Program Linier Fuzzy Penuh Dengan Metode Kumar

Fully fuzzy linear programing is part of a crisp linear programming (linear programimg with a number of crisp) which the numbers used are fuzzy numbers. Solving a fully fuzzy linear programming problems by using Kumar method to fuzzy optimal solution and crisp optimal value.. Solving fuzzy optimal solution by Kumar method on triangular fuzzy number to divide into tree objective functions and defuzzification by using ranking function and α - cutting to get crisp optimal solution. This paper discusses about Kumar methods method for solving fully fuzzy linear programming in which fuzzy numbers used are triangular fuzzy numbers

Metode Mehar Untuk Solusi Optimal Fuzzy Dan Analisa Sensitivitas Program Linier Dengan Variabel Fuzzy Bilangan Triangular

. Fuzzy linear programming problems containing closely with uncertainty about the parameters. Changes in the value of the parameters without changing the optimal solution or change the optimal solution is called sensitivity analysis. Sensitivity analysis is a basic for studying the effect of the changes that occur to the optimal solution. Linear programming with fuzzy variable is a form of fuzzy linear program is not fully because there are objective function coefficients and coefficients of constraints that are crisp numbers. Resolving the problem of linear programming with fuzzy variables by using mehar method will get solutions and optimal fuzzy value and solutions and optimal crisp value. To solve the problem of linear program with fuzzy variable is using mehar, must be converted beforehand in the form of crisp linear programming. This thesis explores mehar method to solve linear programming problems with fuzzy variables with triangular number and a sensitivity analysis on the optimum solution FVLP so that when there is a change of data of the problem, new solution will remain optimal

OPTIMASI MASALAH PROGRAM LINIER FUZZY TIDAK PENUH MENGGUNAKAN METODE SIMPLEKS DAN ALGORITMA DETERMINAN MATRIKS ORDO DUA

. Not fully fuzzy linear programming where there is trapezoidal fuzzy number form on decision variables, objective function coefficients, constraint functions coefficients, or the right side of constraints is part of fuzzy linear programming. This essay discusses about how to determine the optimal solution of not fully fuzzy linear programming problems. The method used is Simplex Method and Determinant’s Algorithm of Two Square Matrix and Robust Ranking to convert the fuzzy linear programming problems into crisp linear programming problem. Determinant of two order matrix Algorithm involves determinant of matrix in each iteration steps to obtain an optimal solution that is applicable to the maximum or minimum case. This algorithm is more efficient than usual simplex method which involves row transformation

Metode Dekomposisi Dan Metode Big-muntuk Menyelesaikan Program Linier Variabel Fuzzy Triangular Studi Kasus: Home Industri Borobudur Furniture, Bogor, Indonesia

Fuzzy Variable Linear Programming (FVLP) with triangular fuzzy variable is part of not fully fuzzy linear programming with decision variables and the right side is a fuzzy number. Solving FVLP with triangular fuzzy variables used Decomposition Methods and Big-M Methods by using Robust Ranking to obtain crisp values. DecompositionMethods of resolving cases maximization and minimization FVLP by dividing the problems into three parts CLP. Solving FVLP with Big-M Methods to directly solve the minimization case FVLP do without confirmation first. The optimal solution fuzzy, crisp optimal solution, optimal objective function fuzzy and crisp optimal objective function generated from Decomposition Methods and Big-M Methods for minimizing case has same solution. Decomposition Methods has a longer process because it divides the problem into three parts CLP and Big-M Methods has a fewer processes but more complicated because the process without divide the problems into three part

Solving fully fuzzy critical path analysis in project . . .

