Treffer: ‎A Modified Novel Method for Solving the Uncertainty Linear Programming Problems Based on Triangular Neutrosophic Number

Title:
‎A Modified Novel Method for Solving the Uncertainty Linear Programming Problems Based on Triangular Neutrosophic Number
Authors:
Kshitish Mohanta, Vishal Chaubey, Deena Sharanappa, Vishnu Mishra
Source:
Transactions on Fuzzy Sets and Systems, Vol 1, Iss 1, Pp 155-169 (2022)
Publisher Information:
Islamic Azad University, Bandar Abbas Branch, 2022.
Publication Year:
2022
Collection:
LCC:Applied mathematics. Quantitative methods
Subject Terms:
linear programming problem, triangular neutrosophic number, ranking function, nutrosophic linear programming problem, Applied mathematics. Quantitative methods, T57-57.97
Document Type:
Fachzeitschrift article
File Description:
electronic resource
Language:
English
ISSN:
2821-0131
Relation:
https://tfss.journals.iau.ir/article_690702_9434a6d52498602d3001689c0319f796.pdf; https://doaj.org/toc/2821-0131
DOI:
10.30495/tfss.2022.1956751.1022
Access URL:
https://doaj.org/article/57c61d3352ca4838a32f7d592f55d2e9
Accession Number:
edsdoj.57c61d3352ca4838a32f7d592f55d2e9
Database:
Directory of Open Access Journals

Weitere Informationen

Generally, linear programming (LP) problem is the most extensively utilized technique for solving and optimizing real-world problems due to its simplicity and efficiency. However, to deal with the inaccurate data, the neutrosophic set theory comes into play, which creates a simulation of the human decision-making process by considering all parts of the choice (i.e., agree, not sure, and disagree). Keeping the bene ts in mind, we proposed the neutrosophic LP models based on triangular neutrosophic numbers (TNN) and provided a method for solving them. Fuzzy LP problem can be converted into crips LP problem based on the de ned ranking function. The provided technique has been demonstrated with numerical examples given by Abdelfattah. Finally, we found that, when compared to previous approaches, the suggested method is simpler, more efficient, and capable of solving all types of fuzzy LP models.