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Distribution based representative sets for multi-objective integer programs

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  • Özarık, Sami Serkan
  • Lokman, Banu
  • Köksalan, Murat

Abstract

We study and exploit the characteristics of the nondominated sets of Multi-objective Integer Programs (MOIPs). We introduce a density measure and search for common properties of the distributions of nondominated points for different MOIPs. We design a procedure that categorizes the nondominated set into regions based on the densities of nondominated points. We develop an approach that generates representative sets of nondominated points using the estimated density information in different regions for general MOIPs. Experiments show that our approach is robust across different types of MOIPs.

Suggested Citation

  • Özarık, Sami Serkan & Lokman, Banu & Köksalan, Murat, 2020. "Distribution based representative sets for multi-objective integer programs," European Journal of Operational Research, Elsevier, vol. 284(2), pages 632-643.
    • Handle: RePEc:eee:ejores:v:284:y:2020:i:2:p:632-643
    DOI: 10.1016/j.ejor.2020.01.001
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    1. Murat Köksalan & Banu Lokman, 2015. "Finding nadir points in multi-objective integer programs," Journal of Global Optimization, Springer, vol. 62(1), pages 55-77, May.
    2. Boland, Natashia & Charkhgard, Hadi & Savelsbergh, Martin, 2017. "A new method for optimizing a linear function over the efficient set of a multiobjective integer program," European Journal of Operational Research, Elsevier, vol. 260(3), pages 904-919.
    3. Laumanns, Marco & Thiele, Lothar & Zitzler, Eckart, 2006. "An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method," European Journal of Operational Research, Elsevier, vol. 169(3), pages 932-942, March.
    4. Murat Köksalan & Banu Lokman, 2009. "Approximating the nondominated frontiers of multi‐objective combinatorial optimization problems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(2), pages 191-198, March.
    5. Dhaenens, C. & Lemesre, J. & Talbi, E.G., 2010. "K-PPM: A new exact method to solve multi-objective combinatorial optimization problems," European Journal of Operational Research, Elsevier, vol. 200(1), pages 45-53, January.
    6. Dächert, Kerstin & Klamroth, Kathrin & Lacour, Renaud & Vanderpooten, Daniel, 2017. "Efficient computation of the search region in multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 260(3), pages 841-855.
    7. 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.
    8. Mavrotas, George & Florios, Kostas, 2013. "An improved version of the augmented epsilon-constraint method (AUGMECON2) for finding the exact Pareto set in Multi-Objective Integer Programming problems," MPRA Paper 105034, University Library of Munich, Germany.
    9. Banu Lokman & Murat Köksalan, 2014. "Finding highly preferred points for multi-objective integer programs," IISE Transactions, Taylor & Francis Journals, vol. 46(11), pages 1181-1195, November.
    10. Michael Masin & Yossi Bukchin, 2008. "Diversity Maximization Approach for Multiobjective Optimization," Operations Research, INFORMS, vol. 56(2), pages 411-424, April.
    11. Matthias Ehrgott & Xavier Gandibleux & Anthony Przybylski, 2016. "Exact Methods for Multi-Objective Combinatorial Optimisation," International Series in Operations Research & Management Science, in: Salvatore Greco & Matthias Ehrgott & José Rui Figueira (ed.), Multiple Criteria Decision Analysis, edition 2, chapter 0, pages 817-850, Springer.
    12. Kerstin Dächert & Kathrin Klamroth, 2015. "A linear bound on the number of scalarizations needed to solve discrete tricriteria optimization problems," Journal of Global Optimization, Springer, vol. 61(4), pages 643-676, April.
    13. 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.
    14. Melih Ozlen & Benjamin A. Burton & Cameron A. G. MacRae, 2014. "Multi-Objective Integer Programming: An Improved Recursive Algorithm," Journal of Optimization Theory and Applications, Springer, vol. 160(2), pages 470-482, February.
    15. Özlen, Melih & Azizoglu, Meral, 2009. "Multi-objective integer programming: A general approach for generating all non-dominated solutions," European Journal of Operational Research, Elsevier, vol. 199(1), pages 25-35, November.
    16. Matthias Ehrgott & Xavier Gandibleux, 2004. "Approximative solution methods for multiobjective combinatorial optimization," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(1), pages 1-63, June.
    17. Kirlik, Gokhan & Sayın, Serpil, 2014. "A new algorithm for generating all nondominated solutions of multiobjective discrete optimization problems," European Journal of Operational Research, Elsevier, vol. 232(3), pages 479-488.
    18. Banu Lokman & Murat Köksalan, 2013. "Finding all nondominated points of multi-objective integer programs," Journal of Global Optimization, Springer, vol. 57(2), pages 347-365, October.
    19. Ceyhan, Gökhan & Köksalan, Murat & Lokman, Banu, 2019. "Finding a representative nondominated set for multi-objective mixed integer programs," European Journal of Operational Research, Elsevier, vol. 272(1), pages 61-77.
    20. Sylva, John & Crema, Alejandro, 2004. "A method for finding the set of non-dominated vectors for multiple objective integer linear programs," European Journal of Operational Research, Elsevier, vol. 158(1), pages 46-55, October.
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