Abstract
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.
Highlights
In the modern and competitive world, making the right, scientific-based, and timely decisions plays a very important and decisive role in the success or failure of organizations [1,2]
This study examines a new approach to solve fully fuzzy linear programming (FFLP) problems through fuzzy decision parameters and variables using triangular fuzzy numbers
The technique, 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
Summary
In the modern and competitive world, making the right, scientific-based, and timely decisions plays a very important and decisive role in the success or failure of organizations [1,2]. The technique, 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 is solved by applying the new definition presented for the triangular fuzzy number to optimize decision variables and the objective function. The parameters and variables of the model are written based on the modified triangular fuzzy numbers, and the model is solved by considering the middle object as the objective function and the upper and lower objects as constraints Based on this method, more accurate optimal results are obtained, and the uncertainty of the model is reached.
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