Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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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: 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.Article Citation - WoS: 4Citation - Scopus: 4A Multimodal, Multicommodity, and Multiperiod Planning Problem for Coal Distribution to Poor Families(Elsevier Science inc, 2020-12) Akgun, Ibrahim; Ozkil, Altan; Goren, SelcukTackling poverty has been one of the greatest global challenges and a prerequisite to sustainable development of countries. Countries implement nationally appropriate social protection systems and measures to address poverty. This paper addresses an aid system adopted by the government in Turkey where significant amounts of coal is distributed to poor families each year. The objective of the coal aid system is to complete the delivery of coal to poor families by the start of winter. However, an analysis of the data from previous years indicates that the distribution to many families cannot be completed on time. This results from the fact that planning is done manually and by trial-and-error as there is no system that can be used for distribution planning. This paper describes the planning problem encountered and develops a mathematical model to solve it. The proposed model is a multimodal, multicommodity, and multiperiod linear programming (LP) model. The model can be used to develop and update a distribution plan as well as to answer several what-if questions with regard to capacities, time constraints, and so forth. The model is solved using CPLEX for several problem instances obtained under different scenarios using data for the year 2012. The results show that at least 9% cost savings and about 40% decrease in distribution completion time can be achieved when the model is used. We analyze scenario results qualitatively and quantitatively and provide several insights to the decision makers. As a part of quantitative analysis, we develop regression models to predict optimal costs based on several factors. Our main contribution is to provide an efficient and effective tool to handle a large-scale real-world problem. The model has also helped to prove that the organization responsible for distribution planning may move from the current planning practice to an all-encompassing top-down approach.