Choosing an optimal artificial intelligence (AI) provider involves multiple factors, including scalability, cost, performance, and dependability. To ensure that decisions align with organizational objectives, multi-attribute decision-making (MADM) approaches aid in the systematic evaluation and comparison of AI vendors. Therefore, in this article, we propose a MADM technique based on the framework of the complex intuitionistic fuzzy rough model. This approach effectively manages the complex truth grade and complex false grade along with lower and upper approximation. Furthermore, we introduced aggregation operators based on Dombi t-norm and t-conorm, including complex intuitionistic fuzzy rough (CIFR) Dombi weighted averaging (CIFRDWA), CIFR Dombi ordered weighted averaging (CIFRDOWA), CIFR Dombi weighted geometric (CIFRDWG), and CIFR Dombi ordered weighted geometric (CIFRDOWG) operators, which were integrated into our MADM technique. We then demonstrated the application of this technique in a case study on AI provider selection. To highlight its advantages, we compared our proposed method with other approaches, showing its superiority in handling complex decision-making scenarios.
Citation: Tahir Mahmood, Ahmad Idrees, Majed Albaity, Ubaid ur Rehman. Selection of artificial intelligence provider via multi-attribute decision-making technique under the model of complex intuitionistic fuzzy rough sets[J]. AIMS Mathematics, 2024, 9(11): 33087-33138. doi: 10.3934/math.20241581
Choosing an optimal artificial intelligence (AI) provider involves multiple factors, including scalability, cost, performance, and dependability. To ensure that decisions align with organizational objectives, multi-attribute decision-making (MADM) approaches aid in the systematic evaluation and comparison of AI vendors. Therefore, in this article, we propose a MADM technique based on the framework of the complex intuitionistic fuzzy rough model. This approach effectively manages the complex truth grade and complex false grade along with lower and upper approximation. Furthermore, we introduced aggregation operators based on Dombi t-norm and t-conorm, including complex intuitionistic fuzzy rough (CIFR) Dombi weighted averaging (CIFRDWA), CIFR Dombi ordered weighted averaging (CIFRDOWA), CIFR Dombi weighted geometric (CIFRDWG), and CIFR Dombi ordered weighted geometric (CIFRDOWG) operators, which were integrated into our MADM technique. We then demonstrated the application of this technique in a case study on AI provider selection. To highlight its advantages, we compared our proposed method with other approaches, showing its superiority in handling complex decision-making scenarios.
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Tahir Mahmood, Ahmad Idrees, Majed Albaity, Ubaid ur Rehman. Selection of artificial intelligence provider via multi-attribute decision-making technique under the model of complex intuitionistic fuzzy rough sets[J]. AIMS Mathematics, 2024, 9(11): 33087-33138. doi: 10.3934/math.20241581
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