Multicriteria decision making (mcdm): a framework for research and applications

We view Multicriteria Decision Making (MCDM) as the conjunction of three components: search, preference tradeoffs, and interactive visualization. The first MCDM component is the search process over the space of possible solutions to identify the non-dominated solutions that compose the Pareto set. The second component is the preference tradeoff process to select a single solution (or a small subset of solutions) from the Pareto set. The third component is the interactive visualization process to embed the decisionmaker in the solution refinement and selection loop. We focus on the intersection of these three components and we highlight some research challenges, representing gaps in the intersection. We introduce a requirement framework to compare most MCDM problems, their solutions, and analyze their performances. We focus on two research challenges and illustrate them with three case studies in electric power management, financial portfolio rebalancing, and air traffic planning.

[1]  Piero P. Bonissone.  Multi-Criteria Decision-Making: The Intersection of Search, Preference Tradeoff, and Interaction Visualization Processes , 2007, 2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making.

[2]  David E. Goldberg,et al.  Decomposable Problems, Niching, and Scalability of Multiobjective Estimation of Distribution Algorithms , 2005, ArXiv.

[3]  Jeffrey Horn,et al.  Multicriterion decision making , 1997 .

[4]  Piero P. Bonissone,et al.  Locally Weighted Fusion of Multiple Predictive Models , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[5]  Abdiel Alexander Santos Galindo,et al.  Next Generation Air Transportation System (NextGen) , 2011 .

[6]  Peter J. Fleming,et al.  An Adaptive Divide-and-ConquerMethodology forEvolutionary Multi-criterion Optimisation , 2003, EMO.

[7]  N. S. Iyer,et al.  A family of dominance rules for multiattribute decision making under uncertainty , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[8]  Andrew R. Barron,et al.  Approximation and estimation bounds for artificial neural networks , 2004, Machine Learning.

[9]  Drake Circus,et al.  Preferences and Their Application in Evolutionary Multiobjective Optimization , 2002 .

[10]  N. Iyer,et al.  MONACO - Multi-Objective National Airspace Collaborative Optimization , 2007, 2007 IEEE Aerospace Conference.

[11]  J. Hull.  Options, futures & other derivatives , 2003 .

[12]  Jianjun Hu,et al.  HEMO: A Sustainable Multi-objective Evolutionary Optimization Framework , 2003, GECCO.

[13]  Lily Rachmawati,et al.  Preference Incorporation in Multi-objective Evolutionary Algorithms: A Survey , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[14]  Wolfgang Slany.  Scheduling as a fuzzy multiple criteria optimization problem , 1996, Fuzzy Sets Syst..

[15]  Jorge Casillas,et al.  Modeling Vague Data with Genetic Fuzzy Systems under a Combination of Crisp and Imprecise Criteria , 2007, 2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making.

[16]  Jan Kautz,et al.  Conclusions/summary , 2006, SIGGRAPH Courses.

[17]  Ludmila I. Kuncheva,et al.  Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.

[18]  S. Bonissone,et al.  Evolutionary Multiobjective Optimization on a Chip , 2007, 2007 IEEE Workshop on Evolvable and Adaptive Hardware (WEAH2007).

[19]  FongGifford,et al.  A Multidimensional Framework for Risk Analysis , 2006 .

[20]  Peter J. Fleming,et al.  Many-Objective Optimization: An Engineering Design Perspective , 2005, EMO.

[21]  Jürgen Branke,et al.  Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation , 2006, IEEE Transactions on Evolutionary Computation.

[22]  Lionel Tarassenko,et al.  Estimations of error bounds for RBF networks , 1997 .

[23]  Feng Xue,et al.  Management of Complex Dynamic Systems based on Model-Predictive Multi-objective Optimization , 2006, 2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications.

[24]  Stefano R. Bonissone,et al.  Evolutionary algorithms for multi-objective optimization: fuzzy preference aggregation and multisexual EAs , 2001, SPIE Optics + Photonics.

[25]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[26]  Raj Subbu,et al.  NextGen Collaborative Air Traffic Management Solutions , 2008 .

[27]  Piero P. Bonissone,et al.  A Linguistic Approach to Decisionmaking with Fuzzy Sets , 1980, IEEE Transactions on Systems, Man, and Cybernetics.

[28]  Oldrich A Vasicek,et al.  A Multidimensional Framework for Risk Analysis , 1997 .

[29]  Raj Subbu,et al.  User-Oriented Traffic Flow Planning for the Next Generation Air Transport System , 2007 .

[30]  Piero P. Bonissone,et al.  A Review of Two Industrial Deployments of Multi-criteria Decision-making Systems at General Electric , 2007, 2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making.

[31]  Piero P. Bonissone,et al.  Six Sigma Applied Throughout the Lifecycle of an Automated Decision System , 2005 .

[32]  Arthur C. Sanderson,et al.  JADE: Self-adaptive differential evolution with fast and reliable convergence performance , 2007, 2007 IEEE Congress on Evolutionary Computation.

[33]  David W. Corne,et al.  Techniques for highly multiobjective optimisation: some nondominated points are better than others , 2007, GECCO '07.

[34]  Rajeev Kumar,et al.  Multiobjective genetic algorithm partitioning for hierarchical learning of high-dimensional pattern spaces: a learning-follows-decomposition strategy , 1998, IEEE Trans. Neural Networks.

[35]  Piero P. Bonissone,et al.  Evolutionary algorithms + domain knowledge = real-world evolutionary computation , 2006, IEEE Transactions on Evolutionary Computation.

[36]  J. Hull.  Options, Futures, and Other Derivatives , 1989 .

[37]  Piero P. Bonissone,et al.  Fast meta-models for local fusion of multiple predictive models , 2011, Appl. Soft Comput..

[38]  Piero P. Bonissone,et al.  Multiobjective financial portfolio design: a hybrid evolutionary approach , 2005, 2005 IEEE Congress on Evolutionary Computation.