Port-Call Optimization Data-Hub
Truck-Appointment Scheduling Problem
The Truck-Appointment Scheduling Problem (TASP) coordinates how trucking companies and container terminals allocate gate time-slots for pickups and drop-offs. Appointments must respect capacity quotas, yard and gate constraints, and real-time arrival uncertainty. Well-designed systems reduce truck waiting, gate congestion, and idling emissions while improving resource utilization.
Optimization Focus
What is optimized, which decisions are made, and what data drive the system.
Objective
- Minimize truck turnaround and queueing times at the terminal gate and yard.
- Balance truck arrivals to prevent peaks and congestion at yard blocks.
- Lower fuel consumption and emissions from idling vehicles.
Decision Variables
- Assignment of appointment slots to truck requests (import/export).
- Number of slots per window, overbooking margins, and buffer allocation.
- Dynamic adjustment based on traffic and arrival delays.
Constraints
- Maximum truck quota per time window (gate capacity constraint).
- Lead-time and punctuality enforcement (arrival within slot window).
- Penalties for late/early arrivals, no-shows, and buffer capacity violations.
Data Sources
- Terminal gate logs: truck entry, service, and exit times.
- Drayage fleet ETA predictions and no-show statistics.
- Historical container movements, yard availability, and traffic data.
Related Port-Call Processes
Processes related to truck-appointment scheduling — click a card to open the process page.
Click any card to open the process page related to the optimization problem.
Terminal Operations
- Procedures and resource deployment on terminals that influence gate throughput and appointments.
Customs
- Clearance and documentation steps that affect gate release and container handover timing.
Hinterland Transport
- Modal scheduling and terminal-to-hinterland coordination that influence slot timing and throughput.
Main Assumptions
- Slots are discrete (e.g., 15–30 min) and capacity-limited.
- Truck arrival deviations (early/late) are penalized or buffered.
- Bookings made in advance; emergency trucks managed via walk-in slots.
- Service durations and traffic delays estimated from historical data.
Available Datasets
Academic and applied references on truck appointment systems.
🧭 Real-World Datasets
| Study | Findings |
|---|---|
| TAS with Impact to Drayage Truck Tours | Input instances for the experiments in the TAS study that couples appointments with truck tour planning. Good for integrated TAS–drayage benchmarking. |
| Truck Appointments per Time Slot | Input data used to size truck appointments per time slot (i.e., quota planning). Useful to build appointment-quota instances and validate TAS models with terminal-style parameters. |
| Real Terminal Event Logs | The user can derive gate arrivals, yard service, and departure patterns to build TAS instances and validate predictions of no-shows/late shows. |
| APM Terminals – Truck Appointments API | Production API that connects directly to the truck appointment system at participating APMT terminals. Useful for live/time-series experiments (requires registration). |
| Port of Virginia – Events/API | Subscribable operational events (gates/appointments & statuses) via API. Useful to create rolling TAS datasets from a real port (requires registration). |
⚙️ Synthetic Benchmark Instances
| Case | Description |
|---|---|
| BRPAS Instances | Synthetic instances with yard relocations and appointment windows. Handy for TAS integrated with yard effects. |
| PDPTW Instances | 300 synthetic time-window instances ranging between 100–5000 customers. |
| Real-time Truck Scheduling in Cross-Docking | Multiple synthetic instances for truck-to-dock scheduling. Useful to test TAS algorithms’ queuing / slot-fill logic independent of port specifics. |
Most real TAS datasets are proprietary, but benchmark models consistently demonstrate reductions in average waiting time and CO₂ emissions.