IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v51y2003i3p427-436.html
   My bibliography  Save this article

A Procedure to Find Discrete Representations of the Efficient Set with Specified Coverage Errors

Author

Listed:
  • Serpil Sayin

    (Koç University, College of Administrative Sciences and Economics, Rumeli Feneri Yolu, Sariyer, 80910 İstanbul, Turkey)

Abstract

An important issue in multiple objective mathematical programming is finding discrete representations of the efficient set. Because discrete points can be directly studied by a decision maker, a discrete representation can serve as the solution to the multiple objective problem at hand. However, the discrete representation must be of acceptable quality to ensure that a most--preferred solution identified by a decision maker is of acceptable quality. Recently, attributes for measuring the quality of discrete representations have been proposed. Although discrete representations can be obtained in many different ways, and their quality evaluated afterwards, the ultimate goal should be to find such representations so as to conform to specified quality standards. We present a method that can find discrete representations of the efficient set according to a specified level of quality. The procedure is based on mathematical programming tools and can be implemented relatively easily when the domain of interest is a polyhedron. The nonconvexity of the efficient set is dealt with through a coordinated decomposition approach. We conduct computational experiments and report results.

Suggested Citation

  • Serpil Sayin, 2003. "A Procedure to Find Discrete Representations of the Efficient Set with Specified Coverage Errors," Operations Research, INFORMS, vol. 51(3), pages 427-436, June.
    • Handle: RePEc:inm:oropre:v:51:y:2003:i:3:p:427-436
    DOI: 10.1287/opre.51.3.427.14951
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.51.3.427.14951
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.51.3.427.14951?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Dessouky, M. I. & Ghiassi, M. & Davis, W. J., 1986. "Estimates of the minimum nondominated criterion values in multiple-criteria decision-making," Engineering Costs and Production Economics, Elsevier, vol. 10(2), pages 95-104, June.
    2. Robert L. Smith, 1984. "Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed over Bounded Regions," Operations Research, INFORMS, vol. 32(6), pages 1296-1308, December.
    3. C. G. E. Boender & R. J. Caron & J. F. McDonald & A. H. G. Rinnooy Kan & H. E. Romeijn & R. L. Smith & J. Telgen & A. C. F. Vorst, 1991. "Shake-and-Bake Algorithms for Generating Uniform Points on the Boundary of Bounded Polyhedra," Operations Research, INFORMS, vol. 39(6), pages 945-954, December.
    4. Harold P. Benson & Serpil Sayin, 1993. "A face search heuristic algorithm for optimizing over the efficient set," Naval Research Logistics (NRL), John Wiley & Sons, vol. 40(1), pages 103-116, February.
    5. Ralph E. Steuer, 1976. "Multiple Objective Linear Programming with Interval Criterion Weights," Management Science, INFORMS, vol. 23(3), pages 305-316, November.
    6. 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.
    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. Kellner, Florian & Lienland, Bernhard & Utz, Sebastian, 2019. "An a posteriori decision support methodology for solving the multi-criteria supplier selection problem," European Journal of Operational Research, Elsevier, vol. 272(2), pages 505-522.
    2. Jyrki Wallenius & James S. Dyer & Peter C. Fishburn & Ralph E. Steuer & Stanley Zionts & Kalyanmoy Deb, 2008. "Multiple Criteria Decision Making, Multiattribute Utility Theory: Recent Accomplishments and What Lies Ahead," Management Science, INFORMS, vol. 54(7), pages 1336-1349, July.
    3. Nathan Adelgren & Akshay Gupte, 2022. "Branch-and-Bound for Biobjective Mixed-Integer Linear Programming," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 909-933, March.
    4. 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.
    5. Markus Hirschberger & Ralph E. Steuer & Sebastian Utz & Maximilian Wimmer & Yue Qi, 2013. "Computing the Nondominated Surface in Tri-Criterion Portfolio Selection," Operations Research, INFORMS, vol. 61(1), pages 169-183, February.
    6. Argyris, Nikolaos & Karsu, Özlem & Yavuz, Mirel, 2022. "Fair resource allocation: Using welfare-based dominance constraints," European Journal of Operational Research, Elsevier, vol. 297(2), pages 560-578.
    7. D Nadirler & E Karasakal, 2008. "Mixed integer programming-based solution procedure for single-facility location with maximin of rectilinear distance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(4), pages 563-570, April.
    8. Aytug, Haldun & Sayın, Serpil, 2012. "Exploring the trade-off between generalization and empirical errors in a one-norm SVM," European Journal of Operational Research, Elsevier, vol. 218(3), pages 667-675.
    9. 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.
    10. 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.
    11. Kuhn, K. & Raith, A. & Schmidt, M. & Schöbel, A., 2016. "Bi-objective robust optimisation," European Journal of Operational Research, Elsevier, vol. 252(2), pages 418-431.
    12. Lou, Youcheng & Wang, Shouyang, 2016. "Approximate representation of the Pareto frontier in multiparty negotiations: Decentralized methods and privacy preservation," European Journal of Operational Research, Elsevier, vol. 254(3), pages 968-976.
    13. Stacey Faulkenberg & Margaret Wiecek, 2012. "Generating equidistant representations in biobjective programming," Computational Optimization and Applications, Springer, vol. 51(3), pages 1173-1210, April.
    14. Daniel Jornada & V. Jorge Leon, 2020. "Filtering Algorithms for Biobjective Mixed Binary Linear Optimization Problems with a Multiple-Choice Constraint," INFORMS Journal on Computing, INFORMS, vol. 32(1), pages 57-73, January.

