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Abstract

We address the full fuzzy linear fractional programming problem with LR fuzzy numbers. Our goal is to revitalize a strict use of extension principle by employing it in all stages of our solution approach, thus deriving results that fully comply to it. Using the \(\alpha \)-cuts of the coefficients we present the linear optimization models that empirically derive the \(\alpha \)-cuts of the optimal objective fuzzy value, and discuss the optimization models able to derive the exact endpoints of the optimal objective values intervals. For initial maximization (minimization) problems the main issue is related to how to solve two stage min-max (max-min) problems to obtain the left (right) most endpoints. Our goals are as it follows: to obtain exact solutions to small-size problems; to obtain relevant information about solutions to large-scale problems that are in accordance to the extension principle; and to provide a procedure able to measure to which extent the solutions obtained by an approach to full fuzzy linear fractional programming comply to the extension principle. We illustrate the theoretical findings reporting numerical results, and including a relevant comparison to the results from the literature.

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References

  1. Agarwal, D., Singh, P., Li, X., et al.: Optimality criteria for fuzzy-valued fractional multi-objective optimization problem. Soft Comput. 23, 9049–9067 (2019)

    Article  MATH  Google Scholar 

  2. Anukokila, P., Radhakrishnan, B.: Goal programming approach to fully fuzzy fractional transportation problem. J. Taibah Univ. Sci. 13(1), 864–874 (2019)

    Article  Google Scholar 

  3. Arya, R., Singh, P., Kumari, S., Obaidat, M.: An approach for solving fully fuzzy multi-objective linear fractional optimization problems. Soft Comput. 24, 9105–9119 (2020)

    Article  MATH  Google Scholar 

  4. Arya, A., Yadav, S.P.: Development of intuitionistic fuzzy data envelopment analysis models and intuitionistic fuzzy input-output targets. Soft Comput. 23, 8975–8993 (2019)

    Article  MATH  Google Scholar 

  5. Bellman, R.E., Zadeh, L.A.: Decision-making in a fuzzy environment. Manag. Sci. 17(4), B-141–B-164 (1970)

    Google Scholar 

  6. Charnes, A., Cooper, W.W.: Programming with linear fractional functionals. Naval Res. Logist. Q. 9(3–4), 181–186 (1962)

    Article  MathSciNet  MATH  Google Scholar 

  7. Das, S.K., Mandal, T., Edalatpanah. S.A.: A new approach for solving fully fuzzy linear fractional programming problems using the multi-objective linear programming. RAIRO Oper. Res. 51(1), 285–297 (2017)

    Google Scholar 

  8. Das, S.K., Edalatpanah, S.A., Mandal, T.: Application of linear fractional programming problem with fuzzy nature in industry sector. Filomat 34(15), 5073–5084 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  9. Diniz, M.M., Gomes, L.T., Bassanezi, R.C.: Optimization of fuzzy-valued functions using Zadeh’s extension principle. Fuzzy Sets Syst. 404, 23–37 (2021)

    Google Scholar 

  10. Dubois, D.: The role of fuzzy sets in decision sciences: old techniques and new directions. Fuzzy Sets Syst. 184(1), 3–28 (2011)

    Google Scholar 

  11. Ebrahimnejad, A., Ghomi, S.J., Mirhosseini-Alizamini. S.M.: A revisit of numerical approach for solving linear fractional programming problem in a fuzzy environment. Appl. Math. Model. 57, 459–473 (2018)

    Google Scholar 

  12. Ebrahimnejad, A., Naser, A.: Fuzzy data envelopment analysis in the presence of undesirable outputs with ideal points. Complex Intell. Syst. 7, 379–400 (2021)

    Article  Google Scholar 

  13. Ghanbari, G., Ghorbani-Moghadam, K., De Baets, B.: Fuzzy linear programming problems: models and solutions. Soft Comput. 24, 10043–10073 (2020)

    Article  MATH  Google Scholar 

  14. Khalifa, H.A.E.W., Kumar, P.: A goal programming approach for multi-objective linear fractional programming problem with LR possibilistic variables. Int. J. Syst. Assur. Eng. Manag. 13, 2053–2061 (2022). https://doi.org/10.1007/s13198-022-01618-0

    Article  Google Scholar 

  15. Kaur, J., Kumar, A.: A novel method for solving fully fuzzy linear fractional programming problems. J. Intell. Fuzzy Syst. 33(4), 1983–1990 (2017)

    Article  MATH  Google Scholar 

  16. Liu, S.-T., Kao. C.: Solving fuzzy transportation problems based on extension principle. Eur. J. Oper. Res. 153(3), 661–674 (2004)

    Google Scholar 

  17. Loganathan, T., Ganesan. K.: Solution of fully fuzzy linear fractional programming problems - a simple approach. IOP Conf. Ser. Mater. Sci. Eng. 1377, 012040 (2021)

    Google Scholar 

  18. Pérez-Cañedo, B., Verdegay, J., Miranda Pérez, R.: An epsilon-constraint method for fully fuzzy multiobjective linear programming. Int. J. Intell. Syst. 35(4), 600–624 (2020)

