Dominance degrees for intervals and their application in multiple attribute decision-making

Abstract Since intervals are used to express decision information in various decision-making problems, it is necessary to give a feasible method to rank them. The most commonly used method is to employ the possibility degree to compare intervals in pairs, then construct a fuzzy preference relation. Generally, the constructed fuzzy preference relation does not keep (additive, multiplicative) consistency. In order to ensure the consistency of decision information, optimization models are constructed by using the logical relationship between the elements of the consistent fuzzy preference relation and their weights. Then, by solving the optimization models, consistent fuzzy preference relations are obtained. The consistent fuzzy preference relations are the proxy of the original inconsistent fuzzy preference relation constructed by the possibility degree. However, such treatment is flawed. The inconsistent fuzzy preference relation generated from the possibility degree can't guarantee the reliability of the result while the proxy consistent fuzzy preference relation would cause bias. To solve this problem, the additive dominance degree and multiplicative dominance degree for intervals are proposed as a result of indirect comparison. The fuzzy preference relations are constructed from the dominance degrees of intervals to ensure the consistency of decision information. In the process of ranking intervals, this can avoid decision-making bias. Furthermore, a new approach to interval multiple attribute decision-making is proposed based on the dominance degrees.

[1]  Yao Wei-xing Comments on methods for ranking interval numbers , 2010 .

[2]  Tapan Kumar Pal,et al.  On comparing interval numbers , 2000, Eur. J. Oper. Res..

[3]  Jibin Lan,et al.  A new linguistic aggregation operator and its application to multiple attribute decision making , 2015 .

[4]  Z. S. Xu,et al.  The uncertain OWA operator , 2002, Int. J. Intell. Syst..

[5]  Luis G. Vargas,et al.  Uncertainty and rank order in the analytic hierarchy process , 1987 .

[6]  Ranking Method of Interval Numbers Based on Quantity Property , 2011 .

[7]  Yejun Xu,et al.  A distance-based framework to deal with ordinal and additive inconsistencies for fuzzy reciprocal preference relations , 2016, Inf. Sci..

[8]  Francisco Herrera,et al.  Managing non-homogeneous information in group decision making , 2005, Eur. J. Oper. Res..

[9]  Yozo Nakahara,et al.  On the linear programming problems with interval coefficients , 1992 .

[10]  Lu Yue Weight Calculation Method of Fuzzy Analytical Hierarchy Process , 2002 .

[11]  Daniel Mora-Meliá,et al.  Modeling bidding competitiveness and position performance in multi-attribute construction auctions , 2015 .

[12]  Zeshui Xu,et al.  A least deviation method to obtain a priority vector of a fuzzy preference relation , 2005, Eur. J. Oper. Res..

[13]  Jian-Bo Yang,et al.  Interval weight generation approaches based on consistency test and interval comparison matrices , 2005, Appl. Math. Comput..

[14]  Meimei Xia,et al.  Studies on Interval Multiplicative Preference Relations and Their Application to Group Decision Making , 2015 .

[15]  Jian-Bo Yang,et al.  A two-stage logarithmic goal programming method for generating weights from interval comparison matrices , 2005, Fuzzy Sets Syst..

[16]  Zeshui Xu,et al.  On Compatibility of Interval Fuzzy Preference Relations , 2004, Fuzzy Optim. Decis. Mak..

[17]  Zhang Quan,et al.  A Ranking Approach for Interval Numbers in Uncertain Multiple Attribute Decision Making Problems , 1999 .

[18]  Linus J. Luotsinen,et al.  Simulation-based decision support for evaluating operational plans , 2015 .

[19]  Zeshui Xu,et al.  Uncertain Multi-Attribute Decision Making , 2015 .

[20]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[22]  Zhiming Zhang,et al.  Logarithmic least squares approaches to deriving interval weights, rectifying inconsistency and estimating missing values for interval multiplicative preference relations , 2017, Soft Comput..

[23]  G. Facchinetti,et al.  Note on ranking fuzzy triangular numbers , 1998 .

[24]  Leping Liu,et al.  An Information Technology Integration System and Its Life Cycle , 2003 .

[25]  Zhou-Jing Wang,et al.  A two-stage linear goal programming approach to eliciting interval weights from additive interval fuzzy preference relations , 2016, Soft Comput..

[26]  Sukhamay Kundu,et al.  Min-transitivity of fuzzy leftness relationship and its application to decision making , 1997, Fuzzy Sets Syst..

[27]  Hideo Tanaka,et al.  Interval priorities in AHP by interval regression analysis , 2004, Eur. J. Oper. Res..

[28]  S. Orlovsky Decision-making with a fuzzy preference relation , 1978 .

[29]  Zeshui Xu,et al.  Note on “Some models for deriving the priority weights from interval fuzzy preference relations” , 2008 .

[30]  Fang Liu,et al.  On possibility-degree formulae for ranking interval numbers , 2018, Soft Comput..

[31]  Shu Ning,et al.  Edge detection method of remote sensing images based on mathematical morphology of multi-structure elements , 2004 .

[32]  P. Sevastianov Numerical methods for interval and fuzzy number comparison based on the probabilistic approach and Dempster-Shafer theory , 2007 .

[33]  Fang Liu,et al.  Acceptable consistency analysis of interval reciprocal comparison matrices , 2009, Fuzzy Sets Syst..

[34]  D. Djoković,et al.  On the Hadamard product of matrices , 1965 .

[35]  T. Tanino Fuzzy preference orderings in group decision making , 1984 .