Browsing by Author "Karasakal, Orhan"

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Citation Count: Karasakal, O., Kandiller, L., Özdemirel, N.E. (2011). A branch and bound algorithm for sector allocation of a naval task group. Naval Research Logistics, 58(7), 655-669. http://dx.doi.org/10.1002/nav.20474
A branch and bound algorithm for sector allocation of a naval task group
(Wiley-Blackwell, 2011) Karasakal, Orhan; Kandiller, Levent; Özdemirel, Nur Evin; 5706; 2634
A naval task group (TG) is a collection of naval combatants and auxiliaries that are grouped together for the accomplishment of one or more missions. Ships forming a TG are located in predefined sectors. We define determination of ship sector locations to provide a robust air defense formation as the sector allocation problem (SAP). A robust formation is one that is very effective against a variety of attack scenarios but not necessarily the most effective against any scenario. We propose a 0-1 integer linear programming formulation for SAP. The model takes the size and the direction of threat into account as well as the defensive weapons of the naval TG. We develop tight lower and upper bounds by incorporating some valid inequalities and use a branch and bound algorithm to exactly solve SAP. We report computational results that demonstrate the effectiveness of the proposed solution approach
Citation Count: Karasakal, Orhan; Karasakal, Esra; Silav, Ahmet (2021). "A multi-objective approach for dynamic missile allocation using artificial neural networks for time sensitive decisions", Soft Computing, Vol. 25, No. 15, pp. 10153-10166.
A multi-objective approach for dynamic missile allocation using artificial neural networks for time sensitive decisions
(2021) Karasakal, Orhan; Karasakal, Esra; Silav, Ahmet; 216553
In this study, we develop a new solution approach for the dynamic missile allocation problem of a naval task group (TG). The approach considers the rescheduling of the surface-to-air missiles (SAMs), where a set of them have already been scheduled to a set of attacking anti-ship missiles (ASMs). The initial schedule is mostly inexecutable due to disruptions such as neutralization of a target ASM, detecting a new ASM, and breakdown of a SAM system. To handle the dynamic disruptions while keeping efficiency high, we use a bi-objective model that considers the efficiency of SAM systems and the stability of the schedule simultaneously. The rescheduling decision is time-sensitive, and the amount of information to be processed is enormous. Thus, we propose a novel approach that supplements the decision-maker (DM) in choosing a Pareto optimal solution considering two conflicting objectives. The proposed approach uses an artificial neural network (ANN) that includes an adaptive learning algorithm to structure the DM's prior articulated preferences. ANN acts like a DM during the engagement process and chooses one of the non-dominated solutions in each rescheduling time point. We assume that the DM's utility function is consistent with a non-decreasing quasi-concave function, and the cone domination principle is incorporated into the solution procedure. An extensive computational study is provided to present the effectiveness of the proposed approach.
Citation Count: Karasakal, Esra; Karasakal, Orhan; Hazel, Şentürk. "A Multiple Criteria Ranking Method Based on Outranking Relations: An Extension for Prospect Theory", in Multiple Criteria Decision Making with Fuzzy Sets, pp. 115-133.
A Multiple Criteria Ranking Method Based on Outranking Relations: An Extension for Prospect Theory
(2022) Karasakal, Esra; Karasakal, Orhan; Hazel, Şentürk; 216553
In this study, Prospect Theory is integrated into a well-known multiple criteria ranking method, PROMETHEE. PROMETHEE considers the outranking relations among alternatives based on the preference functions. Prospect Theory evaluates the alternatives with a difference function based on gains and losses. The preference functions of PROMETHEE are modified to capture the choice behavior of the decision maker. The proposed method is a generalization of PROMETHEE that can handle the higher loss impact case as well as the usual equal loss and gain impact. The proposed method is compared with PROMETHEE, PT-PROMETHEE that is an extension of PROMETHEE with reference alternative, and the weighted sum method using an exemplary data set and Times Higher Education (THE) World University Ranking 2019 and 2020 data. The results show that rankings of alternatives change significantly when the impact of losses is larger than gains.
Citation Count: Karasakal, O.; Karasakal, E., Töreyen, Ö. (2023). "A partial coverage hierarchical location allocation model for health services", European Journal Of Industrial Engineering, Vol.17, No. 4, pp. 115-147.
