Structure-optimized deep forest model for railway port container reloading time prediction: A hybrid integer programming and Bayesian optimization approach

Guo, Jingwei; Wang, Yimin; Guo, Xiang; Guo, Jiayi; D’Ariano, Andrea; Bosi, Tommaso; Zhang, Yongxiang

doi:10.1016/j.aei.2026.104309

articleAdvanced Engineering InformaticsJan 6, 2026HYBRID OA

Structure-optimized deep forest model for railway port container reloading time prediction: A hybrid integer programming and Bayesian optimization approach

JGJingwei Guo YWYimin Wang XGXiang Guo JGJiayi Guo ADAndrea D’Ariano

Zhengzhou Railway Vocational & Technical College · City University of Macau · +3 more institutions

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Abstract

Container reloading in international railway transport involves transferring containers to trains compatible with the destination’s railway gauge. The duration depends on factors like port facilities, staff proficiency, and customs clearance. Accurate forecasting is crucial for efficient planning and railway efficiency, including predicting train travel times and informing consignment customers about arrivals. However, current prediction methods cannot handle fluctuating nonlinear container reloading times and are too subjective in forest learner selection. Therefore, this paper proposes a novel structure-optimized deep learning model named the intelligent Bayesian deep forest (IBDF) model, which combines the…

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Topics

Keywords

Container (type theory)
Integer programming
Benchmark (surveying)
Train
Heuristics
Port (circuit theory)
Hyperparameter
Random forest

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