Title Simple of the Technical method for Report ordinal classification in multicriteria decision making Turku Cent r e for Comput er Science

Relating decision under uncertainty and

2. Perpustakaan Universiti Teknologi Malaysia dibenarkan membuat salinan untuk tujuan pengajian sahaja. 3. Perpustakaan dibenarkan membuat salinan tesis ini sebagai bahan pertukaran antara institusi pengajian tinggi. 4. **Sila tandakan (√)

Applying evolutionary computation (EC) to multicriteria decision making addresses two difficult problems: (1) searching intractably large and complex spaces and (2) deciding among multiple objectives. Both of these problems are open areas of research, but relatively little work has been done

Multicriteria decision making models are characterized by the need to evaluate a finite set of alternatives with respect to multiple criteria. The criteria weights in dierent aggregation rules have dierent interpretations and implications which have been misunderstood and neglected by many decision

The paper presents a construction of a multicriteria decision model in a purely ordinal context, based on the use of Sugeno integral. The construction is built on the preference relation of the decision maker over a set of prototypical acts. The construction can cope also with “negative ” scores

This paper presents a synthesis on the application of fuzzy integral as an innovative tool for criteria aggregation in decision problems. The main point is that fuzzy integrals are able to model interaction between criteria in a flexible way. The methodology has been elaborated mainly in Japan

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Abstract. This work presents a method of multicriteria decision making using neutrosophic sets. Besides studying some interesting mathematical properties of the method, algorithm viz neut-MCDM is presented. The work also furnishes the fundamentals of neutrosophic set theory succinctly, to provide a

Recent research has recognised that multicriteria decision making (MCDM) should take account of uncertainty, risk and confidence. This paper takes this research forward by using linguistic variables and triangular fuzzy numbers to model the decision maker’s (DM) risk and confidence attitudes