Harnessing Interval Fuzzy Numbers: A Novel Approach to Multi-Criteria Decision-Making Models
محورهای موضوعی : فصلنامه ریاضی
M. Taghizadeh
1
,
Abdollah Hadi-Vencheh
2
,
Mohammad Jalali Varnamkhasti
3
,
Ali Jamshidi
4
1 - Department of Mathematics, Isf.C., Islamic Azad University, Isfahan, Iran
2 - Department of Mathematics, Isf.C., Islamic Azad University, Isfahan, Iran
3 - Department of Mathematics, Isf.C., Islamic Azad University, Isfahan, Iran
4 - Department of Mathematics, Isf.C., Islamic Azad University, Isfahan, Iran
کلید واژه: Multi-Criteria Decision-Making (MCDM), Interval Valued Fuzzy Numbers (IVFNs), Artificial intelligence (AI), Neural networks,
چکیده مقاله :
This paper presents a comprehensive exploration of Multi-Criteria Decision-Making (MCDM) methodologies utilizing Interval Valued Fuzzy Numbers (IVFNs) to address the complexities of decision-making under uncertainty. We introduce a structured approach that integrates traditional IVF-MCDM with a novel combined methodology incorporating artificial intelligence (AI) through neural networks. The traditional method systematically evaluates alternatives based on predefined criteria, allowing decision-makers to express preferences as ranges, thereby accommodating uncertainty. However, it may lack adaptability to dynamic changes in supplier performance. In contrast, the combined method enhances the decision-making process by dynamically adjusting criterion weights based on historical performance data, thus providing a more responsive framework. A case study on supplier selection for Saipa Group illustrates the application of both methods, revealing that the combined approach yields superior rankings and more accurate evaluations compared to the traditional method. The results demonstrate that the integration of AI not only improves the robustness of decision-making but also facilitates continuous learning from new data, ultimately leading to more informed and effective choices. This research underscores the potential of IVFNs and AI in optimizing MCDM processes, paving the way for advancements in decision-making frameworks across various fields. The findings advocate for the adoption of combined methodologies in real-world applications, highlighting their effectiveness in navigating the uncertainties inherent in complex decision-making scenarios.
This paper presents a comprehensive exploration of Multi-Criteria Decision-Making (MCDM) methodologies utilizing Interval Valued Fuzzy Numbers (IVFNs) to address the complexities of decision-making under uncertainty. We introduce a structured approach that integrates traditional IVF-MCDM with a novel combined methodology incorporating artificial intelligence (AI) through neural networks. The traditional method systematically evaluates alternatives based on predefined criteria, allowing decision-makers to express preferences as ranges, thereby accommodating uncertainty. However, it may lack adaptability to dynamic changes in supplier performance. In contrast, the combined method enhances the decision-making process by dynamically adjusting criterion weights based on historical performance data, thus providing a more responsive framework. A case study on supplier selection for Saipa Group illustrates the application of both methods, revealing that the combined approach yields superior rankings and more accurate evaluations compared to the traditional method. The results demonstrate that the integration of AI not only improves the robustness of decision-making but also facilitates continuous learning from new data, ultimately leading to more informed and effective choices. This research underscores the potential of IVFNs and AI in optimizing MCDM processes, paving the way for advancements in decision-making frameworks across various fields. The findings advocate for the adoption of combined methodologies in real-world applications, highlighting their effectiveness in navigating the uncertainties inherent in complex decision-making scenarios.
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