Solving neutrosophic multi-objective linear fractional programming problem using central measures
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.4.23Keywords:
Neutrosophic multi-objective linear fractional programming problem, Neutrosophic triangular number, Harmonic averaging techniques, Advanced harmonic averaging techniques.Dimensions Badge
Issue
Section
License
Copyright (c) 2023 The Scientific Temper
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
In this article, we present different kind of mean technique to solve Multi-Objective Linear Fractional Programming Problem (MOLFPP) in Neutrosophic Environment. Here in these mean techniques the MOLFPP is converted into Single Objective Linear Programming problem(SOLPP) and then we obtained the optimal solution by simplex method in Neutrosophic Environment. The proposed method is illustrated with the help of a numerical example.Abstract
How to Cite
Downloads
Similar Articles
- Jonnakuti V. G. Rama Rao, Muthuvel Balasubramanian, Chaladi S. Gangabhavani, Mutyala A. Devi, Kona D. Devi, A TLBO algorithm-based optimal sizing in a standalone hybrid renewable energy system , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- P. J. Robinson, S. W. A. Prakash, Stochastic artificial neural network for magdm problem solving in intuitionistic fuzzy environment , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Nalini. S, Ritha. W, Sasitharan Nagapan, Optimal Inventory Policies for Perishable Products Under Demand and Lead Time Uncertainty , The Scientific Temper: Vol. 16 No. 09 (2025): The Scientific Temper
- Sharada C, T N Ravi, S Panneer Arokiara, Lancaster sliced regressive keyword extraction based semantic analytics on social media documents , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- Jadhav Girish Vasantrao, Chirag Patel, AT&C and non-technical loss reduction in smart grid using smart metering with AI techniques , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- Anita M, Shakila S, Stochastic kernelized discriminant extreme learning machine classifier for big data predictive analytics , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- R. Sakthiraman, L. Arockiam, RFSVMDD: Ensemble of multi-dimension random forest and custom-made support vector machine for detecting RPL DDoS attacks in an IoT-based WSN environment , The Scientific Temper: Vol. 16 No. 03 (2025): The Scientific Temper
- R. A. Askerov, The role of improving the business environment in agriculture in ensuring the country’s food security , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Temesgen Asfaw, Customer churn prediction using machine-learning techniques in the case of commercial bank of Ethiopia , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- B. Kalpana, P. Krishnamoorthy, S. Kanageswari, Anitha J. Albert, Machine learning approaches for predicting species interactions in dynamic ecosystems , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
