A modified novel method for solving the uncertainty linear programming problems based on triangular neutrosophic number
Publish place: Transactions on Fuzzy Sets and Systems، Vol: 1، Issue: 1
Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
View: 141
This Paper With 15 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_TFSS-1-1_011
تاریخ نمایه سازی: 27 دی 1401
Abstract:
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.
Keywords:
linear programming problem , Triangular Neutrosophic Number , Ranking Function , Nutrosophic Linear Programming Problem
Authors
Kshitish Mohanta
Department of Mathematics Indra Gandhi National Tribal University, Amarkantak Madhya Pradesh, ۴۸۴۸۸۷, India
Vishal Chaubey
Department of Mathematics Indra Gandhi National Tribal University, Amarkantak Madhya Pradesh, ۴۸۴۸۸۷, India
Deena Sharanappa
Department of Mathematics Indra Gandhi National Tribal University, Amarkantak Madhya Pradesh, ۴۸۴۸۸۷, India
Vishnu Mishra
Department of Mathematics, Indira Gandhi National Tribal University, Lalpur, Amarkantak, Anuppur, Madhya Pradesh ۴۸۴ ۸۸۷, India
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :