An improved method on group decision making based on interval-valued intuitionistic fuzzy prioritized operators

Abstract Interval-valued intuitionistic fuzzy prioritized operators are widely used in group decision making under uncertain environment due to its flexibility to model uncertain information. However, there is a shortcoming in the existing aggregation operators (interval-valued intuitionistic fuzzy prioritized weighted average (IVIFPWA)) to deal with group decision making in some extreme situations. For example, when an expert gives an absolute negative evaluation, the operators could lead to irrational results, so that they are not effectively enough to handle group decision making. In this paper, several examples are illustrated to show the unreasonable results in some of these situations. Actually, these unreasonable cases are common for operators in dealing with product averaging, not only emerging in IVIFPWA operators. To overcome the shortcoming of these kinds of operators, an improvement of making slight adjustment on initial evaluations is provided. Numerical examples are used to show the efficiency of the improvement.

[1]  Ibrahim A. Baky,et al.  TOPSIS for bi-level MODM problems , 2013 .

[2]  Ming-Cheng Tsou,et al.  Application of a mixed fuzzy decision making and optimization programming model to the empty container allocation , 2010, Appl. Soft Comput..

[3]  Sankaran Mahadevan,et al.  Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment , 2012, Appl. Soft Comput..

[4]  Manoj Kumar Tiwari,et al.  Global supplier selection: a fuzzy-AHP approach , 2008 .

[5]  Ronald R. Yager,et al.  Modeling prioritized multicriteria decision making , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Huseyin Selcuk Kilic,et al.  An integrated approach for supplier selection in multi-item/multi-supplier environment , 2013 .

[7]  Sankaran Mahadevan,et al.  Evidential cognitive maps , 2012, Knowl. Based Syst..

[8]  Hu-Chen Liu,et al.  Induced aggregation operators in the VIKOR method and its application in material selection , 2013 .

[9]  Zili Zhang,et al.  A biologically inspired solution for fuzzy shortest path problems , 2013, Appl. Soft Comput..

[10]  F. Lai,et al.  Managing dependence in logistics outsourcing relationships: evidence from China , 2013 .

[11]  Ronald R. Yager,et al.  Prioritized aggregation operators , 2008, Int. J. Approx. Reason..

[12]  Yong Deng,et al.  A new optimal consensus method with minimum cost in fuzzy group decision , 2012, Knowl. Based Syst..

[13]  Yong Deng,et al.  A modified multi-criterion optimization genetic algorithm for order distribution in collaborative supply chain , 2013 .

[14]  S. M. Mousavi,et al.  A new design of the elimination and choice translating reality method for multi-criteria group decision-making in an intuitionistic fuzzy environment , 2013 .

[15]  Tien-Chin Wang,et al.  A proposed model for measuring the aggregative risk degree of implementing an RFID digital campus system with the consistent fuzzy preference relations , 2013 .

[16]  Jeng-Ming Yih,et al.  An evaluation of airline service quality using the fuzzy weighted SERVQUAL method , 2011, Appl. Soft Comput..

[17]  D. Braconi,et al.  Prevalence of Isolated Atrial Amyloidosis in Young Patients Affected by Congestive Heart Failure , 2012, TheScientificWorldJournal.

[18]  Vipul Jain,et al.  Quantifying risks in a supply chain through integration of fuzzy AHP and fuzzy TOPSIS , 2013 .

[19]  Anjali Awasthi,et al.  A hybrid approach integrating Affinity Diagram, AHP and fuzzy TOPSIS for sustainable city logistics planning , 2012 .

[20]  Deng Yong,et al.  A TOPSIS-BASED CENTROID–INDEX RANKING METHOD OF FUZZY NUMBERS AND ITS APPLICATION IN DECISION-MAKING , 2005 .

[21]  Dejian Yu,et al.  Interval-valued intuitionistic fuzzy prioritized operators and their application in group decision making , 2012, Knowl. Based Syst..

[22]  Guiwu Wei,et al.  Some hesitant interval-valued fuzzy aggregation operators and their applications to multiple attribute decision making , 2013, Knowl. Based Syst..

[23]  F. Chan,et al.  IFSJSP: A novel methodology for the Job-Shop Scheduling Problem based on intuitionistic fuzzy sets , 2013 .

[24]  Z. Xu,et al.  Methods for aggregating interval-valued intuitionistic fuzzy information and their application to decision making , 2007 .

[25]  J. H. Park,et al.  Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment , 2011 .

[26]  Yong Hu,et al.  TOPPER: Topology Prediction of Transmembrane Protein Based on Evidential Reasoning , 2013, TheScientificWorldJournal.