A Review of Modeling and Simulation Research for Coal Supply Chains Based on Multi-Method Integration: Methods, Applications, and Development Trends

doi:10.11871/jfdc.issn.2096-742X.2026.04.015

Abstract

Abstract:

[Objective] Against the backdrop of global energy transition and supply chain complexity, the coal industry is facing multiple challenges, including efficiency improvement, cost optimization, and green development. This paper aims to systematically analyze the research status and development trends of coal supply chain simulation technology, providing theoretical support for the intelligent upgrading of the industry. [Methods] By systematically reviewing methods such as discrete event simulation, system dynamics, and multi-agent simulation, combined with an analysis of typical application scenarios, the applicability and effectiveness of various simulation technologies in the coal supply chain are evaluated. [Results] Research indicates that simulation technology can significantly enhance decision-making in areas such as transportation scheduling, inventory management, and energy conservation and emission reduction. However, limitations exist, including insufficient integration of multiple methods, difficulties in data integration, and a lack of standardization. [Conclusion] Future efforts should focus on strengthening research on digital twins, artificial intelligence, and multi-method collaborative simulation. By developing standardized models and enhancing data governance, the coal supply chain can be propelled toward intelligent, green, and integrated development.

Key words: coal supply chain simulation technology, system dynamics, discrete event simulation, multi-agent simulation, intelligent optimization, digital twin

GENG Hua, WANG Lina, GAO Manda, WANG Wenbin, LI Yawei, YOU Bo. A Review of Modeling and Simulation Research for Coal Supply Chains Based on Multi-Method Integration: Methods, Applications, and Development Trends[J]. Frontiers of Data and Computing, 2026, 8(4): 203-213, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2026.04.015.

