Journal of System Simulation
Abstract
Abstract: A two-level optimization model integrating heterogeneous electric vehicle fleet configuration with multimodal path coordination is built to address the challenges in industrial cluster freight transportation involving electric heterogeneous fleet scheduling, such as diverse tasks and stringent constraints on energy consumption and delivery timeliness. Considering the performance coupling characteristics of heterogeneous electric fleets in terms of load-energy consumption-endurance, based on the coordination of multimodal paths and fleet configuration coding, the upper level employs the GWO to optimize task splitting and fleet configuration. The lower level adopts a multi-neighborhood SA algorithm to achieve path coordination scheduling. Simulation results show that the coordinated optimization of heterogeneous fleets and multimodal paths reduces the comprehensive transportation cost by 26.4%, the travel distance by 7%, and the number of configured vehicles by 16.7%, with significantly improved fleet timeliness and scheduling reliability. A generalizable technical path is provided for closed-loop freight transportation of industrial clusters.
Recommended Citation
Xiao, Renxin; Xu, Yifan; Chen, Guisheng; and Pan, Nan
(2026)
"Coordinated Optimization of Heterogeneous Fleet Configuration and Multimodal Routing for Industrial Clusters,"
Journal of System Simulation: Vol. 38:
Iss.
10, Article 14.
DOI: 10.16182/j.issn1004731x.joss.25-1148
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol38/iss10/14
First Page
2997
Last Page
3014
CLC
TP391.9
Recommended Citation
Xiao Renxin, Xu Yifan, Chen Guisheng, et al. Coordinated Optimization of Heterogeneous Fleet Configuration and Multimodal Routing for Industrial Clusters[J]. Journal of System Simulation, 2026, 38(10): 2997-3014.
DOI
10.16182/j.issn1004731x.joss.25-1148
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