Abstract
Quadratic Programming (QP) is a mathematical modeling technique designed to optimize the usage of limited resources and has been widely applied to solve real world problems. In conventional quadratic programming model the parameters are known constants. However in many practical situations, it is not reasonable to require that the constraints or the objective function in quadratic programming problems be specified in precise, crisp terms. In such situations, it is desirable to use some type of Fuzzy Quadratic Programming (FQP) problem. In this paper a new approach is proposed to derive the fuzzy objective value of fuzzy quadratic programming problem, where the constraints coefficients and the right-hand sides are all triangular fuzzy numbers. The proposed method is solved using MATLABTM toolbox and the numerical results are presented.
Highlights
Quadratic programming is a particular kind of nonlinear programming
In many practical situations, it is not reasonable to require that the constraints or the objective function in quadratic programming problems be specified in precise, crisp terms
It is desirable to use some type of Fuzzy Quadratic Programming (FQP) problem
Summary
There are several classes of problems that are naturally expressed as quadratic problems. Several applications and test problems for quadratic programming can be found in [1] [2] [3] [4] [5]. A quadratic programming problem is addressed: Example 1.1: Consider a problem which is formulated as follows: max z = 2x1 + x2 + 2x12 + x1x2 + 2x22 s.t. and bT = (2,1).
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