    Article  Google Scholar 

  19. Pop, B., Stancu-Minasian, I.M.: A method of solving fully fuzzified linear fractional programming problems. J. Appl. Math. Comput. 27, 227–242 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  20. Schaible, S.: Fractional programming. In: Horst, R., Pardalos, P.M. (eds.) Handbook of Global Optimization. Nonconvex Optimization and its Applications, vol. 2, pp. 495–608. Kluwer Academic Publishers, Dordrecht (1995)

    Chapter  Google Scholar 

  21. Stancu-Minasian, I.M.: Fractional Programming: Theory, Methods and Applications. Kluwer Academic Publishers, Dordrecht (1997)

    Book  MATH  Google Scholar 

  22. Stancu-Minasian, I.M.: A eighth bibliography of fractional programming. Optimization 66(3), 439–470 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  23. Stancu-Minasian, I.M.: A ninth bibliography of fractional programming. Optimization 68(11), 2125–2169 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  24. Stanojević. B.: Extension principle-based solution approach to full fuzzy multi-objective linear fractional programming. Soft Comput. 26, 5275–5282 (2022)

    Google Scholar 

  25. Stanojević, B., Dzitac, I., Dzitac. S.: On the ratio of fuzzy numbers - exact membership function computation and applications to decision making. Technol. Econ. Dev. Econ. 21(5), 815–832 (2015)

    Google Scholar 

  26. Stanojević, B., Dzitac, S., Dzitac. I.: Fuzzy numbers and fractional programming in making decisions. Int. J. Inf. Technol. Decis. Mak. 19(4), 1123–1147 (2020)

    Google Scholar 

  27. Stanojević, B., Dzitac, S., Dzitac. I.: Solution approach to a special class of full fuzzy linear programming problems. Procedia Comput. Sci. 162, 260–266 (2019)

    Google Scholar 

  28. Stanojević, B., Stanojević, M.: Analytic description to the fuzzy efficiencies in fuzzy standard data envelopment analysis. Procedia Comput. Sci. 199, 487–494 (2022)

    Article  Google Scholar 

  29. Stanojević, B., Stanojević, M.: Empirical \((\alpha ,\beta )\)-acceptable optimal values to full fuzzy linear fractional programming problems. Procedia Comput. Sci. 199, 34–39 (2022)

    Article  Google Scholar 

  30. Stanojević, B., Stanojević, M., Nădăban, S.: Reinstatement of the extension principle in approaching mathematical programming with fuzzy numbers. Mathematics 9, 1272 (2021)

    Article  Google Scholar 

  31. Stanojević, B., Stanojević, M.: Approximate membership function shapes of solutions to intuitionistic fuzzy transportation problems. Int. J. Comput. Commun. Control 16(1), 4057 (2021)

    Google Scholar 

  32. Stanojević, B., Stanojević, M.: Parametric computation of a fuzzy set solution to a class of fuzzy linear fractional optimization problems. Fuzzy Optim. Decis. Mak. 15(4), 435–455 (2016). https://doi.org/10.1007/s10700-016-9232-1

    Article  MathSciNet  MATH  Google Scholar 

  33. Valipour, E., Yaghoobi, M.A.: On fuzzy linearization approaches for solving multi-objective linear fractional programming problems. Fuzzy Sets Syst. 434, 73–87 (2022)

    Article  MathSciNet  Google Scholar 

  34. Wu, H., Xu, Z.: Fuzzy logic in decision support: methods, applications and future trends. Int. J. Comput. Commun. Control 16(1), 4044 (2020)

    Article  Google Scholar 

  35. Wu, H.C.: Generalized extension principle. Fuzzy Optim. Decis. Mak. 9, 31–68 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  36. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MATH  Google Scholar 

  37. Zimmermann.H.-J.: Applications of fuzzy set theory to mathematical programming. Inf. Sci. 36(1), 29–58 (1985)

    Google Scholar 

  38. Zhou, J., Yang, F., Wang, K.: Fuzzy arithmetic on LR fuzzy numbers with applications to fuzzy programming. J. Intell. Fuzzy Syst. 30(1), 71–87 (2016)

    Article  MATH  Google Scholar 

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Acknowledgments

This work was supported by the Serbian Ministry of Education, Science and Technological Development through Mathematical Institute of the Serbian Academy of Sciences and Arts and Faculty of Organizational Sciences of the University of Belgrade.

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Correspondence to Bogdana Stanojević .

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Stanojević, B., Stanojević, M. (2023). Full Fuzzy Fractional Programming Based on the Extension Principle. In: Mihić, M., Jednak, S., Savić, G. (eds) Sustainable Business Management and Digital Transformation: Challenges and Opportunities in the Post-COVID Era. SymOrg 2022. Lecture Notes in Networks and Systems, vol 562. Springer, Cham. https://doi.org/10.1007/978-3-031-18645-5_4

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  • DOI: https://doi.org/10.1007/978-3-031-18645-5_4

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