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Published on 15 May 2025
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Jiang,J.;Wang,Z. (2025). Path Selection of Multimodal Transport for Coal Based on Economic and Environmental Benefits. Theoretical and Natural Science,109,18-23.
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Path Selection of Multimodal Transport for Coal Based on Economic and Environmental Benefits

Jiong Jiang *,1, Zhihuan Wang 2
  • 1 Institute of Logistics Science and Engineering, Shanghai Maritime University, Shang Hai, China
  • 2 Institute of Logistics Science and Engineering, Shanghai Maritime University, Shang Hai, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2753-8818/2025.GL22951

Abstract

This study investigates the coal multimodal transportation route selection problem by integrating economic and environmental benefits, taking the current coal transportation landscape in China as its research context. Focusing on the transportation network from Shanxi region to Shanghai as a case study, we establish a multi-objective optimization model that comprehensively considers transportation costs, carbon emission costs, and transit time. The NSGA-II algorithm is employed to solve this optimization problem. The results demonstrate that rail-water intermodal transport emerges as an effective solution for optimizing transportation structures. Case analysis reveals that optimal route selections exhibit cost sensitivity, adapting to fluctuations in railway and waterway freight rates. Furthermore, sensitivity analysis indicates that carbon tax rate variations exert limited impact on total transportation costs, while conventional transportation costs maintain their dominant influence. This research provides decision support for sustainable transportation planning in coal logistics systems.

Keywords

coal transportation, multimodal transport, route selection, carbon emissions, genetic algorithm

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Cite this article

Jiang,J.;Wang,Z. (2025). Path Selection of Multimodal Transport for Coal Based on Economic and Environmental Benefits. Theoretical and Natural Science,109,18-23.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of CONF-MPCS 2025 Symposium: Leveraging EVs and Machine Learning for Sustainable Energy Demand Management

ISBN:978-1-80590-103-7(Print) / 978-1-80590-104-4(Online)
Conference date: 16 May 2025
Editor:Anil Fernando, Mustafa Istanbullu
Series: Theoretical and Natural Science
Volume number: Vol.109
ISSN:2753-8818(Print) / 2753-8826(Online)

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