Skip to main content
Log in

Minimax regret solution to multiobjective linear programming problems with interval objective functions coefficients

  • Original Paper
  • Published:
Central European Journal of Operations Research Aims and scope Submit manuscript

Abstract

The current paper focuses on a multiobjective linear programming problem with interval objective functions coefficients. Taking into account the minimax regret criterion, an attempt is being made to propose a new solution i.e. minimax regret solution. With respect to its properties, a minimax regret solution is necessarily ideal when a necessarily ideal solution exists; otherwise it is still considered a possibly weak efficient solution. In order to obtain a minimax regret solution, an algorithm based on a relaxation procedure is suggested. A numerical example demonstrates the validity and strengths of the proposed algorithm. Finally, two special cases are investigated: the minimax regret solution for fixed objective functions coefficients as well as the minimax regret solution with a reference point. Some of the characteristic features of both cases are highlighted thereafter.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Bazaraa MS, Sherali HD, Shetty CM (2006) Nonlinear programming: theory and algorithms. 3rd Edn. Wiley, New Jersey

    Book  Google Scholar 

  • Bazaraa MS, Jarvis JJ (2010) Linear programming and network flows. 4th Edn. Wiley, New Jersey

    Google Scholar 

  • Birge JR, Louveaux F (1993) Introduction to stochastic programming. Physica-Verlag, New York

    Google Scholar 

  • Bitran GR (1980) Linear multiobjective problems with interval coefficients. Manag Sci 26: 694–706

    Article  Google Scholar 

  • Das SK, Goswami A, Alam SS (1999) Multiobjective transportation problem with interval cost, source and destination parameters. Eur J Oper Res 117: 100–112

    Article  Google Scholar 

  • Dong C, Huang GH, Cai YP, Xu Y (2011) An interval-parameter minimax regret programming approach for power management systems planning under uncertainty. Appl Energy 88: 2835–2845

    Article  Google Scholar 

  • Ehrgott M (2005) Multicriteria optimization. 2nd Edn. Springer, Berlin

    Google Scholar 

  • Feidler M, Nedoma J, Ramik J, Rohn J, Zimmermann K (2006) Linear optimization problems with inexact data. Springer, Berlin

    Google Scholar 

  • Giove S, Funari S, Nardelli C (2006) An interval portfolio selection problem based on regret function. Eur J Oper Res 170: 253–264

    Article  Google Scholar 

  • Hladík M (2008) Tolerances in portfolio selection via interval linear programming. In: Proceedings of the 26th international conference on mathematical methods in economics, MME08, pp 185–191

  • Hladík M (2010) On necessarily efficient solutions in interval multiobjective linear programming, In: Proceedings of the 25th Mini-EURO conference on uncertainty and robustness in planning and decision making, URPDM 2010, pp 1–10

  • Ida M., Generation of efficient solutions for multiobjective linear programming with interval coefficients. In: Proceedings of the SICE annual conference, SICE’96, pp 1041–1044

  • Ida M (1999) Necessary efficient test in interval multiobjective linear programming. In: Proceedings of the eighth international fuzzy systems association world congress, pp 500–504

  • Ida M (2003) Portfolio selection problem with interval coefficients. Appl Math Lett 16: 709–713

    Article  Google Scholar 

  • Ida M (2004) Solutions for the portfolio selection problem with interval and fuzzy coefficients. Reliab Comput 10: 389–400

    Article  Google Scholar 

  • Inuiguchi M, Kume Y (1991) Goal programming problems with interval coefficients and target intervals. Eur J Oper Res 52: 345–360

    Article  Google Scholar 

  • Inuiguchi M, Sakawa M (1995) Minimax regret solution to linear programming problems with an interval objective function. Eur J Oper Res 86: 526–536

    Article  Google Scholar 

  • Inuiguchi M, Sakawa M (1996) Possible and necessary efficiency in possibilistic multiobjective linear programming problems and possible efficiency test. Fuzzy Sets Syst 78: 321–341

    Article  Google Scholar 

  • Inuiguchi M, Higashitani H, Tanino T (1999) On computation methods of the minimax regret solution for linear programming problems with uncertain objective function coefficients. In: Proceedings of IEEE international conference, pp 979–984

  • Lai KK, Wang SY, Xu JP, Zho SS, Fang Y (2002) A class of linear interval programming problems and its application to portfolio selection. IEEE Trans Fuzzy Syst 10: 698–704

    Article  Google Scholar 

  • Lai Y-J, Hwang C-L (1996) Fuzzy multiple objective decision making: methods and applications, Lecture Notes in Economics and Mathematical Systems, vol 404. Springer, Berlin

  • Loulou R, Kanudia A (1999) Minimax regret strategies for greenhouse gas abatement: methodology and application. Oper Res Lett 25: 219–230

    Article  Google Scholar 

  • Mausser HE, Laguna M (1998) A new mixed integer formulation for the maximum regret problem. Int Trans Oper Res 5: 389–403

    Article  Google Scholar 

  • Oliveira C, Antunes CH (2007) Multiple objective linear programming models with interval coefficients—an illustrative overview. Eur J Oper Res 181: 1434–1463

    Article  Google Scholar 

  • Oliveira C, Antunes CH (2009) An interactive method of tackling uncertainty in interval multiple objective linear programming. J Math Sci 161: 854–866

    Article  Google Scholar 

  • Prókopa A (1995) Stochastic programming. Kluwer, Boston

    Book  Google Scholar 

  • Sakawa M (1993) Fuzzy sets and interactive multiobjective optimization. Plenum Press, New York

    Book  Google Scholar 

  • Savage LJ (1951) The theory of statistical decision. J Am Stat Assoc 46: 55–67

    Article  Google Scholar 

  • Shimizu K, Aiyoshi E (1980) Necessary conditions for min-max problems and algorithms by a relaxation procedure. IEEE Trans Automat Control AC-25: 62–66

    Article  Google Scholar 

  • Urli B, Nadeau R (1992) An interactive method to multiobjective linear programming problems with interval coefficients. INFOR 30: 127–137

    Google Scholar 

  • Wang ML, Wang HF (2001) Interval analysis of a fuzzy multiobjective linear programming. Int J Fuzzy Syst 34: 558–568

    Google Scholar 

  • Wierzbicki AP (1982) A mathematical basis for satisficing decision making. Math Model 3: 391–405

    Article  Google Scholar 

  • Wu HC (2009) The Karush-Kuhn-Tucker optimality conditions in multiobjective programming problems with interval-valued objective functions. Eur J Oper Res 196: 49–60

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. A. Yaghoobi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rivaz, S., Yaghoobi, M.A. Minimax regret solution to multiobjective linear programming problems with interval objective functions coefficients. Cent Eur J Oper Res 21, 625–649 (2013). https://doi.org/10.1007/s10100-012-0252-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10100-012-0252-9

Keywords

Navigation