Analyzing the ranking method for L-R fuzzy numbers based on deviation degree

Ranking fuzzy numbers based on their left and right deviation degree (L-R deviation degree) has attracted the attention of many scholars recently, yet most of their ranking methods have two systematic shortcomings that are usually ignored. This paper addresses these shortcomings and proves them through mathematical proofs instead of providing counter-examples. Applying our analyses will help other authors avoid some common errors when building their own ranking index functions. We use Asady's ranking index function (2010) as an example when we present our arguments and proofs and provide fully detailed analyses of two of the ranking index functions herein. Based on these analyses, an algorithm for detecting inconsistencies in ranking results is proposed, and numerical examples are given to illustrate our arguments.

[1]  Saeid Abbasbandy,et al.  A note on "The revised method of ranking LR fuzzy number based on deviation degree" , 2011, Expert Syst. Appl..

[2]  Bo Feng,et al.  Ranking L-R fuzzy number based on deviation degree , 2009, Inf. Sci..

[3]  T. Chu,et al.  A Fuzzy TOPSIS Method for Robot Selection , 2003 .

[4]  Y. Chen,et al.  A methodology of determining aggregated importance of engineering characteristics in QFD , 2007, Comput. Ind. Eng..

[5]  A. Saeidifar Application of weighting functions to the ranking of fuzzy numbers , 2011, Comput. Math. Appl..

[6]  Miao-Ling Wang,et al.  Ranking Fuzzy Number Based on Lexicographic Screening Procedure , 2005, Int. J. Inf. Technol. Decis. Mak..

[7]  H. B. Mitchell,et al.  On ordering fuzzy numbers , 2000, International Journal of Intelligent Systems.

[8]  Ching-Hsue Cheng,et al.  A new approach for ranking fuzzy numbers by distance method , 1998, Fuzzy Sets Syst..

[9]  Mao-Jiun J. Wang,et al.  Ranking fuzzy numbers with integral value , 1992 .

[10]  B. Asady,et al.  RANKING FUZZY NUMBERS BY DISTANCE MINIMIZATION , 2007 .

[11]  B. Asady,et al.  The revised method of ranking LR fuzzy number based on deviation degree , 2010, Expert Syst. Appl..

[12]  Mashaallah Mashinchi,et al.  Ranking fuzzy numbers based on the areas on the left and the right sides of fuzzy number , 2011, Comput. Math. Appl..

[13]  Amit Kumar,et al.  A new approach for ranking of L-R type generalized fuzzy numbers , 2011, Expert Syst. Appl..

[14]  Hsuan-Shih Lee,et al.  The revised method of ranking fuzzy numbers with an area between the centroid and original points , 2008, Comput. Math. Appl..

[15]  T. Chu,et al.  Ranking fuzzy numbers with an area between the centroid point and original point , 2002 .

[16]  Ching-Hsue Cheng,et al.  Fuzzy system reliability analysis by interval of confidence , 1993 .

[17]  Ying Luo,et al.  Area ranking of fuzzy numbers based on positive and negative ideal points , 2009, Comput. Math. Appl..

[18]  C. K. Kwong,et al.  A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment , 2002, J. Intell. Manuf..

[19]  Shyi-Ming Chen,et al.  A new method for analyzing fuzzy risk based on a new fuzzy ranking method between generalized fuzzy numbers , 2009, ICMLC 2009.

[20]  R. Goetschel,et al.  Elementary fuzzy calculus , 1986 .