A NEW APPROACH TOWARDS EVAULATION AND SELECT‹ON OF SALESPERSON CANDIDATES: FUZZYTOPSIS

The aim of the study is to introduce Fuzzy TOPSIS method and to show how to benefit it for evaluation of salesperson candidates. Foundation of Fuzzy TOPSIS method which is one of Multiple Criteria Decision Making (MCDM) methods and to allow group decision-making in fuzzy environment is calculation of the closeness coefficients by means of Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS). According to the calculated closeness coefficients, alternatives are ranked. In the study, salesperson candidates were assessed in accordance with seven decision criteria by three decision makers (DM’s). Decision makers have done their assessments with linguistic variables then these variables transformed to positive trapezoidal fuzzy numbers. The study shows that Fuzzy TOPSIS method is very well suited method, as a decision tool, towards using for salesperson candidates evaluation and selection decisions.Fuzzy TOPSIS, Trapezoidal Fuzzy Numbers, Salesperson.

PRIORITIZING THE WEIGHTS OF THE EVALUATION CRITERIA UNDER FUZZINESS: THE FUZZY FULL CONSISTENCY METHOD – FUCOM-F

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

GRUP KARARI VERMEDE YARARLANILAN FARKLI FUZZY TOPSIS YÖNTEMLERİNİN KARŞILAŞTIRILMASI VE BİR UYGULAMA

Bulanık ortamlarda grup kararı vermede kullanılan Fuzzy TOPSIS yöntemi Çok Kriterli Karar Verme (ÇKKV) yöntemlerinden birisidir. Yöntemin uygulanabilmesi için karar vericilere (KV), alternatiflere ve karar kriterlerine gereksinim duyulur. Yöntemin temelini ideal çözümün Fuzzy Pozitif İdeal Çözüm'den (FPİÇ) en yakın, Fuzzy Negatif İdeal Çözüm'den (FNİÇ) ise en uzak mesafede olması oluşturur. FPİÇ ve FNİÇ kullanılarak alternatiflerin yakınlık katsayıları hesaplanır. Yakınlık katsayıları alternatiflerin skorlarını ifade eder. Yakınlık katsayılarına göre alternatifler sıralanır. Çalışmada farklı algoritmalara sahip iki Fuzzy TOPSIS yöntemi karşılaştırılmıştır. Bu amaçla KV'lerin değerlendirmeleri üçgen fuzzy sayılara dönüştürülmüştür. Çalışmanın sonucunda alternatiflerin sıralamasının değişmediği görülmüştür

Bulanık Ortamlarda Grup Kararı Vermeye Yardımcı Bir Yöntem: Fuzzy Topsis ve Bir Uygulama

The purpose of the study is to present Fuzzy TOPSIS method utilized for group decision-making in fuzzy environments under uncertainty. For this purpose, an interview has been conducted in a department store. Four decision makers firstly assessed decision criteria to be considered in salesperson recruitment, then assessed salesperson candidates using linguistic variables in accordance with these decision criteria. Assessments converted to triangular fuzzy numbers and using the method's algorithm, candidates were ranked in accordance with the evaluated closeness coefficients. The study showed that Fuzzy TOPSIS method could be used. Çalısmanın amacı, belirsizlik altında kararların verildigi bulanık ortamlarda, grup kararı vermede yararlanılan Fuzzy TOPSIS yöntemini ortaya koymaktır. Bu amaçla departmanlı bir magazada bir mülakat gerçeklestirilmistir. Dört karar verici önce satıs elemanı alımında dikkate alınan karar kriterlerini, sonra da bu karar kriterlerine göre satıs elemanı adaylarını dilsel degiskenlerle degerlendirmislerdir. Degerlendirmeler üçgen fuzzy sayılara dönüstürülmüs ve yöntemin algoritması kullanılarak adaylar hesaplanan yakınlık katsayılarına göre sıralanmıstır. Çalısma, Fuzzy TOPSIS yönteminin insan kaynagını seçme sürecinde bir grup kararı verme yöntemi olarak kullanılabilecegini göstermistir

A state-of-the-art review of the BWM method and future research agenda

The superiority of BWM over other weighting methods for obtaining the weight values of the attributes is that it achieves high-confidence results with a reasonable number of pairwise comparisons. Although the best-worst method (BWM) is a well-known multi-criteria decision-making (MCDM) method that has been successfully utilized in almost all scientific areas to solve challenging real-life problems, no research has comprehensively examined the state-of-the-art in this regard. The present study depicts a detailed overview of publications concerned with BWM during the period 2015–2022. Based on the information obtained from the Scopus database, this work presents a big picture of current research on BWM. In other words, this paper analyzes the existing literature about BWM and identifies thematic contexts, application areas, emerging trends, and remaining research gaps to shed light on future research agendas aligning with those gaps. Further, the most recent BWM research is analyzed in the top ten scientific areas, from engineering to materials science. “Engineering”, “computer science”, and “business, management, and accounting” are the hottest fields of BWM research. China is the most active country regarding “engineering” and “computer science”, whereas India is the leader in “business, management, and accounting”. The study also reveals that there are still many research gaps in BWM research. The big picture taken in this study will not only showcase the current situation of BWM research but will also positively impact the direction and quality of new research

