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2009 yılında piyasaya giriş yapan kripto paralar, âdemi merkeziyetçi bir yapıda mübadele işlevini yerine getiren sanal para birimleridir. Kripto paralar, blockchain teknolojisine bağlı olarak ticari işlemlere konu olması ve düşük transfer maliyeti ile hızlı işlemler gerçekleştirilmesi gibi niteliksel özelliklere sahiptir. Kripto paralar, bir yatırım aracı olarak son yıllarda büyük ilgi görmektedir. Bu çalışmanın temel amacı, yatırımcıların yatırım kararı vermesini kolaylaştıracak bir karar destek mekanizması sunmaktır. Bu amaçla Entropi-ARAS çok kriterli modeli kullanılarak finansal piyasalarda en yüksek işlem hacmine sahip ilk on kripto para değerlendirilmiştir. Öncelikle Entropi yöntemi kullanılarak yıllık ortalama getiri, işlem hacmi, toplam piyasa değeri, işlem gördüğü borsa sayısı, işlem gördüğü yıl sayısı, işlem hızı, en yüksek değerden değişim ve volatilite kriterinin ağırlıkları belirlenmiştir. Ardından ARAS yönteminde kullanılarak, kripto para alternatiflerinin performans sıralaması yapılmıştır. Çalışmanın bulgularına göre, işlem hacmi yatırım yaparken dikkate alınması gereken en önemli faktördür. Ayrıca, alternatif kripto paralar içinde ilk sırada Bitcoin, Tether ve Usd Coin yer alırken, Dogecoin son sırada yer almaktadır. Kripto paraların işlem hacmi ile performans sıralamalarının yüksek derecede pozitif korelasyona sahip olduğu sonucuna da ulaşılmıştır. Son olarak önerilen modelin sağlamlığı ve geçerliliği duyarlılık analiziyle de kontrol edilmiştir.

PurposeNowadays, companies have required new alternatives and strategies to handle environmental sustainability difficulties, primarily as ecological and social awareness has grown. In this context, the aim is to determine the green transportation indicators in companies with corporate identity and logistics activities at the international level in Giresun, Ordu, Gümüshane, Artvin, Rize, and Trabzon in the Eastern Black Sea Region in Turkey. At the same time, the study contributes to providing an effective and applicable solution to decision-making problems involving the assessment of green transportation indicators and smart network strategies in the logistics sector, which is a critical sector for countries. The purpose of this paper is to address these issues.Design/methodology/approachThis study aims to propose a model for the selection of smart network strategy and to determine the criteria weights used in green transportation indicators, and establish an ideal smart network strategy. In achieving the outlined goals of the study, the authors believe that the model proposed in the study will draw the focus to green logistics which will aid the environmental, economic and social efforts of businesses and governments through the provision of efficient use of scarce resources, which will, in turn, ensure that we leave a sustainable environment for future generations and businesses enjoy a competitive advantage. At the same time, different smart network strategies and green transportation indicators in companies show the success rate of social, economic and environmental indicators in green logistics practices. In addition to providing innovative, reliable and sustainable transportation systems, smart network strategies are critical for businesses to create cost advantages. Through the green transportation indicators and smart network strategies selection model outlined in this study, it is clear that the contribution will not only be limited to businesses, as the society and governments will also benefit from the important indicators on sustainability, as well as the protection of the environment and nature.FindingsAccording to the findings, “economic indicators” is the essential green transportation indicator in logistics companies with a corporate identity and worldwide transportation operations. Besides, the “mixed access model strategy” is the most appropriate smart network strategy in logistics firms with corporate identity and worldwide transportation activities. Currently, it is possible to assume that logistics organizations prefer to profit from all smart network strategies in terms of cost optimization and competitiveness rather than from just one. The study, on the other hand, which is a road map that will help sustainability practices in the logistics sector due to green transportation, also examines the similarities and differences of green transportation practices in companies in the relevant sector and to what extent they can be reflected. As a result, the study provides a practical road map for selecting green transport indicators and a smart network strategy process for the logistics industry.Originality/valueThis study examined logistics companies with a corporate identity and international transportation activities in provinces in the Eastern Black Sea Region such as Ordu, Giresun, Trabzon, Rize, Artvin and Gümüshane. Novel picture fuzzy level based weight assessment (PF-LBWA) and picture fuzzy combined compromise solution (PF-CoCoSo) methods are developed to solve the decision-making problem.

