Weighted minimum and maximun operations in fuzzy sets theory
Information Sciences: an International Journal
On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
The PROMCALC & GAIA decision support system for multicriteria decision aid
Proceedings of the conference on First specialized conference on decision support systems
Construction of aggregation functions from data using linear programming
Fuzzy Sets and Systems
Optimisation of garment design using fuzzy logic and sensory evaluation techniques
Engineering Applications of Artificial Intelligence
Loss and gain functions for CBR retrieval
Information Sciences: an International Journal
Introducing attribute risk for retrieval in case-based reasoning
Knowledge-Based Systems
An application of multi-criteria decision aids models for Case-Based Reasoning
Information Sciences: an International Journal
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This contribution presents a new approach on weights determination in industrial decision making aided by OWA operators. Multi-criteria decision aid is a good way, for an industrialists, to determine his preferred compromise products, in the case of risk products or innovative products. The multi-criteria decision support chosen is the Ordered Weighted Average (OWA) operators, introduced by Yager [R.R. Yager, On ordered weighted averaging aggregation operators in multicriteria decision making, IEEE Trans. Syst. Man Cybern. 18 (1988) 183-190]. The interest of this aggregation method is, beyond its simplicity of use, product evaluation according unique scale. Furthermore, the weights are not fixed by criterion but according to utility level. First, a learning sample is ranked by the decision-maker. Then, this ranked sample is used in order to determine the weights by parametric identification. For this, an hypothesis of equipartition of the scores of each sample is used. An industrial application, from a food production, illustrates this approach. The ranks obtained from several samples are compared.