Browsing by Author "Karasakal, Esra"

Now showing 1 - 11 of 11

An approach for extending PROMETHEE to reflect choice behaviour of the decision maker
(2019) Karasakal, Esra; Karasakal, Orhan; Bozkurt, Ahmet; 216553; 06.04. Endüstri Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
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.
An Approach For Extending Promethee To Reflect Choice Behaviour Of The Decision Maker
(2019) Bozkurt, Ahmet; Karasakal, Esra; Karasakal, Orhan; 06.04. Endüstri Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
Bu çalışmada, karar vericinin fayda teorisi ile açıklanamayan seçim davranışını doğru bir şekilde yansıtabilmek için PROMETHEE yöntemini temel alan bir yaklaşım geliştirilmiştir. Seçim davranışı teorisi, zararların kazançlardan daha yüksek etkisinin olduğunu ileri sürmektedir. Bu teori PROMETHEE yöntemine yeni tercih fonksiyonları tanımlamak suretiyle entegre edilmiştir. Önerilen yaklaşım, davranışsal olarak gerçekçi ve karar vericinin tercihlerinde oluşabilecek geçişsiz değerlendirmelere izin veren bir yöntemdir. Kriter ağırlıklarının belirlenmesinde Analitik Hiyerarşi Süreci yaklaşımındaki ikili karşılaştırma metodu kullanılmıştır. Önerilen yaklaşımın etkinliği bir üniversite sıralama problem üzerinde gösterilmiştir.
Citation - WoS: 20
Citation - Scopus: 22
Bi-Objective Dynamic Weapon-Target Assignment Problem With Stability Measure
(Springer, 2022) Karasakal, Esra; Karasakal, Orhan; Silav, Ahmet; 216553; 06.04. Endüstri Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
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 - WoS: 6
Citation - Scopus: 9
Bi-Objective Missile Rescheduling for a Naval Task Group With Dynamic Disruptions
(Wiley, 2019) Karasakal, Orhan; Karasakal, Esra; Silav, Ahmet; 216553; 06.04. Endüstri Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
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 - WoS: 7
Citation - Scopus: 11
A Multi-Objective Approach for Dynamic Missile Allocation Using Artificial Neural Networks for Time Sensitive Decisions
(Springer, 2021) Karasakal, Esra; Silav, Ahmet; Karasakal, Orhan; 216553; 06.04. Endüstri Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
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 - WoS: 4
Citation - Scopus: 4
Multiobjective Aerial Surveillance Over Disjoint Rectangles
(Pergamon-elsevier Science Ltd, 2020) Karasakal, Esra; Maras, Guliz; Karasakal, Orhan; 216553; 06.04. Endüstri Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
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.
Multiple Criteria Target Classification Using Heterogeneous Sensor Data
(2019) Karasakal, Orhan; Atıcı, Bengü; Karasakal, Esra; 216553; 06.04. Endüstri Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
Otomatik Hedef Sınıflandırma Sistemleri İçin Çok Kriterli Hedef Sınıflandırma
(2019) Atıcı, Bengü; Karasakal, Esra; Karasakal, Orhan; 216553; 06.04. Endüstri Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
A Partial Coverage Hierarchical Location Allocation Model for Health Services
(inderscience Enterprises Ltd, 2023) Karasakal, Esra; Toreyen, Ozgun; Karasakal, Orhan; 216553; 06.04. Endüstri Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
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. [Submitted: 20 January 2021; Accepted: 15 January 2022]
PROMETHEE Yöntemini Beklenti Teorisi ile Uyumlandıran Bütünleşik bir Yaklaşım
(2018) Karasakal, Orhan; Karasakal, Esra; 216553; 06.04. Endüstri Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
Citation - WoS: 10
Citation - Scopus: 9
Ranking Using Promethee When Weights and Thresholds Are Imprecise: a Data Envelopment Analysis Approach
(Taylor & Francis Ltd, 2022) Eryilmaz, Utkan; Karasakal, Orhan; Karasakal, Esra; 216553; 06.04. Endüstri Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
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.