Innovation can be the greatest hope of overcoming economic challenges. This paper aims to evaluate countries concerning their innovation performances. We introduce an innovation performance evaluation methodology by considering objective factors and applying seven reliable MCDM methods: MEREC, CODAS, MABAC, MARCOS, CoCoSo, WASPAS, and MAIRCA. MEREC calculates the relative weights of indicators considered, while the other techniques decide the ranking order of G7 countries. The Borda rule is then employed to gain an aggregated ranking order. “Business sophistication” is the most critical indicator, whereas the US has the best position regarding the overall ranking. Sensitivity control is as well conducted.

İnternetin keşfedilmesiyle birlikte insanların yaşam tarzlarında ve alışkanlıklarında önemli ölçüde değişimler meydana gelmiştir. İnternetle birlikte işletmeler satışlarını internet üzerinden yapma eğilimine girmiş ve insanlar giderek daha fazla ihtiyacını internet üzerinden karşılamaya başlamışlardır. 2019 yılında ortaya çıkan Covid-19 ile internet tüketicilerin hayatında daha da önemli bir yere sahip olmaya başlamıştır. Tüketiciler hastalık korkusuyla ihtiyaçlarını sanal alışveriş platformları üzerinden gidermeye yönelmişlerdir. Bu çerçevede bu çalışmada beş ana kriter ve bunlara ilişkin yirmi üç alt kriter doğrultusunda sanal alışveriş platformlarının salgın dönemindeki performansları AHP ve MAIRCA bütünleşik modeliyle analiz edilmiştir. Bu bağlamda AHP yöntemiyle kriterlerin önem ağırlıkları tespit edilmiştir. MAIRCA yöntemi ise sanal alışveriş sitelerinin sıralanması amacıyla kullanılmıştır AHP sonuçları sırasıyla “uygun fiyat”, “karantina süreci” ve “enfekte olma korkusu” kriterlerinin sanal alışveriş platformlarının performanslarının en önemli belirleyicileri olduğunu göstermiştir. Ayrıca Trendyol, en başarılı alışveriş sitesi olarak belirlenmiştir. Bu çalışma sanal alışveriş platformları için salgın dönemindeki kendi performans değerlendirmelerinde kullanabilecekleri bir kılavuz niteliğinde olabilir.

This paper proposes a new multi-criteria group decision-making (MCGDM) method utilizing q-rung orthopair fuzzy (qROF) sets, improved power weighted operators and improved power weighted Maclaurin symmetric mean (MSM) operators. The power weighted averaging operator and power weighted Maclaurin symmetric mean (MSM) operator used in the existing MCGDM methods have the drawback of being unable to distinguish the priority order of alternatives in some scenarios, especially when one of the qROF numbers being considered has a non-belongingness grade of 0 or a belongingness grade of 1. To address this limitation of existing MCGDM methods, four operators, namely qROF improved power weighted averaging (qROFIPWA), qROF improved power weighted geometric (qROFIPWG), qROF improved power weighted averaging MSM (qROFIPWAMSM) and qROF improved power weighted geometric MSM (qROFIPWGMSM), are proposed in this paper. These operators mitigate the effects of erroneous assessment of information from some biased decision-makers, making the decision-making process more reliable. Following that, a group decision-making methodology is developed that is capable of generating a reasonable ranking order of alternatives when one of the qROF numbers considered has a non-belongingness grade of 0 or a belongingness grade of 1. To investigate the applicability of the proposed approach, a case study is also presented and a comparison-based investigation is used to demonstrate the superiority of the approach.

