.png){kind=icon}
.png){kind=icon}
Return
Structure-optimized deep forest model for railway port container reloading time prediction: A hybrid integer programming and Bayesian optimization approach
.png){kind=icon}
OA
AI
DOI：10.1016/j.aei.2026.104309.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• Design an IBDF model for complex datasets in container reloading time prediction • Develop a combinatorial optimization algorithm to find optimal forest learner types. • Propose a BO method to obtain the optimal number of forest learners per type selected. • Validate the IBDF model with actual data from the China Railway Express trains. • Establish an AI-powered analytics tool for improving data-driven decision-making
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.9
Papers:
4.0K
Citations:
1.7W
View more organizations