.png){kind=icon}
.png){kind=icon}
Return
Preference-agile multi-objective optimization for real-time vehicle dispatching
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ejor.2026.04.017.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• An end-to-end Preference-Agile Multi-Objective Optimization (PAMOO) is proposed. • A novel network architecture is tailored with enhanced generalization. • The proposed method facilitates dynamic and interactive preference adjustment in an online setting. • The method marks the first Dynamic Multi-Objective Reinforcement Learning for port operations.
Keywords:
Transportation
Dynamic vehicle routing
Digit port
Multi-objective optimization
Deep reinforcement learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W