Research background: International market selection is an essential issue for big companies that supply food products. Different types of decision factors and different characteristics of different international markets have brought up a complicated decision-making problem for food supply companies. In order to select the most suitable and profitable market, food supply companies have to consider several qualitative and quantitative factors, including social, political, economic, and ecological aspects. Purpose of the article: In order to overcome international market selection issues, the current study develops a novel integrated decision-making tool. Methods: A novel decision-making model of market analysis is developed as an extended model of Market Attractiveness and Business Attractiveness (MABA) analysis based on the Multiple Attribute Decision Making (MADM). To improve the MABA analysis model, we combine the EDAS method with MABA analysis to empower decision-makers in food supply companies to evaluate several international markets and select the most profitable market for their products. Findings & value added: In this study, we first identified the most important and frequently used decision factors for market analysis problems within MABA analysis under two categories: market attractiveness and business attractiveness. To show the proposed methodology's applicability and feasibility, we perform a case study for a food supply company in Iran that supplies products to Middle East and Asian countries. In order to investigate the reliability of the obtained results, we perform a sensitivity analysis concerning the importance of involved decision factors. The proposed decision-making tool results suggest that the model can be used as a reliable tool for market analysis problems. To sum up the long-term value of the study, we have developed a novel decision-making tool using MABA analysis and the EDAS method. No study integrates any MCDM methods with MABA analysis to the best of our knowledge. Integration of EDAS method with MABA analysis empowers decision-makers in market selection division to use more systematic methods for evaluating several markets.

The COVID-19 pandemic has created unexpected demand for air cargo in terms of rapid mobility of critical basic needs. Air cargo carriers aim to maximize their profits by taking advantage of the current demand and using their limited capacity in the right place. At this point, some of the qualifications of the airports in the places where demand plays a crucial role in this decision of the carriers. Thus, evaluating the factors considered in the airport selection for air cargo carriers during the COVID-19 period is curious. This study proposes a triangular fuzzy Dombi-Bonferroni best-worst method (BWM) framework with vast flexibility to establish the priority preferences of factors considered in selecting airports. The fuzzy BWM model becomes a superior decision support system by combining the Bonferroni mean operator's ability to consider interrelationships between attributes and the flexibility of the Dombi operator. In this sense, we highlight eighteen criteria based on five airport aspects: location, physical features, performance, costs, and reputation. Findings reveal that the foremost aspects are location and costs, whereas the most crucial factors are airport charges and handling charges. The study suggests that airports should follow a low-price policy for airport-related charges without compromising their sustainability to have a share of the increasing number of air cargo flights, especially during the COVID-19 period, when airline passenger flights are decreased. The study is crucial in deciding the strategy and policy of air cargo carriers and airports during the pandemic period.

All over the world, the COVID-19 outbreak seriously affects life, whereas numerous people have infected and passed away. To control the spread of it and to protect people, appreciable vaccine development efforts continue with increasing momentum. Given that this pandemic will be in our lives for a long time, it is obvious that a reliable and useful framework is needed to choose among coronavirus vaccines. To this end, this paper proposes a new intuitionistic fuzzy extension of MAIRCA framework, named intuitionistic fuzzy MAIRCA (IF-MAIRCA) to assess coronavirus vaccines according to some evaluation criteria. Based on the group decision-making, the IF-MAIRCA framework both extracts the criteria weights and discovers the prioritization of the alternatives under uncertainty. In this work, as a case study, five coronavirus vaccines approved by the world's leading authorities are evaluated according to various criteria. The findings demonstrate that the most significant criteria considered in coronavirus vaccine selection are “duration of protection,” “effectiveness of the vaccine,” “success against the mutations,” and “logistics,” respectively, whereas the best coronavirus vaccine is AZD1222. Apart from this, the proposed model's robustness is verified with a three-phase sensitivity analysis.

Çalışmanın amacı, Türkiye'deki özel sermayeli bankaların kurumsal sürdürülebilirlik performanslarını analiz etmektir. Bu amaçla Entropi-ARAS bütünleşik modeli önerilmiştir. Önerilen modelinin etkinliğini kontrol etmek için ayrıca duyarlılık analizi yapılmıştır. Çalışmanın bulgularına göre özel sermayeli bankaların sürdürülebilirlik performanslarının belirlenmesinde en önemli boyut sosyal boyuttur. Kurumsal sürdürülebilirlik bakımından yüksek performans hedefine ulaşmak isteyen bankalar, öncelikle personel devir hızını düşürmeli, ardından doğrudan sera gazı tüketimini azaltmalıdırlar. Çalışma ayrıca, önerilen modelin bankaların kurumsal sürdürülebilirlik performanslarının belirlenmesinde yararlanılabilecek kullanışlı ve güçlü bir karar aracı olduğu ortaya koymuştur.

