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An exact method for computing the nadir values in multiple objective linear programming

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  • Alves, Maria João
  • Costa, João Paulo

Abstract

In this paper we propose a new method to determine the exact nadir (minimum) criterion values over the efficient set in multiple objective linear programming (MOLP). The basic idea of the method is to determine, for each criterion, the region of the weight space associated with the efficient solutions that have a value in that criterion below the minimum already known (by default, the minimum in the payoff table). If this region is empty, the nadir value has been found. Otherwise, a new efficient solution is computed using a weight vector picked from the delimited region and a new iteration is performed. The method is able to find the nadir values in MOLP problems with any number of objective functions, although the computational effort increases significantly with the number of objectives. Computational experiments are described and discussed, comparing two slightly different versions of the method.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ejores:v:198:y:2009:i:2:p:637-646
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    References listed on IDEAS

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    Cited by:

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    2. 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.
    3. 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.
    4. Kahina Ghazli & Nicolas Gillis & Mustapha Moulaï, 2020. "Optimizing over the properly efficient set of convex multi-objective optimization problems," Annals of Operations Research, Springer, vol. 295(2), pages 575-604, December.
    5. Luque, Mariano & Ruiz, Francisco & Steuer, Ralph E., 2010. "Modified interactive Chebyshev algorithm (MICA) for convex multiobjective programming," European Journal of Operational Research, Elsevier, vol. 204(3), pages 557-564, August.
    6. Jornada, Daniel & Leon, V. Jorge, 2016. "Biobjective robust optimization over the efficient set for Pareto set reduction," European Journal of Operational Research, Elsevier, vol. 252(2), pages 573-586.
    7. 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.
    8. Özgür Özpeynirci, 2017. "On nadir points of multiobjective integer programming problems," Journal of Global Optimization, Springer, vol. 69(3), pages 699-712, November.
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    10. Ricardo Landa & Giomara Lárraga & Gregorio Toscano, 2019. "Use of a goal-constraint-based approach for finding the region of interest in multi-objective problems," Journal of Heuristics, Springer, vol. 25(1), pages 107-139, February.
    11. Weihua Zhang & Marc Reimann, 2014. "Towards a multi-objective performance assessment and optimization model of a two-echelon supply chain using SCOR metrics," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(4), pages 591-622, December.
    12. Gokhan Kirlik & Serpil Sayın, 2015. "Computing the nadir point for multiobjective discrete optimization problems," Journal of Global Optimization, Springer, vol. 62(1), pages 79-99, May.
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