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Volume 29 (2024)
Volume 29, Issue 1 (In Progress)
Pg 1 - 37 (June 2024)
Volume 28 (2023)
Volume 28, Issue 2
Pg 65 - 119 (December 2023)
Volume 28, Issue 1
Pg 1 - 64 (June 2023)
Volume 27 (2022)
Volume 27, Issue 2
Pg 169 - 273 (December 2022)
Volume 27, Issue 1
Pg 1 - 167 (June 2022)
Volume 26 (2021)
Volume 26, Issue 2
Pg 103 - 209 (December 2021)
Volume 26, Issue 1
Pg 1 - 101 (June 2021)
Volume 25 (2020)
Volume 25, Issue 2
Pg 85 - 142 (December 2020)
Volume 25, Issue 1
Pg 1 - 84 (June 2020)
Volume 24 (2019)
Volume 24, Issue 2
Pg 55 - 100 (December 2019)
Volume 24, Issue 1
Pg 1 - 54 (June 2019)
Volume 23 (2018)
Volume 23, Issue 2-3
Pg 73 - 127 (November 2018)
Volume 23, Issue 1
Pg 1 - 72 (May 2018)
Volume 22 (2017)
Volume 22, Issue 4
Pg 193 - 223 (December 2017)
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Pg 137 - 191 (September 2017)
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Pg 71 - 136 (June 2017)
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Pg 1 - 70 (March 2017)
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Pg 265 - 315 (December 2016)
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Pg 187 - 264 (September 2016)
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Pg 107 - 185 (June 2016)
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Pg 1 - 86 (September 2014)
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Pg 1 - 93 (March 2014)
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Pg 65 - 123 (December 2013)
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Pg 1 - 64 (October 2013)
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Pg 73 - 111 (August 2013)
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Pg 1 - 71 (June 2013)
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Pg 71 - 132 (April 2013)
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Pg 1 - 69 (February 2013)
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Pg 77 - 142 (December 2012)
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Pg 1 - 75 (October 2012)
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Pg 79 - 143 (August 2012)
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Pg 65 - 129 (April 2012)
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Pg 1 - 64 (February 2012)
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Pg 1 - 75 (October 2011)
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Pg 95 - 179 (August 2010)
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Pg 1 - 94 (June 2010)
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Pg 1 - 80 (February 2010)
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Pg 237 - 355 (October 2009)
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Pg 121 - 236 (June 2009)
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Pg 1 - 120 (February 2009)
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Pg 255 - 387 (October 2008)
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Pg 131 - 254 (June 2008)
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Pg 1 - 129 (February 2008)
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Volume 2, Issue 3
Pg 217 - 320 (October 2007)
Volume 2, Issue 2
Pg 109 - 216 (June 2007)
Volume 2, Issue 1
Pg 1 - 108 (February 2007)
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Volume 1, Issue 3
Pg 187 - 271 (October 2006)
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Pg 83 - 185 (June 2006)
Volume 1, Issue 1
Pg 1 - 81 (February 2006)
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Advances in Fuzzy Sets and Systems
Advances in Fuzzy Sets and Systems
Volume 4, Issue 3, Pages 269 - 292 (October 2009)
THE EXTENDED TOPSIS METHOD WITH INTERVAL-VALUED FUZZY SETS AND EXPERIMENTAL ANALYSIS ON SEPARATION MEASURES
Ting-Yu Chen (Taiwan) and Yi-Wen Li (Taiwan)
Abstract:
The theory of interval-valued fuzzy sets has been developed and has been received valuable applications in the field of multiple attribute decision analysis. The purpose of this study is to extend the concept of TOPSIS methods for solving multiple attribute decision making problems with interval-valued fuzzy data which are capable of coping with imprecise information due to the fact that exact data may be difficult to be precisely determined since human judgments are often vague under many conditions. An experimental analysis is conducted to compare preference order by different separation measures that calculate the distance between the alternative and its positive-ideal and negative-ideal solutions. The consistency rate, the contradiction rate of the best alternative, the inversion rate between the better alternatives and worse ones, and average Spearman correlation coefficients are utilized to conduct a pairwise comparison for all separation measures. The results which are inclusive of 100 combinations of 10 different categories of number for each alternative and attribute indicate that the preference orders are hardly identical using different separation measures in the interval-valued fuzzy TOPSIS method.
Keywords and phrases:
interval-valued fuzzy set, multiple attribute decision analysis, TOPSIS, experimental analysis, separation measure.
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P-ISSN: 0973-421X
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