Blockchain technologies, which form the basis of Industry 4.0, paved the way for cryptocurrencies to emerge as technological innovation in the technology age. Recently, investors worldwide have been interested in cryptocurrencies with increasing acceleration due to high earning expectations though they have no backing and intrinsic value. As such, this paper seeks to identify the most proper cryptocurrencies from an investment standpoint in our technological era. Fifteen well-known cryptocurrencies with the highest market capitalization are evaluated as per sixteen factors. An intuitionistic fuzzy set-driven methodology incorporating Evaluation Based on Distance from Average Solution (EDAS), Multi-Attributive Ideal Real Comparative Analysis (MAIRCA, and Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS), which is the study’s prominent novelty, has been applied to provide a strong group decision vehicle for cryptocurrency selection. Notwithstanding, although the results obtained with the three approaches are highly consistent, investors would not like to doubt the instrument they will invest in. The Borda count is then applied to obtain a compromise for the rankings obtained from each approach. As per our findings, Ethereum, Tether, and Bitcoin are the most suitable cryptocurrencies, whereas reliable software, ease of inclusion in the wallet, and stability are the foremost factors to consider when investing in cryptocurrencies. The findings are further discussed in detail from a financial perspective. The proposed approach could be employed to select different investment instruments in future studies.

The Best Worst Method (BWM) represents a powerful tool for multi-criteria decision-making and defining criteria weight coefficients. However, while solving real-world problems, there are specific multi-criteria problems where several criteria exert the same influence on decision-making. In such situations, the traditional postulates of the BWM imply the defining of one best criterion and one worst criterion from within a set of observed criteria. In this paper, an improvement of the traditional BWM that eliminates this problem is presented. The improved BWM (BWM-I) offers the possibility for decision-makers to express their preferences even in cases where there is more than one best and worst criterion. The development enables the following: (1) the BWM-I enables us to express experts&rsquo, preferences irrespective of the number of the best/worst criteria in a set of evaluation criteria, (2) the application of the BWM-I reduces the possibility of making a mistake while comparing pairs of criteria, which increases the reliability of the results, and (3) the BWM-I is characterized by its flexibility, which is expressed through the possibility of the realistic processing of experts&rsquo, preferences irrespective of the number of the criteria that have the same significance and the possibility of the transformation of the BWM-I into the traditional BWM (should there be a unique best/worst criterion). To present the applicability of the BWM-I, it was applied to defining the weight coefficients of the criteria in the field of renewable energy and their ranking.

The cash flow of a company is a key element for the firm value. The firm value of a company is depending largely on the ability to generate cash flows. In other words, a company’s firm value is calculated by using cash flows. Financial performance analysis helps companies in effective decision-making, planning, and auditing functions. Traditional ratio analysis uses the statement of financial position (balance sheet) and the profit and loss statement (income statement) to measure financial performance. However, the income statement of a company just shows the accounting profits. Depending on this, traditional ratios can sometimes be over or underestimated on measuring financial performances. To provide a better picture of a company’s financial strength and measure sensitive financial performance cash flow ratios were suggested instead of traditional ratios. Within this context, the aim of this study is to measure and compare the financial performance of Borsa İstanbul tourism companies with the use of cash flow based ratios. Ten tourism companies listed on BIST were evaluated by the Entropy-MAIRCA hybrid model. To achieve this, cash flow based eleven ratios were calculated within the indicators of liquidity, efficiency, profitability, and solvency (leverage). To calculate ratios a-year balance sheet, additionally, income statement and cash flow statement table gathered from Public Disclosure Platform. Findings of the Entropy method showed that the most important criteria were cash ratio, cash to sales, and cash to long term debts. According to the ranking results obtained by the MAIRCA method, the best tourism company is E. It was followed by G and D.

