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    Analysis of Efficiency and Productivity of Commercial Banks in Turkey Pre- and during COVID-19 with an Integrated MCDM Approach
    (Mdpi, 2022) Unlu, Ulas; Yalcin, Nese; Avsarligil, Nuri
    Above all, this study is original in that it reveals the efficiency and productivity of banks exposed to the current pandemic situation. The aim of this study is to evaluate bank efficiency and productivity of commercial banks operating in Turkey pre- and during COVID-19 by using a novel integrated multi-criteria decision-making (MCDM) approach. We divided the banks into three groups in order to evaluate the differences in terms of their efficiency and productivity: state banks, foreign banks and private domestic banks. This paper fills a gap in the literature by using a novel integrated MCDM approach including SWARA II as a subjective weighting method, MEREC as an objective weighting method, and MARCOS as a ranking method to evaluate bank efficiency and productivity. The results reveal that banks with foreign investors achieved higher productivity than other bank groups and the productivity of state banks decreased especially during the COVID-19 period. It should also be noted that state banks are restricted to certain political objectives.
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    Application of the Fuzzy CODAS Method Based on Fuzzy Envelopes for Hesitant Fuzzy Linguistic Term Sets: A Case Study on a Personnel Selection Problem
    (Mdpi, 2019) Yalcin, Nese; Pehlivan, Nimet Yapici
    Fuzzy multi-criteria decision-making (MCDM) methods are useful and reliable for multi-criteria selection problems under uncertain and imprecise situations. In these methods, if decision-makers hesitate among several linguistic terms, hesitant fuzzy linguistic term sets (HFLTSs), represented by a set of successive linguistic terms instead of single linguistic terms, may be more appropriate to make evaluations. The notion of a fuzzy envelope for the HFLTSs is a beneficial tool that can be directly applied to fuzzy MCDM methods to elicit comparative linguistic expressions (CLEs). The aim of this study is to present a methodology that combines the fuzzy CODAS (COmbinative Distance-based Assessment) method with the fuzzy envelope of HFLTs based on CLEs to solve a personnel selection problem. In order to examine the feasibility of the presented methodology, a case study on blue-collar personnel selection in a manufacturing firm is conducted. A sensitivity analysis is performed to demonstrate the stability and validity of the ranking results. Furthermore, the ranking results of the presented methodology are compared with various fuzzy MCDM methods, including fuzzy EDAS, fuzzy TOPSIS, fuzzy WASPAS, fuzzy ARAS, and fuzzy COPRAS. The results show that the presented methodology is efficient and stable for solving personnel selection problems in a hesitant fuzzy environment.
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    APPLYING EDAS AS AN APPLICABLE MCDM METHOD FOR INDUSTRIAL ROBOT SELECTION
    (Yildiz Technical Univ, 2019) Yalcin, Nese; Uncu, Nusin
    In order to stay an actual competitor in today's environment, it is essential for manufacturing organizations to make decisions promptly and correctly. In the real-time manufacturing decision making problems, some alternatives are more likely to be evaluated with respect to multiple conflicting criteria. Several multi-criteria decision-making (MCDM) methods have been available to help decision makers in choosing the best decisive course of actions. The aim of the study is to apply an efficient and relatively new method called Evaluation based on Distance from Average Solution (EDAS) as an applicable and useful MCDM method for robot selection problem (RSP). In order to examine the feasibility and effectiveness of the presented method, several numerical examples from the literature are considered. Comparing with other methods especially MCDM methods given in the literature for the industrial RSPs, the Spearman's rank correlations analysis indicates that this method is capable of accurately ranking selected robots.