Possibility linear programming with trapezoidal fuzzy numbers

Abstract Fuzzy linear programming with trapezoidal fuzzy numbers (TrFNs) is considered and a new method is developed to solve it. In this method, TrFNs are used to capture imprecise or uncertain information for the imprecise objective coefficients and/or the imprecise technological coefficients and/or available resources. The auxiliary multi-objective programming is constructed to solve the corresponding possibility linear programming with TrFNs. The auxiliary multi-objective programming involves four objectives: minimizing the left spread, maximizing the right spread, maximizing the left endpoint of the mode and maximizing the middle point of the mode. Three approaches are proposed to solve the constructed auxiliary multi-objective programming, including optimistic approach, pessimistic approach and linear sum approach based on membership function. An investment example and a transportation problem are presented to demonstrate the implementation process of this method. The comparison analysis shows that the fuzzy linear programming with TrFNs developed in this paper generalizes the possibility linear programming with triangular fuzzy numbers.

[1]  T. Allahviranloo,et al.  Solving a full fuzzy linear programming using lexicography method and fuzzy approximate solution , 2009 .

[2]  K. Ganesan,et al.  Fuzzy linear programs with trapezoidal fuzzy numbers , 2006, Ann. Oper. Res..

[3]  Amit Kumar,et al.  A new method for solving fully fuzzy linear programming problems , 2011 .

[4]  Stephan Dempe,et al.  On the calculation of a membership function for the solution of a fuzzy linear optimization problem , 2012, Fuzzy Sets Syst..

[5]  C. Hwang,et al.  A new approach to some possibilistic linear programming problems , 1992 .

[6]  T. Allahviranloo,et al.  SOLVING FULLY FUZZY LINEAR PROGRAMMING PROBLEM BY THE RANKING FUNCTION , 2008 .

[7]  Mir Saman Pishvaee,et al.  Environmental supply chain network design using multi-objective fuzzy mathematical programming , 2012 .

[8]  Nils Brunsson My own book review : The Irrational Organization , 2014 .

[9]  Mashaallah Mashinchi,et al.  A method for solving a fuzzy linear programming , 2001 .

[10]  Hans-Jürgen Zimmermann,et al.  Decision Making in Fuzzy Environment , 1985 .

[11]  H. Zimmermann Fuzzy programming and linear programming with several objective functions , 1978 .

[12]  H. Carter Fuzzy Sets and Systems — Theory and Applications , 1982 .

[13]  B. Julien An extension to possibilistic linear programming , 1994 .

[14]  Mashaallah Mashinchi,et al.  Linear programming with fuzzy variables , 2000, Fuzzy Sets Syst..

[15]  Plamen P. Angelov,et al.  Optimization in an intuitionistic fuzzy environment , 1997, Fuzzy Sets Syst..

[16]  Richard Bellman,et al.  Decision-making in fuzzy environment , 2012 .

[17]  Suresh Chandra,et al.  Fuzzy linear programming under interval uncertainty based on IFS representation , 2012, Fuzzy Sets Syst..

[18]  A. Ebrahimnejad Some new results in linear programs with trapezoidal fuzzy numbers: Finite convergence of the Ganesan and Veeramani’s method and a fuzzy revised simplex method , 2011 .

[19]  Xinwang Liu,et al.  Measuring the satisfaction of constraints in fuzzy linear programming , 2001, Fuzzy Sets Syst..

[20]  H. Ishibuchi,et al.  Multiobjective programming in optimization of the interval objective function , 1990 .

[21]  H. Rommelfanger Fuzzy linear programming and applications , 1996 .

[22]  Mashaallah Mashinchi,et al.  Fuzzy Number Linear Programming: A Probabilistic Approach , 2004, AFSS.

[23]  M. Mashinchi,et al.  Fuzzy number linear programming: A probabilistic approach (3) , 2004 .

[24]  Ahmad Makui,et al.  A fuzzy programming approach for dynamic virtual hub location problem , 2012 .

[25]  Özgür Kabak,et al.  Possibilistic linear-programming approach for supply chain networking decisions , 2011, Eur. J. Oper. Res..