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INTEGRATING VISUALIZATION AND MULTI-ATTRIBUTE UTILITY THEORY FOR ONLINE PRODUCT SELECTION
International Journal of Information Technology & Decision Making, 2007Effectively selling products online is a challenging task. Today's product domains often contain a dizzying variety of brands and models with highly complex sets of characteristics. This paper addresses the problem of supporting product search and selection in domains containing large numbers of alternatives with complex sets of features.
Churee Theetranont +2 more
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Multi-attribute utility theory for measuring safety
European Journal of Operational Research, 1982Abstract The advent of new technology, much of which is complex and dangerous, has produced the need for defensible and sound methods for measuring safety and for determining whether a given level of safety is appropriate. This is particularly true in the nuclear industry where most of the opposition to nuclear power is concerned with the safety of ...
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Selection of Inspection Intervals Based on Multi-attribute Utility Theory
2013In the context of complex equipment, inspection has an important role to play in maintaining operations at a suitable performance. In fact, for this kind of system, failures are not immediate. Therefore, the concept of delay time is very useful as a two-step failure process, as a basis for constructing inspection models.
Rodrigo J. P. Ferreira +2 more
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Maintenance Optimization Using Multi-attribute Utility Theory
2015Several factors such as reliability, availability, and cost may consider in the maintenance modeling. In order to develop an optimal inspection program, it is necessary to consider the simultaneous effect of above factor in the model structure. In addition, for finding the optimal maintenance interval it is necessary to make trade-offs between several ...
A. H. S. Garmabaki +2 more
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Multi-Attribute Utility Theory: Models and Assessment Procedures
1975This article reviews multi-attribute utility theory under a measurement theoretic perspective. It describes and classifies decision situations according to three salient aspects of choice: uncertainty, time-variability, and multi-dimensionality. For each choice situation the main mathematical representations, their interrelations and differences are ...
Detlof Von Winterfeldt +1 more
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Multi-Attribute Utility Theory
2012Multi-attribute utility theory is an extension of Utility theory developed to help decision-makers assign utility values, taking into consideration the decision-maker’s preferences, to outcomes by evaluating these in terms of multiple attributes and combining these individual assignments to obtain overall utility measures.
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Application of Multi-Attribute Utility Theory
1977Tversky (1967) identified a major goal of scientific investigation as the decomposition of complex phenomena into sets of ‘basic’ factors according to specifiable rules of combination. Following this approach, this review evaluates Multi-attribute Utility Theory (MAUT) in terms of its claim to success in providing an axiomatization of decomposition and
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A Nonlinear Multi-Attribute Utility Theory
1985In this paper we discuss a new nonlinear Multi-Attribute Utility Function (MAUF) which is quasi-concave. Three types of information on the local scaling constants (coefficients) are defined which are partial, complete, and global. We discuss optimality and how to obtain the most preferred alternative using a partially known MAUF.
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PRODUCT AND PROCESS COST ESTIMATION WITH FUZZY MULTI-ATTRIBUTE UTILITY THEORY
The Engineering Economist, 1999Abstract The need to provide cost-effective manufacturing technologies is critical in today's globally competitive environment. The ability to make sound decisions about the cost effectiveness of new technologies is the key to achieving the affordability goals of future products and systems.
P.-K. DEAN TING +4 more
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Multi-Attribute Utility Theory Based K-Means Clustering Applications
International Journal of Data Warehousing and Mining, 2017One of major obstacles in the application of the k-means clustering algorithm is the selection of the number of clusters k. The multi-attribute utility theory (MAUT)-based k-means clustering algorithm is proposed to tackle the problem by incorporating user preferences.
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