A New Way of Applying Interval Fuzzy Logic in Group Decision Making For Supplier Selection

Today, there is strong competition in the market, and therefore an adequate process of selecting suppliers, as one of the key activities in the supply chain management, can contribute to the company's overall business. In an empirical research that dealt with the selection of suppliers for a paper manufacturing company, it was necessary to select a supplier that would contribute to improving the environmental awareness of this company. The selection of suppliers in this paper is based on the application of the interval fuzzy logic and the group decision making model. Interval fuzzy logic was applied to the AHP and TOPSIS methods. The AHP method has determined the weights of the criteria, while TOPSIS method ranks the supplier based on the distance from an ideally positive and negative solution. Linguistic values were used to meet the criteria of supplier. Based on a practical example conducted on real suppliers, it has been established that supplier A6 shows the best results in relation to other suppliers. These results were confirmed by sensitivity analysis.

[1]  Ali H. Diabat,et al.  Integrated fuzzy multi criteria decision making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain , 2013 .

[2]  R. Tavakkoli-Moghaddam,et al.  A multi-stage decision-making process for multiple attributes analysis under an interval-valued fuzzy environment , 2013 .

[3]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[4]  Basar Öztaysi,et al.  A Group Decision Making Approach Using Interval Type-2 Fuzzy AHP for Enterprise Information Systems Project Selection , 2015, J. Multiple Valued Log. Soft Comput..

[5]  Morteza Yazdani,et al.  Risk Analysis of Critical Infrastructures Using Fuzzy Copras , 2011 .

[6]  Francisco Rodrigues Lima Junior,et al.  A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection , 2014, Appl. Soft Comput..

[7]  Mithat Zeydan,et al.  A combined methodology for supplier selection and performance evaluation , 2011, Expert Syst. Appl..

[8]  M. Gorzałczany.  A method for inference in approximate reasoning based on interval-valued fuzzy sets , 1987 .

[9]  E. Zavadskas,et al.  A new fuzzy additive ratio assessment method (ARAS‐F). Case study: The analysis of fuzzy multiple criteria in order to select the logistic centers location , 2010 .

[10]  Wolfgang Hauke.  Fuzzy Multiple Attribute Decision Making (Fuzzy-MADM) , 1998 .

[11]  Da Ruan,et al.  Multi-Objective Group Decision Making - Methods, Software and Applications with Fuzzy Set Techniques(With CD-ROM) , 2007, Series in Electrical and Computer Engineering.

[12]  Ahmad Makui,et al.  Extension of fuzzy TOPSIS method based on interval-valued fuzzy sets , 2009, Appl. Soft Comput..

[13]  Ilija Tanackov,et al.  An integrated fuzzy AHP and TOPSIS model for supplier evaluation , 2016 .

[14]  Edmundas Kazimieras Zavadskas,et al.  A Novel Hybrid MCDM Approach for Complicated Supply Chain Management Problems in Construction , 2017 .

[15]  Cengiz Kahraman,et al.  FUZZY ANALYTIC HIERARCHY PROCESS WITH TYPE-2 FUZZY SETS , 2012 .

[16]  Golam Kabir.  THIRD PARTY LOGISTIC SERVICE PROVIDER SELECTION USING FUZZY AHP AND TOPSIS METHOD , 2012 .

[17]  Selin Soner Kara,et al.  Long term supplier selection using a combined fuzzy MCDM approach: A case study for a telecommunication company , 2009, Expert Syst. Appl..

[18]  Chen-Tung Chen,et al.  Extensions of the TOPSIS for group decision-making under fuzzy environment , 2000, Fuzzy Sets Syst..

[19]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[20]  T. Saaty,et al.  The Analytic Hierarchy Process , 1985 .

[21]  Jurgita Antucheviciene,et al.  A New Method of Assessment Based on Fuzzy Ranking and Aggregated Weights (AFRAW) for MCDM Problems Under Type-2 Fuzzy Environment , 2016 .