يك رويكرد جديد به حل برنامه ريزي خطي تماماً فازي با اعداد ذوزنقه اي با استفاده از توابع تبديل

A New Approach to Solve Fully Fuzzy Linear Programming with Trapezoidal Numbers Using Conversion Functions

در اين مقاله ما يك مدل شبكه عصبي براي تشخيص واحدهاي تصميم­گيرنده كارا در تحليل پوششي داده­ها معرفي مي­كنيم. مدل شبكه عصبي پيشنهادي از يك مسئله بهينه­سازي نامقيد حاصل مي­شود. از ديدگاه تئوري نشان داده مي­شود شبكه عصبي پيشنهادي داراي پايداري لياپانف و همگراي سراسري مي­باشد. مدل پيشنهادي تك لايه مي­باشد. شبيه سازي نشان مي­دهد مدل پيشنهادي قادر به تشخيص واحدهاي كارا در تحليل پوششي داده­ها مي­باشد.

Recently، fuzzy linear programming problems have been considered by many. In the literature of fuzzy linear programming several models are offered and therefore some various methods have been suggested to solve these problems. One of the most important of these problems that recently has been considered; are Fully Fuzzy Linear Programming (FFLP)، which all coefficients and variables of the problem are the same kind of fuzzy numbers. One of most common of them is the model in which all fuzzy parameters are discussed by triangle numbers. In this paper، we first define a fully fuzzy linear programming with trapezoidal numbers and then suggest a new method based on reducing the original problem to the problem with triangle number. Specially، a conversion function for converting two trapezoidal and triangular numbers to each other is offered. Finally، the mentioned method is illustrated by a numerical example.