GAGE-Q: reinforced genetic algorithm using spatial neighborhood graph embedding for green intermodal transportation
Intermodal transportation, using multiple modes in a single journey, promotes sustainable logistics. This paper addresses the Intermodal Vehicle Routing Problem and proposes GAGE-Q, which integrates graph embedding and Reinforcement Learning into a Genetic Algorithm. Our method models cities and their multi-modal connections as a graph, leveraging embedding for spatial dependencies and RL-based crossover for faster convergence and better solutions. Experiments on synthetic and real-world data show that GAGE-Q outperforms state-of-the-art methods, offering improved solution quality and efficiency in intermodal route planning.
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