Role of Octagonal Fuzzy Numbers in Solving Some Special Fuzzy Linear Programming Problems

2018 ◽  
pp. 152-163
Author(s):  
Felbin C. Kennedy ◽  
S. U. Malini
Keyword(s):  
Linear Programming ◽  
Fuzzy Numbers ◽  
Fuzzy Linear Programming ◽  
Linear Programming Problems

NEW IMPROVED METHOD FOR SOLVING THE FUZZY LINEAR PROGRAMMING PROBLEMS WITH VARIABLES GIVEN AS FUZZY NUMBERS

2021 ◽  
Vol 10 (12) ◽  
pp. 3699-3723
Author(s):  
L. Kané ◽  
M. Konaté ◽  
L. Diabaté ◽  
M. Diakité ◽  
H. Bado
Keyword(s):  
Linear Programming ◽  
Fuzzy Numbers ◽  
Optimal Solution ◽  
Fuzzy Linear Programming ◽  
Interval Linear Programming ◽  
Interval Numbers ◽  
Programming Technique ◽  
Solution Approach ◽  
Interval Linear ◽  
Linear Programming Problems

The present paper aims to propose an alternative solution approach in obtaining the fuzzy optimal solution to a fuzzy linear programming problem with variables given as fuzzy numbers with minimum uncertainty. In this paper, the fuzzy linear programming problems with variables given as fuzzy numbers is transformed into equivalent interval linear programming problems with variables given as interval numbers. The solutions to these interval linear programming problems with variables given as interval numbers are then obtained with the help of linear programming technique. A set of six random numerical examples has been solved using the proposed approach.

scholarly journals Solving Fuzzy Linear Programming Problems with Fuzzy Decision Variables

Mathematics ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 569
Author(s):  
Wu
Keyword(s):  
Linear Programming ◽  
Numerical Method ◽  
Fuzzy Numbers ◽  
Fuzzy Linear Programming ◽  
Optimal Solutions ◽  
Membership Functions ◽  
Fuzzy Decision ◽  
Existence Of Optimal Solutions ◽  
Decision Variables ◽  
Linear Programming Problems

The numerical method for solving the fuzzy linear programming problems with fuzzydecision variables is proposed in this paper. The difficulty for solving this kind of problem is thatthe decision variables are assumed to be nonnegative fuzzy numbers instead of nonnegative realnumbers. In other words, the decision variables are assumed to be membership functions. One of thepurposes of this paper is to derive the analytic formula of error estimation regarding the approximateoptimal solution. On the other hand, the existence of optimal solutions is also studied in this paper.Finally we present two numerical examples to demonstrate the usefulness of the numerical method.

scholarly journals A Novel Approach to Solve Fully Fuzzy Linear Programming Problems with Modified Triangular Fuzzy Numbers

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2937
Author(s):  
Saeid Jafarzadeh Ghoushchi ◽  
Elnaz Osgooei ◽  
Gholamreza Haseli ◽  
Hana Tomaskova
Keyword(s):  
Linear Programming ◽  
Fuzzy Numbers ◽  
Triangular Fuzzy Number ◽  
Fuzzy Linear Programming ◽  
Fuzzy Decision ◽  
Triangular Fuzzy Numbers ◽  
Novel Approach ◽  
Decision Variables ◽  
Linear Programming Problems ◽  
Fuzzy Solution

Recently, new methods have been recommended to solve fully fuzzy linear programming (FFLP) issues. Likewise, the present study examines a new approach to solve FFLP issues through fuzzy decision parameters and variables using triangular fuzzy numbers. The strategy, which is based on alpha-cut theory and modified triangular fuzzy numbers, is suggested to obtain the optimal fully fuzzy solution for real-world problems. In this method, the problem is considered as a fully fuzzy problem and then is solved by applying the new definition presented for the triangular fuzzy number to optimize decision variables and the objective function. Several numerical examples are solved to illustrate the above method.

scholarly journals Comparing different ranking functions for solving fuzzy linear programming problems with fuzzy cost coefficients

2021 ◽  
Vol 4 (2) ◽  
pp. 3-17
Author(s):  
Betsabé Pérez Garrido ◽  
Szabolcs Szilárd Sebrek ◽  
Viktoriia Semenova
Keyword(s):  
Linear Programming ◽  
Objective Function ◽  
Fuzzy Numbers ◽  
Fuzzy Linear Programming ◽  
Ranking Functions ◽  
Statistical Software ◽  
Linear Programming Problems ◽  
Fuzzy Cost ◽  
The Cost ◽  
Cost Vector

In many applications of linear programming, the lack of exact information results in various problems. Nevertheless, these types of problems can be handled using fuzzy linear programming. This study aims to compare different ranking functions for solving fuzzy linear programming problems in which the coefficients of the objective function (the cost vector) are fuzzy numbers. A numerical example is introduced from the field of tourism and then solved using five ranking functions. Computations were carried out using the FuzzyLP package implemented in the statistical software R.

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