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Which efficient solution in multi objective programming problem should be taken?

Author

Listed:
  • Josip Matejaš

    (Faculty of Economics and Business Zagreb)

  • Tunjo Perić

    (Faculty of Economics and Business Zagreb)

  • Danijel Mlinarić

    (Faculty of Economics and Business Zagreb)

Abstract

In practical problems, which can be stated in the form of multi objective programming problem, we have usually a large set of efficient solutions. So, which solution from this set should be taken, appears as a natural question here. In the paper we propose some sustainable principles and simple numerical method for such choice. The method respects aspirations and priorities of decision makers and enables iterations for possible improvement of the solution. In this way decision makers can clearly understand why a particular solution is obtained and when and how it can be improved. The features of the method are explained by a practical example. An applications to bilevel programming problems are also presented, where the both side mapping, between the set of efficient solutions and the set of all possible priorities, is shown. It is illustrated in detail through several linear and nonlinear examples.

Suggested Citation

  • Josip Matejaš & Tunjo Perić & Danijel Mlinarić, 2021. "Which efficient solution in multi objective programming problem should be taken?," 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. 29(3), pages 967-987, September.
    • Handle: RePEc:spr:cejnor:v:29:y:2021:i:3:d:10.1007_s10100-020-00718-1
    DOI: 10.1007/s10100-020-00718-1
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    References listed on IDEAS

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    1. Tunjo Perić & Zoran Babić & Josip Matejaš, 2018. "Comparative analysis of application efficiency of two iterative multi objective linear programming methods (MP method and STEM method)," 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. 26(3), pages 565-583, September.
    2. Mishra, Savita, 2007. "Weighting method for bi-level linear fractional programming problems," European Journal of Operational Research, Elsevier, vol. 183(1), pages 296-302, November.
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    6. Ernestas Filatovas & Algirdas Lančinskas & Olga Kurasova & Julius Žilinskas, 2017. "A preference-based multi-objective evolutionary algorithm R-NSGA-II with stochastic local search," 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. 25(4), pages 859-878, December.
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    Cited by:

    1. Andrej Kastrin & Janez Povh & Lidija Zadnik Stirn & Janez Žerovnik, 2021. "Methodologies and applications for resilient global development from the aspect of SDI-SOR special issues of CJOR," 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. 29(3), pages 773-790, September.

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