Optimizing multi-type demand-driven Ro-Ro fleet scheduling in high-density maritime corridors

Island-land roll-on/roll-off (Ro-Ro) transportation is critical component of regional logistics. However, volatile multi-type demands for trucks, cars, and passengers poses scheduling challenges, causing capacity mismatches and resource redundancy. To address these inefficiencies, this study proposes a demand-driven scheduling optimization framework. A nonlinear mixed-integer programming model is developed to maximize vehicle capacity utilization, incorporating a penalty method to pragmatically manage demand fulfillment. A customized adaptive large neighborhood search algorithm is designed to solve the large-scale problem. The framework’s effectiveness is validated using real-world operational data from the Qiongzhou Strait, a high-density maritime corridor. The proposed ALNS-based approach achieves a 91.95 % capacity utilization rate, significantly outperforming benchmark heuristics while maintaining high demand fulfillment. Furthermore, analysis of an embedded elastic capacity coefficient reveals its strategic function in enabling trade-offs between operational efficiency and service robustness across different planning periods. The proposed framework resolves critical scheduling imbalances and provides port authorities with a quantitative tool for enhancing operational efficiency and sustainability in Ro-Ro networks.