Open Access
RAIRO-Oper. Res.
Volume 56, Number 6, November-December 2022
Page(s) 4035 - 4045
Published online 29 November 2022
  • G. Alefeld and J. Herzberger, Introduction to Interval Computations. Academic Press, New York (1983). [Google Scholar]
  • M.Y. Ali, A. Sultana and A.F.M.K. Khan, Comparison of fuzzy multiplication operation on triangular fuzzy number. IOSR J. Math. 12 (2016) 35–41. [Google Scholar]
  • M. Arana-Jiménez, editor. Optimiality Conditions in Vector Optimization. Bentham Science Publishers Ltd., Bussum (2010). [CrossRef] [Google Scholar]
  • M. Arana-Jiménez, Nondominated solutions in a fully fuzzy linear programming problem. Math Meth Appl Sci. 41 (2018) 7421–7430. [CrossRef] [Google Scholar]
  • M. Arana-Jiménez, Fuzzy Pareto solutions in fully fuzzy multiobjective linear programming. Adv. Intell. Syst. Comput. 991 (2020) 509–517. [Google Scholar]
  • M. Arana-Jiménez and V. Blanco, On a fully fuzzy framework for minimax mixed integer linear programming. Comput. Ind. Eng. 128 (2019) 170–179. [CrossRef] [Google Scholar]
  • M. Arana-Jiménez and L.L. Salles, Sufficient condition for partial efficiency in a bicriteria nonlinear cutting stock problem. RAIRO: Oper. Res. 51 (2017) 709–717. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • M. Arana-Jiménez and M.C. Sánchez-Gil, On generating the set of nondominated solutions of a linearprogramming problem with parameterized fuzzy numbers. J. Global Optim. 77 (2020) 27–52. [CrossRef] [MathSciNet] [Google Scholar]
  • M. Arana-Jiménez, A. Rufián-Lizana, Y. Chalco-Cano and H. Román-Flores, Generalized convexity in fuzzy vector optimization through a linear ordering. Inf. Sci. 312 (2015) 13–24. [CrossRef] [Google Scholar]
  • M. Arana-Jiménez, V. Blanco and E. Fernández, On the fuzzy maximal covering location problem. Eur. J. Oper. Res. 283 (2020) 692–705. [CrossRef] [Google Scholar]
  • A.D. Báez-Sánchez, A.C. Moretti and M.A. Rojas-Medar, On polygonal fuzzy sets and numbers. Fuzzy Sets Syst. 209 (2012) 54–65. [CrossRef] [Google Scholar]
  • R.E. Bellman and L.A. Zadeh, Decision making in a fuzzy environment. Manage. Sci. 17 (1970) 141–164. [Google Scholar]
  • S.K. Bharati and S.R. Singh, A computational algorithm for the solution of fully fuzzy multi-objective linear programming problem. Int. J. Dyn. Control 6 (2018) 1384–1391. [CrossRef] [MathSciNet] [Google Scholar]
  • B. Bhardwaj and A. Kumar, A note on the paper “A simplified novel technique for solving fully fuzzy linear programming problems”. J. Optim. Theory Appl. 163 (2014) 685–696. [CrossRef] [MathSciNet] [Google Scholar]
  • L. Campos and J.L. Verdegay, Linear programming problems and ranking of fuzzy numbers. Fuzzy Set. Syst. 32 (1989) 1–11. [CrossRef] [Google Scholar]
  • D. Chakraborty, D.K. Jana and T.K. Roy, A new approach to solve fully fuzzy transportation problem using triangular fuzzy number. Int. J. Oper. Res. 26 (2016) 153–179. [CrossRef] [MathSciNet] [Google Scholar]
  • M. Clemente-Císcar, S. San Matas and V. Giner-Bosch, A methodology based on profitability criteria for defining the partial defection of customers in non-contractual settings. Eur. J. Oper. Res. 239 (2014) 276–285. [CrossRef] [Google Scholar]
  • D. Dubois and H. Prade, Operations on fuzzy numbers. Int. J. Syst. Sci. 9 (1978) 613–626. [Google Scholar]
  • D. Dubois and H. Prade, Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York (1980). [Google Scholar]
  • A. Ebrahimnejad, S.H. Nasseri, F.H. Lotfi and M. Soltanifar, A primal-dual method for linear programming problems with fuzzy variables. Eur. J. Ind. Eng. 4 (2010) 189–209. [CrossRef] [Google Scholar]
  • R. Ezzati, E. Khorram and R. Enayati, A new algorithm to solve fully fuzzy linear programming problems using the MOLP problem. Appl. Math. Modell. 39 (2015) 3183–3193. [CrossRef] [Google Scholar]
  • K. Ganesan and P. Veeramani, Fuzzy linear programs with trapezoidal fuzzy numbers. Ann. Oper. Res. 143 (2006) 305–315. [CrossRef] [Google Scholar]