    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. Gryazina, Elena & Polyak, Boris, 2014. "Random sampling: Billiard Walk algorithm," European Journal of Operational Research, Elsevier, vol. 238(2), pages 497-504.
    2. 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.
    3. Serpil Sayin, 2000. "Optimizing Over the Efficient Set Using a Top-Down Search of Faces," Operations Research, INFORMS, vol. 48(1), pages 65-72, February.
    4. A. B. Dieker & Santosh S. Vempala, 2015. "Stochastic Billiards for Sampling from the Boundary of a Convex Set," Mathematics of Operations Research, INFORMS, vol. 40(4), pages 888-901, October.
    5. Tu, Ta Van, 2000. "Optimization over the efficient set of a parametric multiple objective linear programming problem," European Journal of Operational Research, Elsevier, vol. 122(3), pages 570-583, May.
    6. Luca Anzilli & Silvio Giove, 2020. "Multi-criteria and medical diagnosis for application to health insurance systems: a general approach through non-additive measures," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 43(2), pages 559-582, December.
    7. Corrente, Salvatore & Figueira, José Rui & Greco, Salvatore, 2014. "The SMAA-PROMETHEE method," European Journal of Operational Research, Elsevier, vol. 239(2), pages 514-522.
    8. Stephen Baumert & Archis Ghate & Seksan Kiatsupaibul & Yanfang Shen & Robert L. Smith & Zelda B. Zabinsky, 2009. "Discrete Hit-and-Run for Sampling Points from Arbitrary Distributions Over Subsets of Integer Hyperrectangles," Operations Research, INFORMS, vol. 57(3), pages 727-739, June.
    9. Alves, Maria João & Costa, João Paulo, 2009. "An exact method for computing the nadir values in multiple objective linear programming," European Journal of Operational Research, Elsevier, vol. 198(2), pages 637-646, October.
    10. Qi Fan & Jiaqiao Hu, 2018. "Surrogate-Based Promising Area Search for Lipschitz Continuous Simulation Optimization," INFORMS Journal on Computing, INFORMS, vol. 30(4), pages 677-693, November.
    11. Reuven Rubinstein, 2009. "The Gibbs Cloner for Combinatorial Optimization, Counting and Sampling," Methodology and Computing in Applied Probability, Springer, vol. 11(4), pages 491-549, December.
    12. Luis V. Montiel & J. Eric Bickel, 2014. "A Generalized Sampling Approach for Multilinear Utility Functions Given Partial Preference Information," Decision Analysis, INFORMS, vol. 11(3), pages 147-170, September.
    13. Pavel Shcherbakov & Mingyue Ding & Ming Yuchi, 2021. "Random Sampling Many-Dimensional Sets Arising in Control," Mathematics, MDPI, vol. 9(5), pages 1-16, March.
    14. Cheng, Haiyan & Sandu, Adrian, 2009. "Efficient uncertainty quantification with the polynomial chaos method for stiff systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(11), pages 3278-3295.
    15. Sorawit Saengkyongam & Anthony Hayter & Seksan Kiatsupaibul & Wei Liu, 2020. "Efficient computation of the stochastic behavior of partial sum processes," Computational Statistics, Springer, vol. 35(1), pages 343-358, March.
    16. van Valkenhoef, Gert & Tervonen, Tommi, 2016. "Entropy-optimal weight constraint elicitation with additive multi-attribute utility models," Omega, Elsevier, vol. 64(C), pages 1-12.
    17. Ru, Zice & Liu, Jiapeng & Kadziński, Miłosz & Liao, Xiuwu, 2023. "Probabilistic ordinal regression methods for multiple criteria sorting admitting certain and uncertain preferences," European Journal of Operational Research, Elsevier, vol. 311(2), pages 596-616.
    18. Jan Heufer, 2014. "Generating Random Optimising Choices," Computational Economics, Springer;Society for Computational Economics, vol. 44(3), pages 295-305, October.
    19. Cyril Bachelard & Apostolos Chalkis & Vissarion Fisikopoulos & Elias Tsigaridas, 2022. "Randomized geometric tools for anomaly detection in stock markets," Papers 2205.03852, arXiv.org, revised May 2022.
    20. Postek, Krzysztof & Ben-Tal, A. & den Hertog, Dick & Melenberg, Bertrand, 2015. "Exact Robust Counterparts of Ambiguous Stochastic Constraints Under Mean and Dispersion Information," Other publications TiSEM d718e419-a375-4707-b206-e, Tilburg University, School of Economics and Management.

    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:inm:oropre:v:51:y:2003:i:3:p:427-436. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.