Karasakal, Orhan
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Karasakal, Orhan
Karasakal, O.
Karasakal, O.
Job Title
Prof. Dr.
Email Address
okarasakal@cankaya.edu.tr
Main Affiliation
Endüstri Mühendisliği
Status
Current Staff
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Documents
18
Citations
449
h-index
9
Documents
14
Citations
324
Scholarly Output
20
Articles
15
Views / Downloads
1631/1550
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
84
Scopus Citation Count
125
WoS h-index
6
Scopus h-index
7
Patents
0
Projects
0
WoS Citations per Publication
4.20
Scopus Citations per Publication
6.25
Open Access Source
3
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0
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| Journal | Count |
|---|---|
| Naval Research Logistics (NRL) | 4 |
| 39. Yöneylem Araştırması ve Endüstri Mühendisliği Ulusal Kongresi (YAEM 2019) Bildiriler Kitabı | 1 |
| Annals of Operations Research | 1 |
| Book of Abstracts of 25th International Conference on Multiple Criteria Decision Making | 1 |
| Computers & Industrial Engineering | 1 |
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20 results
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Conference Object PROMETHEE Yöntemini Beklenti Teorisi ile Uyumlandıran Bütünleşik bir Yaklaşım(2018) Karasakal, Orhan; Karasakal, EsraArticle Citation - Scopus: 8A Partial Coverage Hierarchical Location Allocation Model for Health Services(Inderscience Publishers, 2023) Karasakal, O.; Karasakal, E.; Töreyen, Ö.We consider a hierarchical maximal covering location problem (HMCLP) to locate health centres and hospitals so that the maximum demand is covered by two levels of services in a successively inclusive hierarchy. We extend the HMCLP by introducing the partial coverage and a new definition of the referral. The proposed model may enable an informed decision on the healthcare system when dynamic adaptation is required, such as a COVID-19 pandemic. We define the referral as coverage of health centres by hospitals. A hospital may also cover demand through referral. The proposed model is solved optimally for small problems. For large problems, we propose a customised genetic algorithm. Computational study shows that the GA performs well, and the partial coverage substantially affects the optimal solutions. © 2023 Inderscience Enterprises Ltd.Article Minisum and maximin aerial surveillance over disjoint rectangles(2016) Karasakal, OrhanThe aerial surveillance problem (ASP) is finding the shortest path for an aerial surveillance platform that has to visit each rectangular area once and conduct a search in strips to cover the area at an acceptable level of efficiency and turn back to the base from which it starts. In this study, we propose a new formulation for ASP with salient features. The proposed formulation that is based on the travelling salesman problem enables more efficient use of search platforms and solutions to realistic problems in reasonable time. We also present a max–min version of ASP that maximizes the minimum probability of target detection given the maximum flight distance of an aerial platform. We provide computational results that demonstrate features of the proposed models.Article Citation - WoS: 22Citation - Scopus: 23Bi-Objective Dynamic Weapon-Target Assignment Problem With Stability Measure(Springer, 2022) Karasakal, Esra; Karasakal, Orhan; Silav, AhmetIn this paper, we develop a new bi-objective model for dynamic weapon-target assignment problem. We consider that the initial weapon assignment plan of defense is disrupted during engagement because of a destroyed air target, breakdown of a weapon system or a new incoming air target. The objective functions are defined as the maximization of probability of no-leaker and the maximization of stability in engagement order of weapon systems. Stability is defined as assigning same air target in sequence in engagement order of a weapon system so that reacquisition and re-tracking of air target are not required by sensors. We propose a new solution procedure to generate updated assignment plans by maximizing efficiency of defense while maximizing stability through swapping weapon engagement orders. The proposed solution procedure generates non-dominated solutions from which defense can quickly choose the most-favored course of action. We solve a set of representative problems with different sizes and present computational results to evaluate effectiveness of the proposed approach.Article Citation - WoS: 11Citation - Scopus: 10Ranking Using Promethee When Weights and Thresholds Are Imprecise: a Data Envelopment Analysis Approach(Taylor & Francis Ltd, 2022) Eryilmaz, Utkan; Karasakal, Orhan; Karasakal, EsraMulticriteria decision making (MCDM) provides tools for the decision makers (DM) to solve complex problems with multiple conflicting criteria. Scalarization of criteria values requires using weights for criteria. Determining weights creates controversy as they are influential on the final ranking and challenges the DM as they are hard to elicit. PROMETHEE method is widely used in MCDM for ranking the alternatives and appropriate in situations when there is limited information on the preference structure of the DM. The DM should