Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A feedforward neural network for multiple criteria decision making
Computers and Operations Research
Applied Mathematics and Computation - Special issue on multicriterion decision making with engineering applications
Artificial neural network representations for hierarchical preference structures
Computers and Operations Research
Applied Mathematics and Computation
Interactive multiple objective programming using Tchebycheff programs and artificial neural networks
Computers and Operations Research - Special issue on artificial intelligence and decision support with multiple criteria
The relationship between citations and number of downloads in Decision Support Systems
Decision Support Systems
Intelligent fuzzy multi-objective optimization: analysis and new research directions
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Algorithms for fuzzy multi expert multi criteria decision making (ME-MCDM)
Knowledge-Based Systems
Modeling decision-maker preferences through utility function level sets
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
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In this paper, a new approach for solving multiple criteria decision-making (MCDM) problems is proposed based on Decision Neural Network (DNN). The DNN is used to capture and represent the Decision Maker's (DM's) preference. Then, with DNN, an optimization problem is solved to search for the most desirable solution. Procedures of model modification through an interactive procedure, and model optimization are also discussed. An example is given to illustrate the approach and the result is compared with the Interactive FFANN Procedure [Manage. Sci. 42 (1996)]. The result shows that the DNN approach is an encouraging and robust method for solving MCDM problems.