Tree of Hubs Location Problem for Non-Complete Networks(Pergamon-Elsevier Science Ltd, 2021-12) Kayisoglu, Betul; Akgun, IbrahimWe study the Multiple Allocation Tree of Hubs Location Problem where a tree topology is required among the hubs and transportation cost of sending flows between OD pairs is minimized. Unlike most studies in the literature that assume a complete network with costs satisfying the triangle inequality to formulate the problem, we define the problem on non-complete networks and develop a modeling approach that does not require any specific cost and network structure. The proposed approach may provide more flexibility in modeling several characteristics of real-life hub networks. Moreover, the approach may produce better solutions than the classical approach, which may result from the differences in the selected hubs, the flow routes between origin-destination points, and the assignment of non-hub nodes to hub nodes. We solve the proposed model using CPLEX-based branch-and-bound algorithm and Gurobi-based branch-and-bound algorithm with Norel heuristic and develop Benders decomposition-based heuristic algorithms using two acceleration strategies, namely, strong cut generation and cut disaggregation. We conduct computational experiments using problem instances defined on non-complete networks with up to 500 nodes. The results indicate that the Benders-type heuristics are especially effective in finding good feasible solutions for large instances.