Fuzzy goal programming approach for solving linear fractional programming problems with fuzzy conditions
Predicting the exact outcome of a real-life problem, which may occur in various fields like the industrial or healthcare sector, is challenging. Due to high information uncertainty and complicated factors influencing the industrial sector, traditional data-driven prediction approaches can hardly ref...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Ayandegan Institute of Higher Education,
2024-07-01
|
| Series: | Journal of Fuzzy Extension and Applications |
| Subjects: | |
| Online Access: | https://www.journal-fea.com/article_193524_e64f78eb24316a61a9270723cb42b306.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Predicting the exact outcome of a real-life problem, which may occur in various fields like the industrial or healthcare sector, is challenging. Due to high information uncertainty and complicated factors influencing the industrial sector, traditional data-driven prediction approaches can hardly reflect real changes in practical situations. Fuzzy programming is a powerful prediction reasoning and risk assessment model for uncertain environments. This article mainly explores and applies a modified form of fuzzy programming, namely the Fuzzy Linear Fractional Programming Problem (FLFPP), having the coefficients of the objectives and constraints as Triangular Fuzzy Numbers (TFNs). The FLFPP is converted into an equivalent crisp Multi-Objective Linear Fractional Programming Problem (MOLFPP) and solved individually to associate an aspiration level. Then, by applying the Fuzzy Goal Programming (FGP) technique, the maximum degree of each membership goal is obtained by minimizing the negative deviational variables. We carry out two industrial application simulations in a hypothetical industrial scenario. Our study shows that the proposed model is practical and applicable to the uncertain practical environment to realize the prediction, and the results obtained are compared with those of the existing methods. |
|---|---|
| ISSN: | 2783-1442 2717-3453 |