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Yakıcı, Ertan

Profile Picture
Profile URL
https://hdl.handle.net/20.500.12416/8744
Name Variants
Yakici, Ertan
Job Title
Doç. Dr.
Email Address
eyakici@cankaya.edu.tr
Main Affiliation
Endüstri Mühendisliği
Status
Current Staff
Website
ie.cankaya.edu.tr/faculty_eyakici_en.php
ORCID ID
0000-0002-8148-1031
Scopus Author ID
41262760100
Turkish CoHE Profile ID
242924
Google Scholar ID
6MBV_1oAAAAJ
WoS Researcher ID
KVB-1423-2024
Files
02170.jpeg (247.04 KB)

Sustainable Development Goals

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Documents

24

Citations

399

h-index

10

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Documents

23

Citations

316

Go to WoS profile
Scholarly Output

3

Articles

3

Views / Downloads

5/0

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

8

Scopus Citation Count

8

WoS h-index

2

Scopus h-index

2

Patents

0

Projects

0

WoS Citations per Publication

2.67

Scopus Citations per Publication

2.67

Open Access Source

2

Supervised Theses

0

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Computers & Industrial Engineering1
Computers & Operations Research1
Computers & Operations Research1
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Now showing 1 - 3 of 3
  • Thumbnail Image
    Article
    Citation - WoS: 3
    Citation - Scopus: 3
    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.
  • Thumbnail Image
    Article
    Citation - WoS: 5
    Citation - Scopus: 5
    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.
  • Thumbnail Image
    Article
    Optimization of Fleet Search on Network of Regions
    (Elsevier Ltd, 2026) Yakıcı, E.; Erişkin, L.; Karatas, M.; Karasakal, O.
    Unmanned Aerial Vehicles (UAVs) are widely used in modern military missions, primarily for surveillance, reconnaissance, search and detection, and air-to-ground strikes. The widespread use of UAVs in recent conflicts, such as the Russia–Ukraine war, once again highlighted their growing strategic importance. The complexity of military missions carried out by UAVs, coupled with the need for autonomous and coordinated fleet operations, requires analytical approaches to optimize deployment planning and improve operational efficiency. In this study, we address a UAV deployment planning problem for search and detection missions, in which a homogeneous fleet of UAVs is tasked with searching for hostile assets across a network of disjoint regions. Each region is characterized by an a priori probability of target presence, a search difficulty factor which affects the probability of detection, and known inter-region distances. For this purpose, we first develop a mixed-integer nonlinear programming formulation which determines the base locations of UAVs, allocates the limited search time across regions, and sequences the visits to maximize the total time-weighted detection probability mass to achieve the highest probability as much and as early as possible during the operation. Next, we apply a tangent line approximation technique to reformulate the model as a mixed-integer linear programming problem, which we solve using commercial off-the-shelf solvers. We then propose a hybrid heuristic approach based on the ant colony optimization method to generate high-quality solutions. Our computational experiments reveal that the proposed heuristic significantly reduces solution time while maintaining superior performance compared to the linear approximation model. © 2026 The Authors