
Path Selection of Multimodal Transport for Coal Based on Economic and Environmental Benefits
- 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.
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
[1]. Zhang M.Optimization of Multimodal Transport Routes ConsideringCarbon Emissions in Fuzzy Scenarios [J].International Core Journal of Engineering,2022,8(4):267-281.
[2]. Zhang H,Huang Q,Ma L,et al.Sparrow search algorithm with adaptivet distribution for multi-objective low-carbon multimodal transportationplanning problem with fuzzy demand and fuzzy time [J].Expert Systemswith Applications,2024,238:122042.
[3]. Zheng C,Sun K,Gu Y,et al.Multimodal Transport Path Selection of Cold Chain Logistics Based on Improved Particle Swarm Optimization Algorithm[J].Journal of Advanced Transportation,2022(Pt.7):1.1-1.12.
[4]. Rademeyer M, Minni Tt R, Falcon R. Multi-product coal distribution and price discovery for the domestic market via mathematical optimisation[J]. Mineral Economics, 2020, 34: 113-126.
[5]. Botha A, Badenhorst-Weiss J A. Risk management in a bulk coal export logistic chain: A stakeholder perspective[J]. Journal of transport and supply chain management, 2019, 13: 316-324.
[6]. Biswal J N, Muduli K, Satapathy S, et al. A TISM based study of SSCM enablers: an Indian coal-fired thermal power plant perspective[J]. International Journal of System Assurance Engineering and Management, 2019, 10(1): 126-141.
[7]. Mitra S, Avittathur B. Application of linear programming in optimizing the procurement and movement of coal for an Indian coal-fired power-generating company[J]. DECISION, 2018, 45(3): 207–224.
[8]. Rosyid F A, Adachi T. Optimization on long term supply allocation of Indonesian coal to domestic market[J]. Energy Systems, 2018, 9(2): 385–414.
[9]. Lv H D, Zhou J S, Yang L, et al. An accounting of the external environmentalcosts of coal in Inner Mongolia using the pollution damage method[J]. Environment, Development and Sustainability, 2020, 22(2): 1299-1321.
[10]. Li H., Su L. Multimodal transport path optimization model and algorithm considering car bon emission multitask[J]. The Journal of Supereomputing, 2020, 76(12):9355-9373
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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Volume title: Proceedings of CONF-MPCS 2025 Symposium: Leveraging EVs and Machine Learning for Sustainable Energy Demand Management
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