Temiz bir enerji kaynağı olan ve doğal dengeyi bozmayan rüzgâr enerjisi, günümüzün en önemli yenilenebilir enerji kaynaklarından birisidir. Bu çalışmada rüzgâr çiftlikleri için uygun yer tespitinde kullanılan faktörler, oldukça yeni bir sübjektif ağırlıklandırma yöntemi olan FUCOM (Full Consistency Method-Tam Tutarlılık Yöntemi) yöntemi kullanılarak analiz edilmiştir. FUCOM, doğrusal programlama temelli bir yöntemdir ve diğer ağırlıklandırma yöntemlerinden daha az ikili karşılaştırmaya ihtiyaç duyar. Çalışmadaki değerlendirme modeli, üç temel boyutu ve on iki faktörü dikkate almaktadır. Bulgular, bir rüzgâr çiftliği sahasının tercih edilmesinin büyük ölçüde rüzgâr potansiyeline ve yerleşim yerlerine olan uzaklığına bağlı olduğunu göstermiştir. Ayrıca önerilen yöntemin tutarlılığını ve kullanışlılığını göstermek için bir duyarlılık analizi yapılmıştır. Bu çalışmanın sonuçları rüzgâr enerjisine yönelik politika belirlemekte kullanılabilir.

Suitability-Feasibility-Acceptability (SFA) is a fundamental tool for the development and selection of strategy. Any type of decision-making problem can be resolved by Multiple Criteria Decision Making (MCDM) methods. In this research, we explore the complexity of determining the proper goal market for the Chilean fish market. This study proposed a combined approach of SFA with MCDM methods in a real case study. The proposed structure helps to assign the best market for Chilean export fish to West Asia. Three countries (Saudi Arabia, the United Arab Emirates, and Oman) are selected as a target market in this region, and then related criteria are obtained from various sources. In order to develop a new market for the Chilean fishery industry, five major criteria, including the potential of a target market, region's economic attractiveness, consumption of the seafood, location, cost of transportation, and country risks, were selected based on the SFA framework. Calculating the criteria weights is performed by the Best-Worst (BWM) method, and ordering the alternatives is operated by Measurement Alternatives and Ranking according to compromise Solution (MARCOS) methods. The results showed that Oman is the best destination (importer) for the Chilean fish market (Salmon fish as the case).

Financial difficulties make it difficult for families with disabilities to live a standard life. In this study, a model is developed to estimate the required financial support for the families with disabilities that ensures them to have lives like the families with non-disabilities by considering main consumption groups. Hence, the aim of the study is to determine the financial support for families with disabilities that need to have the same life standards as other families by taking into consideration main consumption groups. In the analyses, the Household Budget Survey (HBS) data prepared by Turkish Statistical Institute (TurkStat) are utilized. Furthermore, 101,504 households are included in the analysis. During the development of the model, the AHP method is used. Besides, a sensitivity analysis is conducted to demonstrate the stability of the proposed model. Moreover, the suggested model considers families having different individuals as well.

Assessing and ranking private health insurance companies provides insurance agencies, insurance customers, and authorities with a reliable instrument for the insurance decision-making process. Moreover, because the world’s insurance sector suffers from a gap of evaluation of private health insurance companies during the COVID-19 outbreak, the need for a reliable, useful, and comprehensive decision tool is obvious. Accordingly, this article aims to identify insurance companies’ priority ranking in terms of healthcare services in Turkey during the COVID-19 outbreak through a multi-criteria performance evaluation methodology. Herein, alternatives are evaluated and then ranked as per 7 criteria and assessments of 5 experts. Experts’ judgments and assessments are full of uncertainties. We propose a Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) technique under an intuitionistic fuzzy environment to rank insurance companies. The outcomes yielded ten insurance companies ranking in terms of healthcare services in the era of COVID-19. The payback period, premium price, and network are determined as the most crucial factors. Finally, a comprehensive sensitivity analysis is performed to verify the proposed methodology’s stability and effectiveness. The introduced approach met the insurance assessment problem during the COVID-19 pandemic very satisfactory manner based on sensitivity analysis findings.

Values, opinions, perceptions, and experiences are the forces that drive almost each and every kind of decision-making. Evaluation criteria are considered as sources of information used to compare alternatives and, as a result, make selection easier. Seeing their direct effect on the solution, weighting methods that most accurately determine criteria weights are needed. Unfortunately, the crisp values are insufficient to model real life problems due to the lack of complete information and the vagueness arising from linguistic assessments of decision-makers. Therefore, this paper proposes a novel subjective weighting method called the Fuzzy Full Consistency Method (FUCOM-F) for determining weights as accurately as possible under fuzziness. The most prominent feature of the proposed method is obtaining the most accurate weight values with very few pairwise comparisons. Consequently, thanks to this model, consistency and reliability of the results increase while the processing time and effort decrease. Moreover, an illustrative example related to the green supplier evaluation problem is performed. Finally, the robustness and effectiveness of the proposed fuzzy model is demonstrated by comparing it with fuzzy best-worst method (F-BWM) and fuzzy AHP (F-AHP) models.

