scholarly journals Solving Multi-Objective Linear Programming Problems with Fuzzy Interval Based on Decomposition Method

2021 ◽  
Vol 23 (07) ◽  
pp. 751-760
Author(s):  
Mohamed Solomon ◽  
◽  
Mohamed Saied Abd-Alla ◽  
Keyword(s):  
Linear Programming ◽  
Decomposition Method ◽  
Ranking Functions ◽  
Numerical Examples ◽  
Fuzzy Variables ◽  
Multi Objective ◽  
Linear Programming Problems ◽  
Available Information ◽  
Fuzzy Interval ◽  
Technical Coefficients

Most of the problems in real-world situations have a multi-objective, in these situations, available information in the system is not exact or imprecise. In this paper, solving multi-objective linear programming problems with fuzzy non-negative intervals such as objective function coefficients, technical coefficients, and fuzzy variables by using an approximation but a convenient method called decomposition method has been proposed. In the composition method, ranking functions are not used. With the help of numerical examples, the method is illustrated.

scholarly journals A Simplified Novel Technique for Solving Linear Programming Problems with Triangular Fuzzy Variables via Interval Linear Programming Problems

2021 ◽  
pp. 82-93
Author(s):  
Ladji Kané ◽  
Lassina Diabaté ◽  
Daouda Diawara ◽  
Moussa Konaté ◽  
Souleymane Kané
Keyword(s):  
Linear Programming ◽  
Simplex Method ◽  
Real Problem ◽  
Interval Linear Programming ◽  
Numerical Examples ◽  
Fuzzy Variables ◽  
Novel Technique ◽  
Interval Linear ◽  
Linear Programming Problems

This study proposes a novel technique for solving Linear Programming Problems with triangular fuzzy variables. A modified version of the well-known simplex method and the Existing Method for Solving Interval Linear Programming problems are used for solving linear programming problems with triangular fuzzy variables. Furthermore, for illustration, some numerical examples and one real problem are used to demonstrate the correctness and usefulness of the proposed method. The proposed algorithm is flexible, easy, and reasonable.

scholarly journals Solving fully fuzzy linear programming problems by controlling the variation range of variables

2021 ◽  
Vol 103 (3) ◽  
pp. 13-24
Author(s):  
S.M. Davoodi ◽  
◽  
N.A. Abdul Rahman ◽  
Keyword(s):  
Linear Programming ◽  
Optimal Solution ◽  
Fuzzy Linear Programming ◽  
Ranking Functions ◽  
Fuzzy Variables ◽  
Decision Variables ◽  
Proposed Model ◽  
Linear Programming Problems ◽  
Left And Right ◽  
The Right

This paper deals with a fully fuzzy linear programming problem (FFLP) in which the coefficients of decision variables, the right-hand coefficients and variables are characterized by fuzzy numbers. A method of obtaining optimal fuzzy solutions is proposed by controlling the left and right sides of the fuzzy variables according to the fuzzy parameters. By using fuzzy controlled solutions, we avoid unexpected answers. Finally, two numerical examples are solved to demonstrate how the proposed model can provide a better optimal solution than that of other methods using several ranking functions.

Solving Fully Fuzzy Multi-Objective Linear Programming Problems

2012 ◽  
Vol 3 (4) ◽  
pp. 1-6 ◽  
Author(s):  
M.Jayalakshmi M.Jayalakshmi ◽  
◽  
P.Pandian P.Pandian
Keyword(s):  
Linear Programming ◽  
Multi Objective ◽  
Linear Programming Problems

THE DS/AHP METHOD UNDER PARTIAL INFORMATION ABOUT CRITERIA AND ALTERNATIVES BY SEVERAL LEVELS OF CRITERIA

2012 ◽  
Vol 11 (02) ◽  
pp. 307-326 ◽  
Author(s):  
LEV V. UTKIN ◽  
NATALIA V. SIMANOVA
Keyword(s):  
Linear Programming ◽  
Incomplete Information ◽  
Decision Problem ◽  
Partial Information ◽  
Decision Makers ◽  
Ahp Method ◽  
Numerical Examples ◽  
Finite Set ◽  
Linear Programming Problems ◽  
Decision Alternatives

An extension of the DS/AHP method is proposed in the paper. It takes into account the fact that the multi-criteria decision problem might have several levels of criteria. Moreover, it is assumed that expert judgments concerning the criteria are imprecise and incomplete. The proposed extension also uses groups of experts or decision makers for comparing decision alternatives and criteria. However, it does not require assigning favorability values for groups of decision alternatives and criteria. The computation procedure for processing and aggregating the incomplete information about criteria and decision alternatives is reduced to solving a finite set of linear programming problems. Numerical examples explain in detail and illustrate the proposed approach.

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