Figures/Tables 2

References 29

[1]	宋彧, 李巍巍, 金飞. 煤炭供应链协同管理的策略研究[J]. 煤炭技术, 2011, 30(10): 273-274.
[2]	LAW A M, KELTON W D. Simulation modeling and analysis[M]. New York: Mcgraw-hill, 2007:125-127.
[3]	BANKS J. Discrete event system simulation[M]. Pearson Education India, 2005: 30-32.
[4]	ZUPICK N, SADOWSKI R, KELTON W D. Simulation with arena[M]. 2014: 56-58.
[5]	SIER D, ERNST A, MARQUEZ L. Rail Capacity Expansion in the Coal Supply Chain: A Simulation Study[C]. IFORS, 2008: 1-5.
[6]	WIJAYA H, RIDWAN A Y, SETYAWAN E B. Designing simulation model for minimizing coal train unloading time: A case study of Kereta Api Logistik Company[C]. IOP Conference Series: Materials Science and Engineering, IOP Publishing, 2022, 1212(1): 012049.
[7]	刘金鹏, 张洋. 基于witness煤炭码头的物流系统仿真及评价[J]. 中小企业管理与科技(上旬刊), 2012, (3): 286-287.
[8]	隆双双. 基于AnyLogic的采选充一体化煤矿生产物流系统仿真研究[D]. 安徽理工大学, 2023.
[9]	STERMAN J. System Dynamics: systems thinking and modeling for a complex world[J]. Engineering Systems Division, 2002, (4): 3-4.
[10]	贺勇, 卫岂伦, 廖诺. 基于系统动力学的电煤供应链节能减排路径仿真分析[J]. 工业工程, 2022, 25(3): 39-46+94. doi: 10.3969/j.issn.1007-7375.2022.03.005
[11]	高太光, 王奎, 梅亚丽, 等. 基于系统动力学的煤炭供应链碳减排研究[J]. 科技与经济, 2022, 35(4): 86-90.
[12]	XU J, LI X. Using system dynamics for simulation and optimization of one coal industry system under fuzzy environment[J]. Expert Systems with Applications, 2011, 38(9): 11552-11559. doi: 10.1016/j.eswa.2011.03.033
[13]	吴悦, 门敏刚, 何龙龙. 基于系统动力学的煤炭供应链节能减排路径分析[J]. 煤炭工程, 2025, 57(4): 202-209.
[14]	WOOLDRIDGE M. An introduction to multiagent systems[M]. John wiley & sons, 2009: 156-158.
[15]	李丹, 路世昌, 张荣兰, 等. 煤炭企业供应链优化建模的仿真分析[J]. 计算机仿真, 2013, 30(7): 204-207.
[16]	苏旭, 梁雨, 金玮楠, 等. 供应链仿真建模在煤炭企业中的应用[J]. 科技视界, 2016,(1): 69.
[17]	栾博钰, 周伟, 以斯哈, 等. 露天煤矿多干线运输系统无人卡车实时调度策略[J/OL]. 煤炭学报, 1-12[2025-11-12]. https://doi.org/10.13225/j.cnki.jccs.2025.0044.
[18]	李丹, 路世昌, 赵球, 等. 煤炭企业绿色供应链系统仿真模型构建研究[J]. 计算机仿真, 2013, 30(6): 239-243.
[19]	YU S, ZHENG S, GAO S, et al. A multi-objective decision model for investment in energy savings and emission reductions in coal mining[J]. European Journal of Operational Research, 2017, 260(1): 335-347. doi: 10.1016/j.ejor.2016.12.023
[20]	YANG Y, ZHOU Q, CHEN K. Multiagent-based modeling and simulation of a coal multimodal transport system[J]. IEEE Access, 2022, 10: 65873-65885. doi: 10.1109/ACCESS.2022.3184728
[21]	FELANI M I, NUGRAHA A D, RAHMANTA M A. Optimizing Supply Chain Management in Coal Power Generation[C]. ICORES, 2017: 460-463.
[22]	DA B, LIU C, LIU N, et al. Coal-electric power supply chain reduction and operation strategy under the cap-and-trade model and green financial background[J]. Sustainability, 2019, 11(11): 3021. doi: 10.3390/su11113021
[23]	张新华, 卢灿华, 陈志伟. 碳调度模式下火力发电商的碳减排投资策略分析[J]. 中国管理科学, 2017, 25(11): 179-188.
[24]	王芹鹏, 赵道致, 何龙飞. 供应链企业碳减排投资策略选择与行为演化研究[J]. 管理工程学报, 2014, 28(3): 181-189+180.
[25]	XU J, MA N, XIE H. Ecological coal mining based dynamic equilibrium strategy to reduce pollution emissions and energy consumption[J]. Journal of Cleaner Production, 2017, 167: 514-529. doi: 10.1016/j.jclepro.2017.08.115
[26]	HARMSE M, JANSE VAN RENSBURG S J. Capacity modelling of the coal value chain at Sasol Coal Supply[C]. MOAS, 2007: 221-226.
[27]	于华. 煤炭供应链排放测算与控制研究[D]. 北京交通大学, 2011.
[28]	姜庆国, 穆东, 王立海, 等. 电煤供应链碳排放的经济测度研究[J]. 北京交通大学学报(社会科学版), 2012, 11(4): 45-50.
[29]	琚俊梅. 基于规范和多Agent的煤炭供应链建模与仿真研究[D]. 宁夏大学, 2014.

仿真方法	建模视角	适用场景	优势特点	局限性	典型应用案例
离散事件仿真	过程导向	物流操作优化	精细化过程描述	宏观行为支持有限	港口操作、铁路调度
系统动力学	系统导向	长期趋势分析	处理非线性关系	微观交互描述不足	节能减排策略
多智能体仿真	主体导向	协调机制设计	描述个体异质性	建模复杂度高	供应链优化

仿真平台	主要建模方法	研究内容	主要发现
Arena	离散事件仿真	煤炭列车卸货系统优化	增加1台门式起重机和5辆自卸车可使卸货时间减少30.7%
Witness	离散事件仿真	煤炭码头物流系统分析	堆场面积是制约系统发展的关键瓶颈因素
Vensim	系统动力学	电煤供应链节能减排	降低发电标准耗煤率是最有效的节能减排途径
结构方程	多方法融合	绿色供应链管理系统	引入环保和资源优化变量显著提高模型准确性