An Intuitionistic Fuzzy Framework for Efficient Transportation Decision-Making

Abstract

Transportation decision-making involves substantial uncertainty arising from fluctuating demand, variable supply chains, unpredictable fuel costs, traffic conditions, and environmental factors. Traditional crisp optimization methods inadequately address this inherent vagueness and hesitation in real-world scenarios. This research develops an intuitionistic fuzzy framework incorporating both membership and non-membership degrees alongside hesitation margins to enhance transportation efficiency. The study applies triangular intuitionistic fuzzy numbers for cost optimization, route selection, and resource allocation across Indian transportation networks. Through systematic analysis of five major transportation corridors involving Mumbai-Pune, Delhi-Jaipur, Chennai-Bangalore, Kolkata-Bhubaneswar, and Ahmedabad-Surat routes, the framework demonstrates significant improvements in decision accuracy and cost reduction. Utilizing accuracy function-based ranking and multi-objective optimization techniques, this research addresses supply-demand balancing, vehicle allocation, and transportation cost minimization. Results indicate 18-24% cost reduction compared to conventional fuzzy approaches and 32-38% improvement over crisp methodologies. The framework provides decision-makers with flexible, robust tools for managing transportation uncertainties while maintaining operational efficiency and sustainability in developing economies like India.