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Behnam Malakooti, Ph.D., P.E.
 

Clustering and Grouping: Multiple Criteria Decision Making

Behnam Malakooti

Brief Table Of Contents Detailed Table Of Contents

  • Part I. Clustering and Group Selection in Multiple Criteria Decision Making

    • Chapter 1 Clustering and Group Selection by Multi-Objective Optimization
      • Clustering and Group Selection of Multiple Criteria Alternatives with Application to space Networks (1)
      • 1. Abstract
      • 2. Introduction
      • 3. Definitions and Notation for Discrete MCDM Problems
      • 4. Definitions and Notation for Clustering Problems
      • 5. Theories and Procedures for Clustering of Multiple Criteria Alternatives
      • 6. The Procedure of Clustering Alternatives
      • 7. Selection of the Most Satisfactory Alternative for Each Group
      • 8. Conclusions
      • 9. References
      • 10. Appendix 1 (Proofs of Remarks and Propositions)
      • 11. Appendix 2 (An Example and Explanation of the Variable v in the Linear Program)
      • 12. Appendix 3 (Details for the Example of Section 5.1)
    • Chapter 2 Clustering and Group Selection in Multiple Criteria Decision Making by Supervised and Unsupervised Artificial Neural Networks
      • Clustering and Selection of Multiple Criteria Alternatives using Unsupervised and Supervised Neural Networks (7)
      • 1. Introduction
      • 2. A Naïve Approach for Multi-Criteria Clustering
        • 2.1. The Naïve Multi-Criteria Clustering Approach Using Rank Order Clustering
      • 3. Review of Neural Networks
      • 4. Our Approach for Multi-Criteria Clustering using Unsupervised Learning Algorithm
        • 4.1. Problem Formulation
        • 4.2. Momentum Term
        • 4.3. Summary of the Developed Algorithm
        • 4.4. Clustering Application Example
      • 5. Some Experimental Analysis with Multi-Criteria Clustering Algorithm; Effect of Inaccuracy, Sensitivity Analysis, and Effect of Odd Alternatives
        • 5.1. Some Computational Analysis
        • 5.2. Effect of Inaccuracy
        • 5.3. Sensitivity Analysis - Effect of Adding Alternatives
        • 5.4. Odd Families
      • 6. Selection of Multiple Criteria Decision Making Alternatives using Feedforward Artificial Neural Networks
        • 6.1. Basics of Feedforward ANN
        • 6.2. Cluster Characteristics
      • 7. References
    • Chapter 3 Ranking Groups of Multiple Criteria Alternatives by Strengths of Preferences
      • Measurable Value Functions for Ranking and Selection of Group of Alternatives (26)
      • 1. Introduction
      • 2. Existence of Measurable Value Functions
        • 2.1. Notations and Definitions
        • 2.2. Axioms and a Theorem
        • 2.3. Binary as an Extension of Trinary
        • 2.4. Quaternary as an Extension of Trinary
        • 2.5. Higher-Order Relationships as Extensions of Trinary-The Effect of Substitutability
        • 2.6. Discussion and Summary of Experiments for Assessing Measurable Value Functions
      • 3. Conclusions
      • 4. References
    • Chapter 4 Efficient Ranking Algorithms for Ordinal, Cardinal, and Strengths of Preferences
      • Ranking and Screening Multiple Criteria Alternatives with Partial Information and use of Ordinal and Cardinal Strength of Preferences (6)
      • 1. Abstract
      • 2. Introduction
      • 3. Some Theory and Ranking Algorithm for Screening and Ranking Alternatives with Partial Information
      • 4. Ranking Algorithm for Additive Multi-Attribute Utility Function
      • 5. Ordinal and Cardinal Strength of Preference, Generation of Partial Information, and Computational Experiments
      • 6. Conclusions
      • 7. References

  • Part II. Family Formation for Multiple Criteria Group Technology

    • Chapter 5 Generation of Efficient Alternatives for Multiple Criteria Group Technology Problems
      • Multiple Criteria Approach and Generation of Efficient Alternatives for Machine-Part Family Formation in Group Technology (4)
      • 1. Introduction
      • 2. The Multiple Criteria Approach for Cell Formation
        • 2.1. Model Formulation
        • 2.2. A Three-level Approach to Find the Best Alternative
      • 3. An Example to Explain the Three-level MCDM Approach and the Relationships Among Criteria
        • 3.1. An Example
        • 3.2. Some Observations About the Relationships Among Criteria
      • 4. Experimental Results
      • 5. Conclusions
      • 6. References
    • Chapter 6 Clustering Group Technology Multiple Criteria Alternatives Using Unsupervised Artificial Neural Networks
      • A Variable-Parameter Unsupervised Learning Clustering Neural for Machine Part Group Formation (14)
      • 1. Introduction
      • 2. Basic Notations, Definitions, and Review of Methods for Machine-Part Group Formation
      • 3. Self-Organizing Neural Network
      • 4. A Variable-Parameter Unsupervised Learning Algorithm
      • 5. Some Experimental Results
      • 6. Conclusion
      • 7. References

  • Part III. Design of Cells and Single-Layer Networks

    • Chapter 7 Uni-Directional and Bi-Directional Single-Layer Network Cells: A Quick Heuristic Approach
      • Unidirectional and Bi-directional single Row Layouts by Largest Candidate Heuristics with application to Design of Tele-communication Networks (3)
      • 1. Abstract
      • 2. Introduction
      • 3. Mathematical Model and the Largest Candidate Heuristic for Unidirectional Layout
      • 4. The Largest Candidate Heuristic for Unidirectional Layout
      • 5. Some Experiments and Computational Results
      • 6. Bi-Directional Facility Layout Problem
      • 7. Conclusions
      • 8. References
    • Chapter 8 Uni-Directional and Bi-Directional Single-Layer Network Cells: A Linear Programming Approach
      • Unidirectional Loop Network Layout by a Linear Programming Heuristic and Design of Tele-communications Networks (2)
      • 1. Abstract
      • 2. Introduction
      • 3. Mathematical Model and the Heuristic
      • 4. Two Other Extensions of the Heuristic (#2 and #3)
      • 5. Experiments and Computational Results with Three Heuristics
      • 6. Conclusions
      • 7. References