  • M. Ghaznavi, F. Soleimani and N. Hoseinpoor, Parametric analysis in fuzzy number linear programming problems. Int. J. Fuzzy Syst. 18 (2016) 463–477. [CrossRef] [MathSciNet] [Google Scholar]
  • R. Goestschel and W. Voxman, Elementary fuzzy calculus. Fuzzy Sets Syst. 18 (1986) 31–43. [CrossRef] [Google Scholar]
  • M.L. Guerra and L. Stefanini, A comparison index for interval based on generalized Hukuhara difference. Soft. Comput. 16 (2012) 1931–1943. [CrossRef] [Google Scholar]
  • M. Hanss, Applied Fuzzy Arithmetic. Springer, Stuttgart (2005). [Google Scholar]
  • M. Jimenez and A. Bilbao, Pareto-optimal solutions in fuzzy multiobjective linear programming. Fuzzy Sets Syst. 160 (2009) 2714–2721. [CrossRef] [Google Scholar]
  • A. Kaufmann and M.M. Gupta, Introduction to Fuzzy Arithmetic Theory and Applications. Van Nostrand Reinhold, New York (1985). [Google Scholar]
  • I.U. Khan, T. Ahmad and N. Maan, A simplified novel technique for solving fully fuzzy linear programming problems. J. Optim. Theory Appl. 159 (2013) 536–546. [CrossRef] [MathSciNet] [Google Scholar]
  • I.U. Khan, T. Ahmad and N. Maan, A reply to a note on the paper “A simplified novel technique for solving fully fuzzy linear programming problems”. J. Optimiz. Theory Appl. 173 (2017) 353–356. [CrossRef] [Google Scholar]
  • A. Kumar, J. Kaur and P. Singh, A new method for solving fully fuzzy linear programming problems. Appl. Math. Modell. 35 (2011) 817–823. [CrossRef] [MathSciNet] [Google Scholar]
  • B. Liu, Uncertainty Theory. Springer-Verlag, Berlin (2015). [Google Scholar]
  • Q. Liu and X. Gao, Fully fuzzy linear programming problem with triangular fuzzy numbers. J. Comput. Theor. Nanosci. 13 (2016) 4036–4041. [CrossRef] [Google Scholar]
  • F.H. Lotfi, T. Allahviranloo, M.A. Jondabeha and L. Alizadeh, Solving a fully fuzzy linear programming using lexicography method and fuzzy approximate solution. Appl. Math. Modell. 3 (2009) 3151–3156. [CrossRef] [Google Scholar]
  • H.R. Maleki, Ranking functions and their applications to fuzzy linear programming. Far East J. Math. Sci. 4 (2002) 283–301. [Google Scholar]
  • H.R. Maleki, M. Tata and M. Mashinchi, Linear programming with fuzzy variables. Fuzzy Set. Syst. 109 (2000) 21–33. [CrossRef] [Google Scholar]
  • R. Marler and J. Arora, Survey of multi-objective optimization methods for engineering. Struct. Multi. Optim. 26 (2004) 269–395. [Google Scholar]
  • M.K. Mehlawat, A. Kumar, S. Yadav and W. Chen, Data envelopment analysis based fuzzy multi-objective portfolio selection model involving higher moments. Inf. Sci. 460 (2018) 128–150. [CrossRef] [Google Scholar]
  • R.E. Moore, Interval Analysis. Prentice-Hall, Englewood Cliffs, NJ (1966). [Google Scholar]
  • R.E. Moore, Method and Applications of Interval Analysis. SIAM, Philadelphia (1979). [CrossRef] [Google Scholar]
  • H.S. Najafi and S.A. Edalatpanah, A note on “A new method for solving fully fuzzy linear programming problems”. Appl. Math. Modell. 37 (2013) 7865–7867. [CrossRef] [Google Scholar]
  • L. Stefanini, L. Sorini and M.L. Guerra, Parametric representation of fuzzy numbers and application to fuzzy calculus. Fuzzy Sets Syst. 157 (2006) 2423–2455. [CrossRef] [Google Scholar]
  • L. Stefanini and M. Arana-Jiménez, Karush–Kuhn–Tucker conditions for interval and fuzzy optimization in several variables under total and directional generalized differentiability. Fuzzy Sets Syst. 262 (2019) 1–34. [CrossRef] [Google Scholar]
  • J. Tind and M.M. Wiecek, Augmented Lagrangian and Tchebycheff approaches in multiple objective programming. J. Global Optim. 14 (1999) 251–266. [CrossRef] [MathSciNet] [Google Scholar]
  • H.C. Wu, The optimality conditions for optimization problems with convex constraints and multiple fuzzy-valued objective functions. Fuzzy Optim. Decis. Making 8 (2009) 295–321. [CrossRef] [MathSciNet] [Google Scholar]
  • P.L. Yu, A class of solutions for group decision problems. Manage. Sci. 19 (1973) 936–946. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.