4.7 Article

Two new methods for deriving the priority vector from interval multiplicative preference relations

期刊

INFORMATION FUSION
卷 26, 期 -, 页码 122-135

出版社

ELSEVIER
DOI: 10.1016/j.inffus.2014.12.002

关键词

Decision analysis; AHP; Eigenvalue method; Row geometric mean method; Interval multiplicative preference relation

资金

  1. State Key Program of National Natural Science of China [71431006]
  2. Funds for Creative Research Groups of China [71221061]
  3. Projects of Major International Cooperation NSFC [71210003]
  4. National Natural Science Foundation of China [71201089, 71271217, 71201110, 71271029]
  5. National Science Foundation for Post-doctoral Scientists of China [2014M560655]
  6. Program for New Century Excellent Talents in University of China [NCET-12-0541]

向作者/读者索取更多资源

Interval preference relations are widely used in the analytic hierarchy process (AHP) for their ability to express the expert's uncertainty. The most crucial issue arises when deriving the interval priority vector from the interval preference relations. Based on two of the most commonly used prioritization methods (the eigenvalue method (EM) and the row geometric mean method (RGMM)), two new methods for obtaining the interval priority vector from interval multiplicative preference relations are developed, which endow the expert with different risk preferences for his/her interval judgments. In contrast to existing methods, new approaches calculate the interval priority weights of alternatives separately. Then, several concepts of acceptable consistency for interval multiplicative preference relations are defined. Using a convex combination method, the acceptable consistency of interval multiplicative preference relations can be derived from the associated and exact numerical relations. To increase the distinction of intervals, an improved interval ranking method is presented. After that, two algorithms that can cope with acceptably and unacceptably consistent cases are introduced. Meanwhile, three numerical examples are examined to show the application of the new approaches, and comparisons with several other methods are also made. (C) 2014 Elsevier B.V. All rights reserved.

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