IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v88y1996i2p368-381.html
   My bibliography  Save this article

A combined constraint-space, objective-space approach for determining high-dimensional maximal efficient faces of multiple objective linear programs

Author

Listed:
  • Dauer, Jerald P.
  • Gallagher, Richard J.

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:88:y:1996:i:2:p:368-381
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0377-2217(94)00199-5
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gallagher, Richard J. & Saleh, Ossama A., 1994. "Constructing the set of efficient objective values in linear multiple objective transportation problems," European Journal of Operational Research, Elsevier, vol. 73(1), pages 150-163, February.
    2. 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.
    3. Dauer, J. P. & Saleh, O. A., 1990. "Constructing the set of efficient objective values in multiple objective linear programs," European Journal of Operational Research, Elsevier, vol. 46(3), pages 358-365, June.
    4. 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.
    5. 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.
    6. White, D. J., 1991. "A characterisation of the feasible set of objective function vectors in linear multiple objective problems," European Journal of Operational Research, Elsevier, vol. 52(3), pages 361-366, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Eusébio, Augusto & Figueira, José Rui, 2009. "On the computation of all supported efficient solutions in multi-objective integer network flow problems," European Journal of Operational Research, Elsevier, vol. 199(1), pages 68-76, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Harold P. Benson & Serpil Sayin, 1997. "Towards finding global representations of the efficient set in multiple objective mathematical programming," Naval Research Logistics (NRL), John Wiley & Sons, vol. 44(1), pages 47-67, February.
    6. 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.
    7. Jacinto Martín & Concha Bielza & David Ríos Insua, 2005. "Approximating nondominated sets in continuous multiobjective optimization problems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(5), pages 469-480, August.
    8. A.P. Wierzbicki, 1998. "Reference Point Methods in Vector Optimization and Decision Support," Working Papers ir98017, International Institute for Applied Systems Analysis.
    9. Ankhili, Z. & Mansouri, A., 2009. "An exact penalty on bilevel programs with linear vector optimization lower level," European Journal of Operational Research, Elsevier, vol. 197(1), pages 36-41, August.
    10. Sylva, John & Crema, Alejandro, 2007. "A method for finding well-dispersed subsets of non-dominated vectors for multiple objective mixed integer linear programs," European Journal of Operational Research, Elsevier, vol. 180(3), pages 1011-1027, August.
    11. S. Razavyan, 2016. "A Method for Generating a Well-Distributed Pareto Set in Multiple Objective Mixed Integer Linear Programs Based on the Decision Maker’s Initial Aspiration Level," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(04), pages 1-23, August.
    12. Andreas Löhne & Benjamin Weißing, 2016. "Equivalence between polyhedral projection, multiple objective linear programming and vector linear programming," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 84(2), pages 411-426, October.
    13. Arevalo Quijada, Mª Teresa & Zapata Reina, Asunción, 1996. "La programación fraccionada múltiple: tratamiento del problema de la producción," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 6, pages 5-24, Diciembre.
    14. Löhne, Andreas & Weißing, Benjamin, 2017. "The vector linear program solver Bensolve – notes on theoretical background," European Journal of Operational Research, Elsevier, vol. 260(3), pages 807-813.
    15. Amparo María Mármol Conde & Justo Puerto Albandoz, 1996. "Incorporación de la información adicional sobre las preferencias en un problema de transporte multiobjetivo," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 5, pages 81-103, Junio.
    16. Strijbosch, L.W.G. & van Doorne, A.G.M. & Selen, W.J., 1990. "A simplified MOLP algorithm : The MOLP-S procedure," Research Memorandum FEW 460, Tilburg University, School of Economics and Management.
    17. Alves, Maria João & Henggeler Antunes, Carlos, 2022. "A new exact method for linear bilevel problems with multiple objective functions at the lower level," European Journal of Operational Research, Elsevier, vol. 303(1), pages 312-327.
    18. G. Tohidi & H. Hassasi, 2018. "Adjacency‐based local top‐down search method for finding maximal efficient faces in multiple objective linear programming," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(3), pages 203-217, April.
    19. Kalu, Timothy Ch. U., 1999. "An algorithm for systems welfare interactive goal programming modelling," European Journal of Operational Research, Elsevier, vol. 116(3), pages 508-529, August.
    20. Wierzbicki, Andrzej P. & Granat, Janusz, 1999. "Multi-objective modeling for engineering applications: DIDASN++ system," European Journal of Operational Research, Elsevier, vol. 113(2), pages 374-389, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:88:y:1996:i:2:p:368-381. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.