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Multi-Criteria Decision-Making (MCDM) plays a pivotal role in addressing complex decision problems with multiple conflicting criteria. Within the context of Pythagorean Fuzzy Sets (PFSs), the Pythagorean fuzzy ordered weighted averaging aggregation operator (PFOWA operator) serves as a mathematical tool for combining data from multiple sources. Here, we have also proposed Einstein-Ordered Weighted Averaging aggregation operator (EOWA operator) to solve the MCDM problem incorporating appropriate score function measure of Pythagorean Fuzzy Sets. With the use of Pythagorean Fuzzy Sets along with PFOWA and EOWA operators incorporating an appropriate score function, this research investigates the application of multi-criteria decision-making within IT project management. The proposed operators aim to address the inherent uncertainties and complexities associated with IT project environments by leveraging Pythagorean Fuzzy Numbers. Through the integration of a suitable score function, the PFOWA and EOWA operators allow for a nuanced consideration of the importance and ranking of criteria, contributing to a more refined decision-making process. The study explores the practical application of these two operators in IT project management, providing insights into their effectiveness in handling diverse and conflicting criteria. The findings contribute to the advancement of decision support systems in IT project management, offering a promising avenue for enhancing the precision and adaptability of decision-making processes in this dynamic field. Here, we have determined the more suitable team based on a few criteria in the current competitive environment in the IT industry with the help of appropriate scoring measures for Pythagorean fuzzy sets using the aggregation approach. Subsequently, a numerical example has been considered to assess the performance of the scoring function that was enriched with Pythagorean Fuzzy Set Measures across a range of multi-criteria decision scenarios. Finally, the experimental results of the study have been presented.

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Pythagorean Fuzzy Ordered Weighted Averaging Aggregation Operator Based on Appropriate Score Function and Their Application to Multi-criteria Decision-Making in IT Project Management

  • 章节

  • 发表日期：

    2025年

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  • Jogjiban Chakraborty,
  • Sathi Mukherjee,
  • Laxminarayan Sahoo

摘要

Multi-Criteria Decision-Making (MCDM) plays a pivotal role in addressing complex decision problems with multiple conflicting criteria. Within the context of Pythagorean Fuzzy Sets (PFSs), the Pythagorean fuzzy ordered weighted averaging aggregation operator (PFOWA operator) serves as a mathematical tool for combining data from multiple sources. Here, we have also proposed Einstein-Ordered Weighted Averaging aggregation operator (EOWA operator) to solve the MCDM problem incorporating appropriate score function measure of Pythagorean Fuzzy Sets. With the use of Pythagorean Fuzzy Sets along with PFOWA and EOWA operators incorporating an appropriate score function, this research investigates the application of multi-criteria decision-making within IT project management. The proposed operators aim to address the inherent uncertainties and complexities associated with IT project environments by leveraging Pythagorean Fuzzy Numbers. Through the integration of a suitable score function, the PFOWA and EOWA operators allow for a nuanced consideration of the importance and ranking of criteria, contributing to a more refined decision-making process. The study explores the practical application of these two operators in IT project management, providing insights into their effectiveness in handling diverse and conflicting criteria. The findings contribute to the advancement of decision support systems in IT project management, offering a promising avenue for enhancing the precision and adaptability of decision-making processes in this dynamic field. Here, we have determined the more suitable team based on a few criteria in the current competitive environment in the IT industry with the help of appropriate scoring measures for Pythagorean fuzzy sets using the aggregation approach. Subsequently, a numerical example has been considered to assess the performance of the scoring function that was enriched with Pythagorean Fuzzy Set Measures across a range of multi-criteria decision scenarios. Finally, the experimental results of the study have been presented.