A Novel Method for Solving Multiobjective Linear Programming Problems with Triangular Neutrosophic Numbers

Autor: Qing Wang, Yi Huang, Shiming Kong, Xinqiang Ma, Youyuan Liu, S. K. Das, S. A. Edalatpanah
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Journal of Mathematics, Vol 2021 (2021)
Druh dokumentu: article
ISSN: 2314-4629
2314-4785
18340679
DOI: 10.1155/2021/6631762
Popis: In the field of operation research, linear programming (LP) is the most utilized apparatus for genuine application in various scales. In our genuine circumstances, the manager/decision-makers (DM) face problems to get the optimal solutions and it even sometimes becomes impossible. To overcome these limitations, neutrosophic set theory is presented, which can handle all types of decision, that is, concur, not certain, and differ, which is common in real-world situations. By thinking about these conditions, in this work, we introduced a method for solving neutrosophic multiobjective LP (NMOLP) problems having triangular neutrosophic numbers. In the literature study, there is no method for solving NMOLP problem. Therefore, here we consider a NMOLP problem with mixed constraints, where the parameters are assumed to be triangular neutrosophic numbers (TNNs). So, we propose a method for solving NMOLP problem with the help of linear membership function. After utilizing membership function, the problem is converted into equivalent crisp LP (CrLP) problem and solved by any suitable method which is readily available. To demonstrate the efficiency and accuracy of the proposed method, we consider one classical MOLP problem and solve it. Finally, we conclude that the proposed approach also helps decision-makers to not only know and optimize the most likely situation but also realize the outcomes in the optimistic and pessimistic business situations, so that decision-makers can prepare and take necessary actions for future uncertainty.
Databáze: Directory of Open Access Journals
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