A Framework for Low-Carbon Container Multimodal Transport Route Optimization Under Hybrid Uncertainty: Model and Case Study

To enhance the operational efficiency of container multimodal transportation and mitigate carbon emissions during freight transit, this study investigates carbon emission-conscious multimodal transportation route optimization models and solution methodologies. Addressing the path optimization challe...

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Bibliographic Details
Published in:Applied sciences Vol. 15; no. 12; p. 6894
Main Authors: Feng, Fenling, Zheng, Fanjian, Zhang, Ze, Wang, Lei
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.06.2025
Subjects:
Air quality management
Algorithms
Analysis
Carbon
Case studies
Costs
Decision-making
Efficiency
Emissions (Pollution)
Fuzzy sets
Genetic algorithms
Logistics
Mathematical optimization
multimodal transport
Optimization algorithms
Pareto optimum
path optimization
Risk assessment
Route optimization
Sustainable development
Taxation
Transportation services
uncertainty
value and time attributes
whale optimization algorithm
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Summary:To enhance the operational efficiency of container multimodal transportation and mitigate carbon emissions during freight transit, this study investigates carbon emission-conscious multimodal transportation route optimization models and solution methodologies. Addressing the path optimization challenges under uncertain conditions, triangular fuzzy numbers are employed to characterize transportation time uncertainty, while a scenario-based robust regret model is formulated to address freight price volatility. Concurrently, the temporal value attributes of cargo are incorporated by transforming transportation duration into temporal costs within the model framework. Through the implementation of four distinct low-carbon policies, carbon emissions are either converted into cost metrics or established as constraint parameters, thereby constructing an optimization model with total cost minimization as the objective function. For model resolution, fuzzy chance-constrained programming is adopted for defuzzification processing. Subsequently, a multi-strategy improved whale optimization algorithm (WOA) is developed to solve the formulated model. Numerical case studies are conducted to validate the proposed methodology through comparative analysis with conventional WOA implementations, demonstrating the algorithm’s enhanced computational efficiency. The experimental results confirm the model’s capability to adapt multimodal transportation schedules for cargo with varying temporal value attributes and effectively reduce CO2 emissions under different carbon reduction policies. This research establishes a comprehensive decision-making framework that provides logistics enterprises with a valuable reference for optimizing low-carbon multimodal transportation operations.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app15126894