In sustainable supply chain management (SSCM), triple bottom line (TBL) of sustainability (economic, social, and environmental) are considered when selecting the suppliers. In this paper, the relative weights of SSCM practices are extracted by fuzzy best worst method (F-BWM) which is capable of better modeling of human thinking. Afterwards, the traditional Combined Compromise Solution (CoCoSo) method is enhanced by the integration of the normalized weighted and the normalized weighted geometric Bonferroni mean functions to select the most proper supplier in a supply chain. The integration of the Bonferroni functions into the CoCoSo method (CoCoSo’B) enables: (1) flexible decision-making respecting the interaction between decision attributes; (2) the elimination of the influence of extreme/awkward data on the values of the criterion functions, and (3) checking the robustness of the results through a variation of the parameters λ, p, and q. Testing the model on the example of sustainable supplier selection demonstrates the rationality, objectivity, flexibility, and stability of the results of the proposed model. Further, the proposed framework makes it possible to evaluate suppliers in terms of sustainability in spite of ambiguities in the decision-making process and a lack of quantitative information. Finally, a real world example of home appliance manufacturer in Serbia is discussed to verify the proposed model applicability.

Sustainable development (SD) can be considered as a bridge incorporating economic development and environmental protection. Sustainability in oil industry is a crucial environmental issue, from the extraction to the delivery levels. As a result of the rapidly increasing population and industrialization, there would not only be the energy demand but also an increase in oil production in next decades affecting SD. Unfortunately, some industries including oil production are directly related to the CO2 and other harmful emissions making it an important issue in terms of sustainability. Therefore, assessment of sustainability performance of OPEC countries is of vital importance affecting the global energy sectors hierarchies. Along this line, via using a multiple attribute decision making (MADM) approach namely Combined Compromise Solution (CoCoSo), the OPEC countries are analysed according to 41 SD indicators in 10 dimensions in this study. Twelve selected main members of OPEC are evaluated based on the official real data and the outputs are tested through sensitivity analyses compared to other extensively known robust MADM methods. Based on the findings, United Arab Emirates is the most sustainable OPEC country with 71.9% performance score as well as Qatar, Kuwait, and Iran are positioned next with 69.3%, 66.6, and 56.2% performance scores, respectively. High correlation (greater than 97%) of the method used in this study compared to other robust MADM methods (WASPAS, MABAC, CODAS, and VIKOR) demonstrated the effectiveness and usefulness of the proposed method.

Experience from the banking crises during the past two decades suggest that advanced prediction models are needed for helping prevent bank failures. This paper compares the ability of artificial neural networks and support vector machines in predicting bank failures. Although artificial neural networks have widely been applied complex problems in business, the literature utilizing support vector machines is relatively narrow and their capability for predicting bank failures is not very familiar. In this paper, these two intelligent techniques are applied to a dataset of Turkish commercial banks. Empirical findings show that although the prediction performance of the two models can be considered as satisfactory, neural networks show slightly better predictive ability than support vector machines. In addition, different types of error from each model also indicate that neural network models are better predictors.

Extantliterature on the financial analysis of football clubs has applied a vast arrayof techniques in determining the fiscal stability of such entities. Althoughmany studies have provided useful comparisons and analyses of the states ofvarious clubs, direct research comparing the financial performance amongvarious clubs is still scant. Hence, we present an application of financialratio analysis to the greater Italian football market within the Serie A. The mainpurpose of this study is to assess the financial performance of the top threeItalian football clubs currently listed on the Borsa Italiana (Italian StockExchange), those being: Juventus F.C., A.S. Roma, and S.S. Lazio. In accomplishingthis, we offer a comparative analysis of these clubs through the usage of greyrelational analysis (GRA), an optimal performance technique derived from engineering.Overall, our results suggest that S.S. Lazio appears to be the most financiallystable club among the sampled, publicly traded Serie A teams. In light of thesefindings, this study furthers the application of economic evaluation intolarger segments of international football.

