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.
Scheduling Focus
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.
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.
Modeling Approaches
The Truck Appointment Scheduling Problem has been modeled through queuing theory, simulation, and mathematical programming. Modern systems combine predictive analytics with real-time rescheduling.
- Exact models (MILP): optimize slot assignment, quota setting, and tardiness penalties [Zhao & Goodchild 2013].
- Simulation & queuing: evaluate system performance under stochastic arrivals and variable service times.
- Heuristics/metaheuristics: GA, tabu search, and adaptive rescheduling for large-scale or uncertain environments.
Reference Studies
🧭 Academic Studies
| 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). |
⚙️ Applied & Industry Cases
| 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.