Datasets for Learning Assisted Hybrid Simulation Optimization Model

Disruptions and uncertainties can significantly reduce the efficiency of conventional intermodal transport, often leading to severe economic losses and deterioration in service levels. To mitigate the negative impacts of disruptions on the shipments, our research leverages the flexibility of synchromodality and develops a learning-based modular framework for disruption management. By utilizing a hybrid simulation-optimization modeling approach, the framework effectively captures disruptions and generates dynamic response strategies. Through the integration of Reinforcement Learning (RL), the proposed approach replans under disruptions, accounting for their stochastic characteristics and enabling swift, effective decision-making in real-time scenarios.Results are compared against a benchmark policy and an alternative reward mechanism, demonstrating that integrating RL into a synchromodal framework increases its resilience and results in lower costs compared to the benchmark policies across different disruption scenarios.