Tedarikçi seçimi, küreselleşme ile artan rekabet ortamında firmalar için en kritik faaliyetlerden biridir. Tedarik zinciri yönetimindeki bu önemli sorunun üstesinden gelmek ise firmalara önemli ölçüde rekabet avantajı sağlamaktadır. Bu çalışmanın amacı Analitik Hiyerarşi Prosesi (AHP) ve VIKOR (VIse Kriterijumsa Optimizacija I Kompromisno Resenje) entegre yöntemi ile en iyi tedarikçinin seçimidir. Çalışmada maliyet, kalite, teslimat, profil ve esneklik kriterleri ana kriter olarak belirlenmiş ve bu kriterlere ilişkin 12 alt kriter tespit edilmiştir. Ayrıca, çalışmada AHP yöntemi kriterlerin göreli öneminin tespit edilmesi için, VIKOR yöntemi ise alternatiflerin sıralanmasında kullanılmıştır. Uygulama sonucunda en önemli kriter “teslimat” olarak belirlenmiştir. Çalışma AHP-VIKOR entegre yönteminin tedarikçi seçiminde kullanılabilecek etkin bir yöntem olduğunu göstermiştir.Anahtar Kelimeler: AHP, VIKOR, Tedarikçi Seçimi, Tedarikçi Değerlendirme., Supplier selection is one of the most critical activities for companies in the increasingly competitive environment due to globalization. Overcoming this important problem in supply chain management provides significant competitive advantage to companies. The aim of this study is to choose the best supplier by using the integrated method of Analytical Hierarchy Process (AHP) and VIKOR (VIse Kriterijumsa Optimizacija I Kompromisno Resenje). In this study, cost, quality, delivery, flexibility and profile were determined as the main criteria and 12 sub-criteria of these main criteria were identified. Also, while AHP method was used for identifying the relative importance of the criteria, VIKOR method was used for ranking the alternatives. As a result, “delivery” was found to be the most important criterion. The findings indicate that AHP-VIKOR integrated method is an efficient method which can be used in the selection of suppliers.Keywords: AHP, VIKOR, Supplier Selection, Supplier Evaluation.

Owner-occupied housing is both a place to live and also the most important asset in many households’ portfolio. Accurately predicting of house prices is therefore of great interest to the general public. This paper aims to compare the housing price prediction accuracies of Hedonic Model (HM) and Artificial Neural Networks (ANNs). In order to achieve this aim, two techniques’ prediction results were compared by using four performance criteria: RMSE, MAE, MAD, and Theil’s U statistic. This study uses the HM and ANNs to empirically determine the house prices in Turkey. HM is the standard technique for modeling the behavior of house prices over the past three decades and is based on micro economic theory. The non-linear relationship between house price and its determinants can be modeled by an ANN, so it is employed in this paper as an alternative method. Empirical results revealed that ANNs performed better than HM in house price predictions, indicating that ANNs could be useful for prediction of house prices. More clearly, the performance criteria from the ANNs are smaller than those from the HM by roughly 60-90%. For instance, the ANN model has about 77 percent lower RMSE, 91 percent lower MAE, 64 percent lower MAD, and 77 percent lower Theil’s U statistic than those of the HM.

Fuzzy TOPSIS method used group decision making in fuzzy environment is one of the Multiple Criteria Decision Making (MCDM) methods. It is needed to decision makers (DM), alternatives and decision criteria in order to apply this method. Foundation of the method is the ideal solution is the shortest distance from Fuzzy Positive Ideal Solution (FPIS) and the farthest distance from Fuzzy Negative Ideal Solution (FNIS). Using FPIS and FNIS, closeness coefficients of alternatives are evaluated. Closeness coefficients express scores of the alternatives. According to closeness coefficients, alternatives are ranked from the best to the worst. In this study, two fuzzy TOPSIS methods having different algorithms are compared. To this purpose, firstly assessments of decision makers are converted to triangular fuzzy numbers. It is seen at the end of the study that ranking orders of alternatives don’t change.