Towards a New Multicriteria Decision Support Method Using Fuzzy Measures and the Choquet Integral

Author:

Alnafie Emdjed1,Hamdadou Djamila1,Bouamrane Karim1

Affiliation:

1. University of Oran1 Ahmed Ben Bella, Oran, Algeria

Abstract

In literature, there is a large panoply of multicriteria analysis methods (MCAM), each one is characterized by the nature of its input data, the way to edit its outputs and the operations used to perform calculations especially the performances aggregation. Aggregation is the operation consisting in grouping several quantities in a unique value in order to facilitate the manipulation and the interpretation of the original values. MCAM are classified according to the type of aggregation that they perform, so we can distinguish total, partial and local aggregation. Each MCAM has advantages and suffers from some limits. In this paper, the authors proposed a new multicriteria analysis method (AMFI) dedicated to solve ranking decision support problems. AMFI is based on the use of fuzzy measures and Choquet integral to represent interactions between criteria and improve the coherence of the results. The authors proceeded to a series of experimentations allowing highlighting theoretical elements of the proposed method and they performed sensitivity analysis to test its robustness.

Publisher

IGI Global

Subject

General Computer Science

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Towards a Group Decision Support System;Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering;2021

2. A two-step multi-objectivization method for improved evolutionary optimization of industrial problems;Applied Soft Computing;2018-03

3. Prioritized Aggregation Operators and Correlated Aggregation Operators for Hesitant 2-Tuple Linguistic Variables;Symmetry;2018-01-30

4. Towards a Group Decision Support System;International Journal of Distributed Artificial Intelligence;2018-01

5. Application of satisfactory degree to interval-valued intuitionistic fuzzy multi-attribute decision making;Journal of Intelligent & Fuzzy Systems;2017-01-13

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