COMPUTATIONAL INTELLIGENCE IN COMPLEX DECISION MAKING SYSTEMS

1. Front Matter

   Pages i-xiv

   PDF

2. Computational Intelligence: Past, Today, and Future

   • Cengiz Kahraman, İhsan Kaya, Didem Çinar

   Pages 1-46

3. Uncertainty in Dynamically Changing Input Data

   • Tiago C. Pais, Rita A. Ribeiro, Luís F. Simões

   Pages 47-66

4. Decision Making under Uncertainty by Possibilistic Linear Programming Problems

   • Peijun Guo

   Pages 67-83

5. Intelligent DecisionMaking in Training Based on Virtual Reality

   • Liliane dos Santos Machado, Ronei Marcos de Moraes

   Pages 85-123

6. A Many-Valued Temporal Logic and Reasoning Framework for Decision Making

   • Zhirui Lu, Jun Liu, Juan C. Augusto, Hui Wang

   Pages 125-146

7. A Statistical Approach to Complex Multi-Criteria Decisions

   • Pierre L. Kunsch

   Pages 147-182

8. A Web Based Assessment Tool via the Evidential Reasoning Approach

   • Dong-Ling Xu

   Pages 183-204

9. An Intelligent Policy Simulator for Supporting Strategic Nuclear Policy Decision Making

   • Suman Rao

   Pages 205-232

10. Computing withWords for Hierarchical and Distributed Decision-Making

    • Jerry M. Mendel, Dongrui Wu

    Pages 233-271

11. Realizing Policies by Projects Using Fuzzy Multiple Criteria Decision Making

    • Cengiz Kahraman, İhsan Kaya

    Pages 273-300

12. Evolutionary ComputationMethods for Fuzzy Decision Making on Load Dispatch Problems

    • Guoli Zhang, Guangquan Zhang, Ya Gao, Jie Lu

    Pages 301-323

13. Intelligent Decision-Making for a Smart Home Environment with Multiple Occupants

    • Andres Muñoz, Juan A. Botía, Juan Carlos Augusto

    Pages 325-371

14. Applying a Choquet Integral Based Decision Making Approach to Evaluate Agile Supply Chain Strategies

    • Gϋlçin Bϋyϋközkan

    Pages 373-386

15. Back Matter

    Pages 387-388

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In recent years, there has been a growing interest in the need for designing intelligent systems to address complex decision systems. One of the most challenging issues for the intelligent system is to effectively handle real-world uncertainties that cannot be eliminated. These uncertainties include various types of information that are incomplete, imprecise, fragmentary, not fully reliable, vague, contradictory, deficient, and overloading. The uncertainties result in a lack of the full and precise knowledge of the decision system, including the determining and selection of evaluation criteria, alternatives, weights, assignment scores, and the final integrated decision result. Computational intelligent techniques (including fuzzy logic, neural networks, and genetic algorithms etc.), which are complimentary to the existing traditional techniques, have shown great potential to solve these demanding, real-world decision problems that exist in uncertain and unpredictable environments. These technologies have formed the foundation for intelligent systems.

• Virtual Reality
• artificial intelligence
• complex decision systems
• computational intelligence
• decision problem
• evolution
• evolutionary computation
• fuzzy logic
• genetic algorithms
• intelligence
• knowledge
• neural networks
• smart home
• uncertainty

• Belgian Nuclear Research Centre (SCK • CEN), Mol & Ghent University, Gent, Belgium

  Da Ruan

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