Optimizing Railway Train Selection in Pakistan Using Confidence-Driven Intuitionistic Fuzzy Methods with Einstein-Based Operators
Journal Title: Journal of Intelligent Management Decision - Year 2025, Vol 4, Issue 1
Abstract
The optimization of railway train selection in Pakistan has become increasingly critical due to rapid population growth and rising travel demands. Despite efforts by the Railway Transport (RT) Department to enhance efficiency, productivity, and safety through policy reforms and infrastructure advancements, persistent challenges such as outdated technology, infrastructure bottlenecks, frequent delays, and inadequate maintenance continue to hinder progress. Addressing these issues is imperative to ensuring sustainable, efficient, and resilient railway operations. Given the multifaceted and uncertain nature of railway system modeling and management, decision-making (DM) processes necessitate robust methodologies capable of handling imprecise and ambiguous data. In this study, an innovative DM framework is introduced, leveraging intuitionistic fuzzy sets (IFSs) as an advanced extension of fuzzy sets (FSs) to manage uncertainty and hesitation in complex scenarios. By employing Einstein t-norm and t-conorm-based operators, novel operational laws for intuitionistic fuzzy credibility numbers (IFCNs) are proposed. Three key aggregation techniques—Confidence Intuitionistic Fuzzy Credibility Einstein Weighted Averaging (CIFCEWA), Confidence Intuitionistic Fuzzy Credibility Einstein Ordered Weighted Averaging (CIFCEOWA), and Confidence Intuitionistic Fuzzy Credibility Einstein Hybrid Weighted Averaging (CIFCEHWA) operators—are developed to provide a structured approach for processing and analyzing intuitionistic fuzzy data. To evaluate the practical applicability and reliability of the proposed methodology, a structured DM algorithm is formulated and validated using a real-world railway train selection case study. The incorporation of confidence levels within the IFCN framework enhances DM precision by quantifying the degree of certainty, thereby reducing risk and improving reliability. The findings demonstrate that the proposed approach effectively addresses the inherent uncertainties in railway selection processes, leading to more informed and strategic planning. Furthermore, the applicability of IFCNs extends beyond railway systems, offering valuable insights for domains such as artificial intelligence, financial DM, management science, and engineering, where uncertainty plays a pivotal role.
Authors and Affiliations
Khaista Rahman, Quaid Iqbal
Keywords
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- EP ID EP769389
- DOI https://doi.org/10.56578/jimd040105
- Views 2
- Downloads 0
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