Third-party logistics (3Pls) provider selection via Fuzzy AHP and EDAS integrated model

In the global competitive environment, companies not only improve the quality of service and increase the efficiency, they also decrease the cost by means of third-party logistics (3PLs). 3PLs, therefore, is an important strategy for companies desiring to gain a competitive advantage and 3PLs provider selection plays a critical role for the success of outsourcing. Nevertheless, the level of uncer­tainty in the selection process is relatively high and need to be carefully considered. Hence, in order to select a proper 3PLs provider, integration of the Fuzzy AHP and Evaluation based on Distance from Average Solution (EDAS) has offered a novel integrated model, in which Fuzzy AHP is used for calculating priority weights of each criteria and EDAS is employed to achieve the final ranking of 3PLs providers. Besides, in order to demonstrate the applicability of the proposed model, it is validated by a case study. Cost together with quality, and professionalism are found to be the most important factors for 3PLs provider selection. Consequently, the advantage of this model is that it is simple to apprehend and easy to apply. The use of the proposed model leads to the selection of suitable alternative successfully in other selection problems. First published online: 23 Apr 201

USPOREDBA PERFORMANSI NEURONSKIH MREŽA PRI PREDVIĐANJU PROPASTI BANAKA I STROJEVA S POTPORNIM VEKTORIMA: SLUČAJ TURSKE

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.Iskustvo stečeno u bankarskoj krizi u posljednja dva desetljeća upućuje na potrebu korištenja naprednih modela predviđanja u svrhu prevencije propasti banaka. Ovaj rad uspoređuje sposobnost umjetnih neuronskih mreža i strojeva s potpornim vektorima da predvide propast banaka. Iako se umjetne neuronske mreže često koriste za složene probleme u poslovanju, literatura koja spominje strojeve s potpornim vektorima je relativno malobrojna a njihova sposobnost predviđanja propasti banaka nije previše poznata. U ovom radu su ove dvije inteligentne tehnike primijenjene na sklop podataka turskih komercijalnih banaka. Empirijski rezultati pokazuju da iako se predviđanje dvaju modela može smatrati zadovoljavajućim, neuronske mreže pokazuju nešto bolju sposobnost predviđanja od strojeva s potpornim vektorima. Osim toga, različite vrste grešaka u svakom modelu također ukazuju na to da su modeli s neuronskim mrežama bolji prediktori

Applying grey relational analysis to Italian football clubs: A measurement of the financial performance of serie a teams

Extant literature on the financial analysis of football clubs has applied a vast array of techniques in determining the fiscal stability of such entities. Although many studies have provided useful comparisons and analyses of the states of various clubs, direct research comparing the financial performance among various clubs is still scant. Hence, we present an application of financial ratio analysis to the greater Italian football market within the Serie A. The main purpose of this study is to assess the financial performance of the top three Italian football clubs currently listed on the Borsa Italiana (Italian Stock Exchange), those being: Juventus F.C., A.S. Roma, and S.S. Lazio. In accomplishing this, we offer a comparative analysis of these clubs through the usage of grey relational analysis (GRA), an optimal performance technique derived from engineering. Overall, our results suggest that S.S. Lazio appears to be the most financially stable club among the sampled, publicly traded Serie A teams. In light of these findings, this study furthers the application of economic evaluation into larger segments of international football

Selection of mobile phone using analytic network process method

Öz (Tr): Karar sürecinde yer alan faktör ve alternatifler arasındaki etkileşim ve bağımlılıklar karar vermeyi güçleştirmektedir. Analitik ağ süreci yöntemi, faktör ve alternatiflerin hem birbirleriyle hem de kendi içlerindeki etkileşimlerini dikkate alarak değerlendirme yapma imkanı sunar. Diğer taraftan cep telefonu seçim kararı birbiriyle ilişkili pek çok faktörün değerlendirilmesini gerektiren ve bu nedenle de karmaşık yapıya sahip bir karar problemidir. Dolayısıyla çalışmanın amacı, çok kriterli bir karar verme yöntemi olan analitik ağ süreci yöntemi ile cep telefonu seçim kararının değerlendirilmesidir. Çalışmada, analitik ağ süreci yöntemi ile cep telefonu seçimine yönelik bir model geliştirilerek örnek bir uygulama yapılmıştır. Uygulamada cep telefonu seçimini etkileyen faktörler ve bunlara ait alt faktörler göz önünde bulundurularak dört farklı cep telefonu modeli değerlendirilmiştir. Öz (Yabancı): Interactions and interdependendes between factors and alternatives in decision process are to make difficult decision-making. Analytic network process method enables to evaluate interactions both between factors and alternatives also each others. On the other hand, mobile phone decision is a problem which is needed to evaluate many factors and so, it is a complex decision problem. Therefore, the purpose of this study is to evaluate the problem of mobile phone selection by using analytic network process method which is a multi criteria decision making method. In this study, a mobile phone selection oriented model was built up and an illustrative example was presented so as to betray disparity and robustness of the method over the existing methods. In application four mobile phone models were evaluated considering factors and their sub-factors affecting mobile phone selection and finally, the best model was selected

Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

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