Browsing by Author "Karatas, Mumtaz"

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Citation - WoS: 0
Citation - Scopus: 0
Ant Colony Optimization for Solving Large-Scale Bi-Level Network Design Problems
(Pergamon-elsevier Science Ltd, 2025) Yakici, Ertan; Karatas, Mumtaz
In this study, we consider a bi-level hierarchical network design problem that encompasses both gradual and cooperative coverage. The lower-level facility serves as the primary point of contact for customers, while the upper-level facility acts as a supplier for the lower-level facilities. We first present a mathematical formulation of the problem, followed by an Ant Colony Optimization (ACO) approach to solve it. We then compare the performance of our method with commercial exact solvers. Our experiments, conducted on instances of various sizes, show that while exact methods may succeed in the long run, our heuristic provides a fast and reliable option for operational decisions that need to be made in a short period of time. In nine out of twelve instances, the exact solver failed to find a feasible solution within three hours for the high-budget case and two hours for the low-budget case. In contrast, our heuristic had run times between 0.1 and 0.4 h for 50 iterations. We also compare the performance of ACO with that of a Genetic Algorithm (GA) to evaluate its effectiveness among heuristics. Our numerical results demonstrate that ACO outperforms GA. This study contributes to the literature by offering a solid theoretical framework for the problem and implementing ACO to solve a bi-level facility location problem. Our results demonstrate that ACO can deliver good solutions in a reasonable time and serves as a promising alternative.
Citation - WoS: 3
Citation - Scopus: 3
Location and Routing of Armed Unmanned Aerial Vehicles and Carrier Platforms Against Mobile Targets
(Pergamon-elsevier Science Ltd, 2024) Yakici, Ertan; Karatas, Mumtaz; Eriskin, Levent; Cicek, Engin
In this study, we consider a real-life combinatorial optimization problem related to deploying and routing Unmanned Aerial Vehicles (UAVs) and naval carrier platforms. In particular, we seek to determine the initial locations for carrier platforms and the optimal type and number of UAVs to be stationed on each carrier platform as well as their spatial/temporal routes for engaging hostile surface targets in the region. Our modeling framework incorporates a number of realistic but challenging ingredients and assumptions such as the mobility of surface targets and carrier platforms during the mission, capacitated multiple platforms and UAVs, UAV-carrier platform compatibility, and allowance for different takeoff/land on platforms for UAVs. In the effort to represent the problem mathematically, we first formulated an Integer Linear Program (ILP) model which seeks to maximize the total time-dependent weights of the targets engaged. Next, we proposed a heuristic solution algorithm based on the ant colony optimization framework. Our computational experiments performed on instances with different sizes showed that the heuristic approach achieves high-quality solutions even for large-size problem instances in short CPU times.