Authors: Stanojević, Bogdana 
Stanojević, Milan
Affiliations: Computer Science 
Title: Empirical versus analytical solutions to full fuzzy linear programming
Journal: Advances in Intelligent Systems and Computing
Volume: 1243 AISC
First page: 220
Last page: 233
Conference: 8th International Conference on Computers Communications and Control, ICCCC 2020 ; Oradea; Romania; 11 May 2020 through 15 May 2020
Issue Date: 1-Jan-2021
ISBN: 978-3-030-53650-3
ISSN: 2194-5357
DOI: 10.1007/978-3-030-53651-0_19
We approach the full fuzzy linear programming by grounding the definition of the optimal solution in the extension principle framework. Employing a Monte Carlo simulation, we compare an empirically derived solution to the solutions yielded by approaches proposed in the literature. We also propose a model able to numerically describe the membership function of the fuzzy set of feasible objective values. At the same time, the decreasing (increasing) side of this membership function represents the right (left) side of the membership function of the fuzzy set containing the maximal (minimal) objective values. Our aim is to provide decision-makers with relevant information on the extreme values that the objective function can reach under uncertain given constraints.
Keywords: Extension principle | Full fuzzy linear programming | Fuzzy numbers | Monte Carlo simulation
Publisher: Springer Link

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