A partial coverage hierarchical location allocation model for health services
(2023) Karasakal, Orhan; Karasakal, Esra; Töreyen, Özgün; 216553
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.
Citation Count: Karasakal, Esra; Karasakal, Orhan; Bozkurt, Ahmet (2019). "An approach for extending PROMETHEE to reflect choice behaviour of the decision maker", Journal of Industrial Engineering, Vol. 30, No. 2, pp. 123-140.
An approach for extending PROMETHEE to reflect choice behaviour of the decision maker
(2019) Karasakal, Esra; Karasakal, Orhan; Bozkurt, Ahmet; 216553
In this study, an approach based on PROMETHEE is developed to correctly reflect the choice behavior of the decision maker that is not explained by the utility theory. The prospect theory argues that losses have higher impact than gains. We integrate the prospect theory into PROMETHEE through defining new preference functions. The proposed approach is behaviorally realistic and tolerates some degree of intransitivities in the preferences of the decision maker. For determining the criteria weights, we utilize pairwise comparison method of Analytic Hierarchy Process. Performance of the approach is demonstrated on a university ranking problem.
Citation Count: Karasakal, O., Özdemirel, N.E., Kandiller, L. (2011). Anti-ship missile defense for a naval task group. Naval Research Logistics, 58(3), 305-322. http://dx.doi.org/10.1002/nav.20457
Anti-ship missile defense for a naval task group
(Wiley-Blackwell, 2011) Karasakal, Orhan; Özdemirel, Nur Evin; Kandiller, Levent; 2634; 5706
In 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
Citation Count: Silav, Ahmet; Karasakal, Esra; Karasakal, Orhan (2021). "Bi-objective dynamic weapon-target assignment problem with stability measure", Annals of Operations Research.
Bi-objective dynamic weapon-target assignment problem with stability measure
(2021) Silav, Ahmet; Karasakal, Esra; Karasakal, Orhan; 216553
In 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.
Citation Count: Silav, Ahmet; Karasakal, Orhan; Karasakal, Esra, "Bi-objective missile rescheduling for a naval task group with dynamic disruptions", Naval Research Logisics, Vol. 66, No. 7, pp. 596-615, (2019).
Bi-objective missile rescheduling for a naval task group with dynamic disruptions
(Wiley, 2019) Sılav, Ahmet; Karasakal, Orhan; Karasakal, Esra; 216553
This 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.
Citation Count: Atıcı, Bengü; Karasakal, Esra; Karasakal, Orhan (2020). "Heterogeneous Sensor Data Fusion for Target Classification Using Adaptive Distance Function", Multiple Criteria Decision Making - Beyond the Information Age, Switzerland: Springer, 2020.
Heterogeneous Sensor Data Fusion for Target Classification Using Adaptive Distance Function
(Springer, 2020) Atıcı, Bengü; Karasakal, Esra; Karasakal, Orhan; 216553
Automatic Target Recognition (ATR) systems are used as decision support systems to classify the potential targets in military applications. These systems are composed of four phases, which are selection of sensors, preprocessing of radar data, feature extraction and selection, and processing of features to classify potential targets. In this study, classification phase of an ATR system having heterogeneous sensors is considered. We propose novel multiple criteria classification methods based on modified Dempster-Shafer theory. Ensemble of classifiers is used as the first step probabilistic classification algorithm. Artificial neural network and support vector machine are employed in the ensemble. Each non-imaginary dataset coming from heterogeneous sensors is classified by both classifiers in the ensemble, and the classification result that has higher accuracy ratio is chosen for each of the sensor. The proposed data fusion algorithms are used to combine the sensors' results to reach the final class of the target. We present extensive computational results that show the merits of the proposed algorithms.
Citation Count: Karasakal, Orhan (2016). "Minisum and maximin aerial surveillance over disjoint rectangles", ORIGINAL PAPER, Vol. 24, pp. 705-724.
Minisum and maximin aerial surveillance over disjoint rectangles
(2016) Karasakal, Orhan; 216553
The 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.