A new method for finding fuzzy optimal solution, the maximum total completion fuzzy time and fuzzy critical path for the given fully fuzzy critical path (FFCP) problems using crisp linear programming (LP) problem is proposed. In this proposed method, all the parameters are represented by triangular fuzzy number. The fuzzy optimal solution of the FFCP problems obtained by the proposed method, do not contain any negative part of the values of the fuzzy decision variables. This paper will present with great clarity of the proposed method and illustrate its application to FFCP problems occurring in real life situations

A NEW APPROACH ON SOLVING INTUITIONISTIC FUZZY LINEAR PROGRAMMING PROBLEM

ABSTRACT In this paper, we propose a new approach for solving Intuitionistic Fuzzy Linear Programming Problems (IFLPP) involving triangular intuitionistic fuzzy numbers (TIFN). We introduce a new algorithm for the solution of an Intuitionistic Fuzzy Linear Programming Problem without converting in to one or more classical Linear Programming Problems. Numerical examples are provided to show the efficiency of the proposed algorithm. Keywords: intuitionistic fuzzy set, fuzzy number, triangular intuitionistic fuzzy number, fuzzy linear programming problem. INTRODUCTION Modelling of real life problems involving optimization process. It is often difficult to get crisp and exact information for various parameters affecting the process and it involves high information cost. Furthermore the optimal solution of the problem depends on a limited number of constraints or conditions and thus some of the information collected is not useful. Under such situations it is highly impossible to formulate the mathematical model through the classical traditional methods. Hence in order to reduce information costs and also to construct a real model, the use of intuitionistic fuzzy number is more appropriate. Fuzzy sets are an efficient and reliable tool that allows us to handle such systems having imprecise parameters effectively. Atanossov [6] extended the fuzzy sets to the theory intuitionistic fuzzy sets. His studies emphasized that in view of handling imprecision, vagueness or uncertainty in information both the degree of belonging and degree of non-belonging should be considered as two independent properties as these are not complement of each other. Bellmann and Zade

An ε-Constraint Method for Multiobjective Linear Programming in Intuitionistic Fuzzy Environment

Effective decision-making requires well-founded optimization models and algorithms tolerant of real-world uncertainties. In the mid-1980s, intuitionistic fuzzy set theory emerged as another mathematical framework to deal with the uncertainty of subjective judgments and made it possible to represent hesitancy in a decision-making problem. Nowadays, intuitionistic fuzzy multiobjective linear programming (IFMOLP) problems are a topic of extensive research, for which a considerable number of solution approaches are being developed. Among the available solution approaches, ranking function-based approaches stand out for their simplicity to transform these problems into conventional ones. However, these approaches do not always guarantee Pareto optimal solutions. In this study, the concepts of dominance and Pareto optimality are extended to the intuitionistic fuzzy case by using lexicographic criteria for ranking triangular intuitionistic fuzzy numbers (TIFNs). Furthermore, an intuitionistic fuzzy epsilon-constraint method is proposed to solve IFMOLP problems with TIFNs. The proposed method is illustrated by solving two intuitionistic fuzzy transportation problems addressed in two studies (S. Mahajan and S. K. Gupta's, "On fully intuitionistic fuzzy multiobjective transportation problems using different membership functions," Ann Oper Res, vol. 296, no. 1, pp. 211-241, 2021, and Ghosh et al.'s, "Multi-objective fully intuitionistic fuzzy fixed-charge solid transportation problem," Complex Intell Syst, vol. 7, no. 2, pp. 1009-1023, 2021). Results show that, in contrast with Mahajan and Gupta's and Ghosh et al.'s methods, the proposed method guarantees Pareto optimality and also makes it possible to obtain multiple solutions to the problems.MCIN/AEI PID2020-112754GB-I00FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto B-TIC-640-UGR2

Strict Solution Method for Linear Programming Problem with Ellipsoidal Distributions under Fuzziness

This paper considers a linear programming problem with ellipsoidal distributions including fuzziness. Since this problem is not well-defined due to randomness and fuzziness, it is hard to solve it directly. Therefore, introducing chance constraints, fuzzy goals and possibility measures, the proposed model is transformed into the deterministic equivalent problems. Furthermore, since it is difficult to solve the main problem analytically and efficiently due to nonlinear programming, the solution method is constructed introducing an appropriate parameter and performing the equivalent transformations