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Hybrid Approach for Solving Multiple-Objective Linear Programs in Outcome Space

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  • H. P. Benson

    (Warrington College of Business Administration, University of Florida)

Abstract

Various difficulties arise in using decision set-based vector maximization methods to solve a multiple-objective linear programming problem (MOLP). Motivated by these difficulties, some researchers in recent years have begun to develop tools for analyzing and solving problem (MOLP) in outcome space, rather than in decision space. In this article, we present and validate a new hybrid vector maximization approach for solving problem (MOLP) in outcome space. The approach systematically integrates a simplicial partitioning technique into an outer approximation procedure to yield an algorithm that generates the set of all efficient extreme points in the outcome set of problem (MOLP) in a finite number of iterations. Some key potential practical and computational advantages of the approach are indicated.

Suggested Citation

  • H. P. Benson, 1998. "Hybrid Approach for Solving Multiple-Objective Linear Programs in Outcome Space," Journal of Optimization Theory and Applications, Springer, vol. 98(1), pages 17-35, July.
    • Handle: RePEc:spr:joptap:v:98:y:1998:i:1:d:10.1023_a:1022628612489
    DOI: 10.1023/A:1022628612489
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    References listed on IDEAS

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    1. Gal, Tomas, 1977. "A general method for determining the set of all efficient solutions to a linear vectormaximum problem," European Journal of Operational Research, Elsevier, vol. 1(5), pages 307-322, September.
    2. Dauer, Jerald P. & Liu, Yi-Hsin, 1990. "Solving multiple objective linear programs in objective space," European Journal of Operational Research, Elsevier, vol. 46(3), pages 350-357, June.
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    5. Gal, Tomas, 1986. "On efficient sets in vector maximum problems -- A brief survey," European Journal of Operational Research, Elsevier, vol. 24(2), pages 253-264, February.
    6. Harold P. Benson, 1985. "Multiple Objective Linear Programming with Parametric Criteria Coefficients," Management Science, INFORMS, vol. 31(4), pages 461-474, April.
    7. Dauer, Jerald P. & Gallagher, Richard J., 1996. "A combined constraint-space, objective-space approach for determining high-dimensional maximal efficient faces of multiple objective linear programs," European Journal of Operational Research, Elsevier, vol. 88(2), pages 368-381, January.
    8. Ralph E. Steuer, 1976. "Multiple Objective Linear Programming with Interval Criterion Weights," Management Science, INFORMS, vol. 23(3), pages 305-316, November.
    9. Gallagher, Richard J. & Saleh, Ossama A., 1995. "A representation of an efficiency equivalent polyhedron for the objective set of a multiple objective linear program," European Journal of Operational Research, Elsevier, vol. 80(1), pages 204-212, January.
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    Cited by:

    1. Esra Karasakal & Murat Köksalan, 2009. "Generating a Representative Subset of the Nondominated Frontier in Multiple Criteria Decision Making," Operations Research, INFORMS, vol. 57(1), pages 187-199, February.
    2. Henri Bonnel & Julien Collonge, 2015. "Optimization over the Pareto outcome set associated with a convex bi-objective optimization problem: theoretical results, deterministic algorithm and application to the stochastic case," Journal of Global Optimization, Springer, vol. 62(3), pages 481-505, July.
    3. Koenen, Melissa & Balvert, Marleen & Fleuren, H.A., 2023. "A Renewed Take on Weighted Sum in Sandwich Algorithms : Modification of the Criterion Space," Other publications TiSEM 795b6c0c-c7bc-4ced-9d6b-a, Tilburg University, School of Economics and Management.
    4. Benson, Harold P. & Sun, Erjiang, 2002. "A weight set decomposition algorithm for finding all efficient extreme points in the outcome set of a multiple objective linear program," European Journal of Operational Research, Elsevier, vol. 139(1), pages 26-41, May.
    5. Matthias Ehrgott & Lizhen Shao & Anita Schöbel, 2011. "An approximation algorithm for convex multi-objective programming problems," Journal of Global Optimization, Springer, vol. 50(3), pages 397-416, July.
    6. Koenen, Melissa & Balvert, Marleen & Fleuren, H.A., 2023. "A Renewed Take on Weighted Sum in Sandwich Algorithms : Modification of the Criterion Space," Discussion Paper 2023-012, Tilburg University, Center for Economic Research.
    7. H. P. Benson & E. Sun, 2000. "Outcome Space Partition of the Weight Set in Multiobjective Linear Programming," Journal of Optimization Theory and Applications, Springer, vol. 105(1), pages 17-36, April.
    8. Lizhen Shao & Matthias Ehrgott, 2008. "Approximately solving multiobjective linear programmes in objective space and an application in radiotherapy treatment planning," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 68(2), pages 257-276, October.
    9. J. Fülöp & L. D. Muu, 2000. "Branch-and-Bound Variant of an Outcome-Based Algorithm for Optimizing over the Efficient Set of a Bicriteria Linear Programming Problem," Journal of Optimization Theory and Applications, Springer, vol. 105(1), pages 37-54, April.
    10. Henri Bonnel & Julien Collonge, 2014. "Stochastic Optimization over a Pareto Set Associated with a Stochastic Multi-Objective Optimization Problem," Journal of Optimization Theory and Applications, Springer, vol. 162(2), pages 405-427, August.

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