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JACIII Vol.8 No.3 pp. 252-259
doi: 10.20965/jaciii.2004.p0252
(2004)

Paper:

Composite Fuzzy Measure and its Application to Decision-Making

Toshihiro Kaino*, and Kaoru Hirota**

*School of Business Administration, Aoyama Gakuin University, 4-4-25 Shibuya, Shibuya-ku, Tokyo 150-8366, Japan

**Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8502, Japan

Received:
August 4,2003
Accepted:
March 20, 2004
Published:
May 20, 2004
Keywords:
composite fuzzy measure, associative, differentiation, Choquet integral, decision-making
Abstract

In applications using fuzzy measures (on real numbers), it becomes a problem how to evaluate in-between intervals each characterized by a fuzzy measure, especially when the Choquet integral is differentiated in real world problems. A composite fuzzy measure built from fuzzy measures defined on fuzzy measurable spaces has been proposed by Kaino and Hirota using composite fuzzy weights, where the measurable space of this composite fuzzy measure is the direct sum of measurable spaces. An associative, composite fuzzy measure built from a finite number of fuzzy measures is proposed and, in a constructive application, it is applied to the automobile plant capital investment decision-making problem. It is assumed that an automobile company plans to sell a new car. The current plant line has a capacity of 3,200 new cars in addition to current car lines. Using this composite fuzzy measure, differentiation of the Choquet integral becomes a quantitative index for decision-making, which is confirmed by this decision-making experiment.

Cite this article as:
Toshihiro Kaino and Kaoru Hirota, “Composite Fuzzy Measure and its Application to Decision-Making,” J. Adv. Comput. Intell. Intell. Inform., Vol.8, No.3, pp. 252-259, 2004.
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Last updated on Sep. 19, 2021