Results 261 to 270 of about 559,365 (305)

Targeting in Multi-Criteria Decision Making

Proceedings of the AAAI Conference on Artificial Intelligence
In this work, we introduce the notion of targeting for multi-criteria decision making. The problem involves selecting the best alternatives related to one particular alternative, called the target. We use an axiomatic approach to this problem by establishing properties that any targeting method should satisfy.
Nicolas Schwind, Patricia Everaere, Sébastien Konieczny, Emmanuel Lonca   +3 more
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Multi-criteria decision making with skyline computation

2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), 2012
Multi-criteria decision making is one of the most critical and yet most challenging components in modern enterprise business intelligence. It is well known that complex business decisions are often made based on multi-dimensional criteria. The competitiveness of optimal business decision making typically resorts to finding a good trade-off among many ...
Wonik Choi, Ling Liu 0001, Boseon Yu
openaire   +1 more source

Semantic Multi-Criteria Decision Making SeMCDM

2009 IEEE Symposium on Computational Intelligence in Milti-Criteria Decision-Making, 2009
Multi-Criteria Decision Making promises important development in the capabilities of intelligent autonomous and adaptive devices. Ontologies and inference engines enable semantic matching between criteria. This paper presents an approach to take benefit from these technologies in distributed autonomous devices.
Ghadi Mahmoudi, Christian Müller-Schloer   +1 more
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Multi Criteria Decision Making

2016
Multi Criteria Decision Making (MCDM) is one of the technique which is used to select most optimal alternative with respect to multiple criteria for a specific goal. The method provides objectivity and compares the alternative relatively to estimate the priority value of the alternatives.
Mrinmoy Majumder, Apu K. Saha
openaire   +1 more source

Multi-criteria optimization and decision-making in radiotherapy

European Journal of Operational Research, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sebastiaan Breedveld, David Craft, Rens van Haveren, Ben Heijmen   +3 more
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Multi-Criteria Decision Making

2002
One aspect that most of the current research on evolutionary multiobjective optimization (EMO) often disregards is the fact that the solution of a multiobjective optimization problem (MOP) really involves three stages: measurement, search, and decision making.
Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont   +2 more
openaire   +1 more source

Adapting and refining in multi-criteria decision-making

Journal of the Operational Research Society, 2010
This paper describes the implementation of a Structured Methodology for Direct-Interactive Structured-Criteria (DISC) Multi-Criteria Decision-Making (MCDM), an eight-stage nomological adjusting cycle of activities that shape the information used to make a decision, requiring it be accessible, differentiable, abstractable, understandable, verifiable ...
D. B. O'Brien, Cathal M. Brugha
openaire   +1 more source

Multi-Criteria Decision Making

2019
Multi-criteria decision making (MCDM) is a discipline that explicitly considers assessing alternatives in a decision problem with respect to multiple criteria. Those methods are frequently used to solve real-life decision problems that incorporate multiple, conflicting, and incommensurate criteria. Considering the chaotic, complex, and ambiguous nature
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Multiclass Classification Based on Multi-criteria Decision-making

Journal of Classification, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hossein Baloochian, Hamid Reza Ghaffary
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Multi-Criteria Decision Making for Ranking Decision Making Units

International Journal of Productivity Management and Assessment Technologies, 2018
There is no doubt the Data Envelopment Analysis (DEA) is a powerful method for the efficiency evaluation of Decision Making Units (DMUs) with multiple inputs and outputs. Despite its usefulness, DEA has some notable limitations. A significant drawback with this approach is that inability to fully rank the DMUs.
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