Browsing by Author "Karasakal, O."

Now showing 1 - 3 of 3

Citation - Scopus: 2
A Multiple Criteria Ranking Method Based on Outranking Relations: An Extension for Prospect Theory
(Springer Science and Business Media Deutschland GmbH, 2022) Karasakal, E.; Karasakal, Orhan; Karasakal, O.; Şentürk, H.; 216553; Endüstri Mühendisliği
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. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Citation - Scopus: 0
Heterogeneous Sensor Data Fusion for Target Classification Using Adaptive Distance Function
(Springer Science and Business Media Deutschland GmbH, 2021) Atıcı, B.; Karasakal, Orhan; Karasakal, E.; Karasakal, O.; 216553; Endüstri Mühendisliği
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, the classification phase of an ATR system having heterogeneous sensors is considered. We propose novel multiple criteria classification methods based on the 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 a higher accuracy ratio is chosen for each of the sensors. 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. © 2021, Springer Nature Switzerland AG.
Citation - Scopus: 6
A 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.