Multiobjective aerial surveillance over disjoint rectangles
(2020) Karasakal, Orhan; Karasakal, Esra; Maraş, Güliz; 216553
In Aerial Surveillance Problem (ASP), an air platform with surveillance sensors searches a number of rectangular areas by covering the rectangles in strips and turns back to base where it starts. In this paper, we present a multiobjective extension to ASP, for which the aim is to help aerial mission planner to reach his/her most preferred solution among the set of efficient alternatives. We consider two conflicting objectives that are minimizing distance travelled and maximizing minimum probability of target detection. Each objective can be used to solve single objective ASPs. However, from mission planner's perspective, there is a need for simultaneously optimizing both objectives. To enable mission planner reaching his/her most desirable solution under conflicting objectives, we propose exact and heuristic methods for multiobjective ASP (MASP). We also develop an interactive procedure to help mission planner choose the most satisfying solution among all Pareto optimal solutions. Computational results show that the proposed methods enable mission planner to capture the tradeoffs between the conflicting objectives for large number of alternative solutions and to eliminate the undesirable solutions in small number of iterations.
Citation Count: Karasakal, Orhan; Atıcı, Bengü; Karasakal, Esra. "Multiple Criteria Target Classification Using Heterogeneous Sensor Data", Book of Abstracts of 25th International Conference on Multiple Criteria Decision Making, pp. 1-145, 2019.
Multiple Criteria Target Classification Using Heterogeneous Sensor Data
(2019) Karasakal, Orhan; Atıcı, Bengü; Karasakal, Esra; 216553
Citation Count: Arslan, Caner; Karasakal, Orhan; Kırca, Ömer (2024). "Naval Air Defense Planning problem: A novel formulation and heuristics", Naval Research Logistics.
Naval Air Defense Planning problem: A novel formulation and heuristics
(2024) Arslan, Caner; Karasakal, Orhan; Kırca, Ömer
This article focuses on air defense in maritime environment, which involves protecting friendly naval assets from aerial threats. Specifically, we define and address the Naval Air Defense Planning (NADP) problem, which consists of maneuvering decisions of the ships and scheduling weapons and sensors to the threats in order to maximize the total expected survival probability of friendly units. The NADP problem is more realistic and applicable than previous studies, as it considers features such as sensor assignment requirements, weapon and sensor blind sectors, sequence-dependent setup times, and ship's infrared/radar signature. In this study, a mixed-integer nonlinear programming model of the NADP problem is presented and heuristic solution approaches are developed. Computational results demonstrate that these heuristic approaches are both fast and efficient in solving the NADP problem.
Citation Count: Atıcı, Bengü; Karasakal, Esra; Karasakal, Orhan. "Otomatik Hedef Sınıflandırma Sistemleri İçin Çok Kriterli Hedef Sınıflandırma", 39. Yöneylem Araştırması ve Endüstri Mühendisliği Ulusal Kongresi (YAEM 2019) Bildiriler Kitabı, pp. 69-70, 2019.
Otomatik Hedef Sınıflandırma Sistemleri İçin Çok Kriterli Hedef Sınıflandırma
(2019) Atıcı, Bengü; Karasakal, Esra; Karasakal, Orhan; 216553
Citation Count: Karasakal, Orhan; Karasakal, Esra. "PROMETHEE Yöntemini Beklenti Teorisi ile Uyumlandıran Bütünleşik bir Yaklaşım", 38. Ulusal Yöneylem Araştırması ve Endüstri Mühendisliği Konferansı, Eskişehir, Türkiye, 26 Haziran 2018.
PROMETHEE Yöntemini Beklenti Teorisi ile Uyumlandıran Bütünleşik bir Yaklaşım
(2018) Karasakal, Orhan; Karasakal, Esra; 216553
Citation Count: Karasakal, Esra; Eryılmaz, Utkan; Karasakal, Orhan (2022). "Ranking using PROMETHEE when weights and thresholds are imprecise: a data envelopment analysis approach", Journal of the Operational Research Society, Vol. 73, No. 9, pp. 1978-1995.
Ranking using PROMETHEE when weights and thresholds are imprecise: a data envelopment analysis approach
(2022) Karasakal, Esra; Eryılmaz, Utkan; Karasakal, Orhan; 216553
Multicriteria 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.