provide exact values for parameters such as criteria weights and thresholds of preference functions. Data Envelopment Analysis (DEA) is used for measuring the relative efficiency of alternatives in a non-parametric way without requiring any weight input. In this study, we propose two novel PROMETHEE based ranking approaches that address the determination of weight and threshold values by using an approach inspired by DEA. The first approach can deal with imprecise specification of criteria weights, and the second approach can utilize both imprecise weights and thresholds. The proposed approaches provide the DM substantial flexibility on the required level of information on those parameters. An illustrative example and a real-life case study are presented to show the utility of the proposed approaches.Article Citation - WoS: 26Citation - Scopus: 37Anti-Ship Missile Defense for a Naval Task Group(Wiley-blackwell, 2011) Kandiller, Levent; Karasakal, Orhan; Ozdemirel, Nur EvinIn this study, we present a new formulation for the air defense problem of warships in a naval task group and propose a solution method. We define the missile allocation problem (MAP) as the optimal allocation of a set of surface-to-air missiles (SAMs) of a naval task group to a set of attacking air targets. MAP is a new treatment of an emerging problem fostered by the rapid increase in the capabilities of anti-ship missiles (ASMs), the different levels of air defense capabilities of the warships against the ASM threat, and new technology that enables a fully coordinated and collective defense. In addition to allocating SAMs to ASMs, MAP also schedules launching of SAM rounds according to shoot-look-shoot engagement policy or its variations, considering multiple SAM systems and ASM types. MAP can be used for air defense planning under a given scenario. As thorough scenario analysis would require repetitive use of MAP, we propose efficient heuristic procedures for solving the problem. (C) 2011 Wiley Periodicals, Inc. Naval Research Logistics 58: 305-322, 2011Conference Object Otomatik Hedef Sınıflandırma Sistemleri İçin Çok Kriterli Hedef Sınıflandırma(2019) Atıcı, Bengü; Karasakal, Esra; Karasakal, OrhanArticle 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 AuthorsArticle Citation - WoS: 6Citation - Scopus: 10Bi-Objective Missile Rescheduling for a Naval Task Group With Dynamic Disruptions(Wiley, 2019) Karasakal, Orhan; Karasakal, Esra; Silav, AhmetThis paper considers the rescheduling of surface-to-air missiles (SAMs) for a naval task group (TG), where a set of SAMs have already been scheduled to intercept a set of anti-ship missiles (ASMs). In missile defense, the initial engagement schedule is developed according to the initial state of the defensive and attacking units. However, unforeseen events may arise during the engagement, creating a dynamic environment to be handled, and making the initial schedule infeasible or inefficient. In this study, the initial engagement schedule of a TG is assumed to be disrupted by the occurrence of a destroyed ASM, the breakdown of a SAM system, or an incoming new target ASM. To produce an updated schedule, a new biobjective mathematical model is formulated that maximizes the no-leaker probability value for the TG and minimizes the total deviation from the initial schedule. With the problem shown to be NP-hard, some special cases are presented that can be solved in polynomial time. We solve small size problems by the augmented epsilon-constraint method and propose heuristic procedures to generate a set of nondominated solutions for larger problems. The results are presented for different size problems and the total effectiveness of the model is evaluated.Article Solution Approaches for the Dynamic Naval Air Defense Planning Problem(Institute of Electrical and Electronics Engineers Inc., 2026) Arslan, C.; Karasakal, O.; Kirca, Ö.The naval air defense planning (NADP) problem entails the defense of a naval fleet against aerial threats. This complex and dynamic problem requires real-time decision-making and adaptation to evolving warfare environment. While our previous work addressed the static NADP problem by proposing a mathematical model and heuristic solutions for sensor allocation, engagement scheduling, and ship routing, this study extends to the dynamic NADP problem. Unlike the static version, which assumes complete knowledge of future threats, the dynamic NADP problem requires continuous updates and real-time adjustments to decisions as new threats emerge and situational parameters change. We present modifications in the mathematical formulation, which is based on a mixed-integer nonlinear programming (MINLP) model, alongside a comprehensive simulation structure. We employ heuristic solution approaches that utilize a combination of a genetic algorithm, construction of an engagement graph to solve the shortest path problem, and dynamic programming (DP) techniques. Computational experiments are conducted to evaluate the effectiveness of these methods in addressing the dynamic NADP problem. The study also explores machine learning models for threat prioritization, offering innovative solutions to the challenges posed by dynamic naval air defense scenarios. © 2013 IEEE.
