Portfolio optimization with percentage error-based fuzzy random data for industrial production
Data-driven decision-making processes are pervasive in various domains, yet the inherent uncertainties within observational and measurement data can lead to misleading outcomes, particularly in portfolio selection where randomness may seem ambiguous. While existing methodologies recognize the signif...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2024
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/11839/1/P17175_5d94fbdfb197989a8805796c3ad99fe0.pdf%209.pdf http://eprints.uthm.edu.my/11839/ |
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| Summary: | Data-driven decision-making processes are pervasive in various domains, yet the inherent uncertainties within observational and measurement data can lead to misleading outcomes, particularly in portfolio selection where randomness may seem ambiguous. While existing methodologies recognize the significance of data preprocessing in managing uncertainties such as fuzziness and randomness, a systematic framework to effectively address these challenges is currently lacking. This study aims to bridge this gap by presenting a comprehensive framework tailored to efficiently handle uncertainty during the preprocessing stage. The proposed framework not only acknowledges the importance of data preprocessing but also offers a systematic approach to processing fuzzy random data, thus providing a robust foundation for portfolio selection algorithms. Leveraging fuzzy integers to manage fuzziness and probability distributions to address randomness, our methodology ensures the construction of reliable portfolio selection strategies. The main objective is to optimize selection based on industrial production, effectively managing uncertainty in traditional portfolio selection models. In this proposed approach, fuzziness is handled using fuzzy numbers, and randomness is addressed through probability distributions. The efficacy of this approach is demonstrated in agricultural planning, evaluating five distinct industrial production types: Agriculture, Mining, Manufacturing, Electricity, and Water. The findings underscore the effectiveness of the proposed methodology in managing uncertainties, reducing errors in model development stages, and providing a robust framework for optimal portfolio selection tailored to industrial production contexts, thereby enhancing decision-making processes in uncertain environments |
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