Multiobjective Optimization: Behavioral and Computational Considerations

1. Front Matter

   Pages i-xv

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2. Introduction

   • Jeffrey L. Ringuest

   Pages 1-17

3. Linear Goal Programming

   • Jeffrey L. Ringuest

   Pages 19-37

4. Generalizing Goal Programming

   • Jeffrey L. Ringuest

   Pages 39-50

5. Compromise Programming

   • Jeffrey L. Ringuest

   Pages 51-59

6. Decision Making and the Efficient Set

   • Jeffrey L. Ringuest

   Pages 61-80

7. Interactive Methods

   • Jeffrey L. Ringuest

   Pages 81-94

8. Computational Efficiency and Problems with Special Structure

   • Jeffrey L. Ringuest

   Pages 95-109

9. Computational Efficiency and Linear Problems of General Structure

   • Jeffrey L. Ringuest

   Pages 111-143

10. Using Multiobjective Linear Programming to Reconcile Preferences Over Time

    • Jeffrey L. Ringuest

    Pages 145-152

11. Data Presentation and Multiobjecitve Optimization

    • Jeffrey L. Ringuest

    Pages 153-164

12. Back Matter

    Pages 165-169

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Throughout the development of mathematical programming researchers have paid great attention to problems that are described by a single objective that can only be achieved subject to satisfying a set of restrictions or constraints. Recently, it has been recognized that the use of a single objective limits the applicability of In reality, many multiobjective mathematical programming models. situations exist and frequently these mUltiple objectives are in direct conflict. Research on multiobjective problems can be broken down into two broad categories: multiobjective optimization and multicriterion decision theory. Multiobjective optimization models are based on techniques such as linear programming. In general, the multiobjective optimization problem can be defined as finding a feasible alternative that yields the most preferred set of values for the objective functions. This problem differs from a single objective because subjective methods are required to determine which alternative is most preferred. A body of literature parallel to that m multiobjective optimization has been developing in the area of multicriterion decision theory. These models are based on classical decision analysis, particularly utility theory. One focus of this research has been the development and testing of procedures for estimating multiattribute utility functions that are consistent with rational decision maker behavior. A utility function provides a model of a decision maker's choice among alternatives. This literature is directly xii MULTIOBJECTIVE OPTIMIZATION applicable to multiobjective optimization and provides much needed insight into the subjective character of that problem.

• decision support
• decision support system
• decision theory
• mathematical programming
• multi-objective optimization
• optimization
• programming

• Operations Research and Strategic Management, Wallace Carroll School of Management, Boston College, Chestnut Hill, USA

  Jeffrey L. Ringuest

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