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Abstract

Multi-modal transportation systems are the logistics networks for global economy. Transportation systems are fraught with uncertainties that hinder the traditional deterministic models reaching the optimal performance. The main obstacle for traditional deterministic models is the uncertainties (e.g., fuel prices volatility, inaccurate transit-times prediction, and evolving environmental regulations). This paper proposes a novel method of fuzzy multi-objective defuzzification. It integrates a modified center of gravity (COG) technique with multi-objective linear programming (MOLP) to address the uncertainty challenges. Triangular fuzzy numbers and partitioning to sud-intervals generated crisp solutions to balance conflicting objectives: cost, time, and environmental sustainability. A four transportation-mode used as a case study, achieving 7.5% cost reduction and 9.2% emission reductions. Analysing sensitivity, 15% increase in air freight allocations occurred by prioritizing time and 20% of shipments to rail and sea occurred by emphasizing sustainability shifts. The robustness of the modified method is highlighted by handling imprecise data and dynamic priorities. Further, a scalable framework for sustainable logistics is aligned with global climate action goals.

Keywords

Multi-modal transportation, Fuzzy optimization, Defuzzification, Center of gravity (COG), Triangular fuzzy numbers, Uncertainty, Sustainability

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