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This paper introduces an automated recommendation system for Sea-River-Inland Waterway Intermodal Transport (SRIIT) that optimizes dry bulk cargo transport by considering port capacity, path throughput, and time windows. A dual-mode scheduling algorithm (fair/priority-based) resolves resource competition using throughput-aware simulation and daily resource recovery. The system, validated on Yancheng's network, demonstrates reduced transport time, prioritized delivery during congestion, and customizable cost-time-carbon balancing.
Stop manually planning your intermodal transport: this system cuts transport time, prioritizes sensitive cargo, and reduces cost/carbon emissions in real-world tests.
This paper proposes an automated recommendation technology for optimizing Sea-River-Inland Waterway Intermodal Transport (SRIIT) of dry bulk cargo in Yancheng, addressing critical challenges in manual planning efficiency, multi-objective trade-offs, and complex operational constraints. Our constraint-aware model integrates port capacity limitations, path throughput dynamics, and time windows, while our novel dual-mode scheduling algorithm (fair/priority-based) resolves resource competition through throughput-aware simulation and daily resource recovery. The weight-driven optimization framework enables customizable cost-time-carbon balancing via min-max normalized scoring, adapting to shipper preferences. Validated using Yancheng's real-world network with 24,000-ton cargo orders, the system achieves: 1) reduction in transport time; 2) earlier delivery for sensitive cargo (e.g., grain) under priority scheduling during congestion; and 3) cost and carbon reduction for critical orders. This work provides actionable intelligence for sustainable logistics, supporting digital-green transformation of inland-coastal intermodal networks in the Yangtze River Delta.
Tsinghua AI