Yard-Vehicle Routing

Optimization Focus

What is optimized, what is decided, what limits apply, and what data drive the model.

Objective

  • Minimize container transport time between quay, yard, and gate/rail points.
  • Reduce empty travel and avoid route conflicts or congestion.
  • Balance vehicle workload and manage AGV energy/charging cycles efficiently.

Decision Variables

  • Assignment of transport jobs (quay↔yard, yard↔gate/rail) to specific vehicles.
  • Conflict-free paths and start/finish times per task and route segment.
  • Battery-aware dispatching and recharging schedules for automated vehicles.

Constraints

  • Conflict-free routing: no collisions, safe distance, and limited lane capacity.
  • Task release times, crane precedence, and time windows for synchronization.
  • Vehicle kinematic limits, battery constraints, and lane direction rules.

Data Sources

  • TOS logs: QC moves, yard inputs/outputs, truck/rail arrivals, container IDs.
  • Network graph: arcs, intersections, buffer zones, and one-way lanes.
  • Telemetry: vehicle positions, speeds, energy levels, and maintenance status.

Main Assumptions

  • Tasks are known over a rolling horizon; re-optimization occurs as new events arrive.
  • Lane capacities, intersections, and turning rules ensure conflict-free traffic.
  • Service times are estimated or data-driven; travel times may depend on congestion.
  • Integration with crane and gate scheduling improves overall terminal coordination.

Modeling Approaches

YVRP has been modeled using Mixed-Integer Linear Programming (MILP), Constraint Programming (CP), simulation-optimization, and deep reinforcement learning for adaptive routing.

  • Exact models: MILP or CP on time-expanded networks for job and path allocation.
  • Heuristics: Hybrid assignment-routing models for real-time and large-scale operations.
  • Learning-based: DRL/DQN agents for dynamic AGV control and congestion management.
  • Integrated: Joint optimization with quay and yard cranes to improve throughput and reduce idle time.

Reference Studies

Key academic and applied works on yard-vehicle routing and dispatching.

🧭 Real-World Datasets

DatasetDescription
Container-Terminal Event Logs Rich event logs (months) with timestamps and yard fields (e.g., block/slot). Ideal to extract quay-yard pickup/delivery jobs, dwell times, and service windows for YVRP.
Vessel Visit API – Portbase (Rotterdam) TProduction API for vessel-level port-call data; use ETAs/ETDs and terminal planning to build time-phased yard-move demand aligned to berth plans (Registration required).
ETA Terminal Open-Data API – Port of Antwerp-Bruges (NxtPort) Open-data API exposing vessel ETA and cargo opening times per terminal. Handy to derive realistic release windows for yard jobs (Free key via NxtPort procedure.)
MyTerminal / Terminal APIs – ECT (Rotterdam) Commercial APIs (e.g., Quay Planning, Object Status) providing real-time vessel status, planning, and object events. Useful to reconstruct intra-terminal transport demand.
Premium Terminal Data via Portbase – RWG/APMT/ECT Feed of terminal ETAs/ETDs and discharge info via Portbase Cargo Controller. Ssupports high-fidelity demand timelines for yard moves.

⚙️ Synthetic Benchmark Instances

DatasetDescription
Multi-YC Instances Ready-made instance files (multi-crane, multi-block; ranging between 6–12 tasks) suitable to synthesize YVRP job lists between quay bays and yard blocks.
Bulk-Port Routing Models Routing datasets and published solutions for intra-port transport. Map directly to yard-tractor networks with node/OD definitions.
Dynamic PDPTW Dynamic pickup-and-delivery instances for live re-planning. Ideal for testing online dispatch heuristics in dynamic yard environments.
DVRP Kilby Set Classic dynamic VRP instances with request arrival times. Useful for evaluating event-driven yard-vehicle dispatching..

While few public datasets exist due to terminal confidentiality, academic benchmarks reflect realistic layouts and traffic dynamics.