Typical situation: a dispatcher manually distributes 50 orders across 15 trucks, spending 3–4 hours a day. The result is 20% underutilized cargo volume, extra trips, and conflicts with drivers due to incorrect unloading sequence. Ultimately, the company overspends up to 20% of the transport budget. An AI-based loading optimization system solves these problems in seconds, reducing the number of trips by 12–20% and completely eliminating stowage errors. Average savings for our clients — 15% of annual logistics costs (typically $30,000–$50,000 per year for a fleet of 20 trucks).
Our AI engineering team has extensive experience and over 50 implemented projects in logistics. We develop a turnkey system: from 3D bin packing algorithms to TMS integration. We will evaluate your project for free — contact us.
Loading a vehicle is a 3D Bin Packing Problem with real-world constraints: axle weight, fragile-on-top, unloading sequence (LIFO), temperature zones. Classic heuristics like First Fit Decreasing get stuck in local optima. We apply Reinforcement Learning (PPO) and Graph Neural Networks.
Case study: For a client with a fleet of 50 refrigerators, the system recalculated the loading plan in 2–3 seconds instead of 4 hours of manual work. Cargo damage decreased by 35%, and underutilization dropped from 20% to 5%. Savings on fuel and depreciation amounted to about 15% of the annual budget — head of the client's transport department.
Code Example
from py3dbp import Packer, Bin, Item def optimize_load(orders, vehicle_dimensions, sequence_required=True): """ 3D bin packing with unloading sequence consideration. orders: list of {'id', 'dims': (l,w,h), 'weight', 'delivery_seq', 'fragile'} """ packer = Packer() l, w, h = vehicle_dimensions packer.add_bin(Bin('truck', l, w, h, max_weight=20000)) if sequence_required: orders_sorted = sorted(orders, key=lambda x: -x['delivery_seq']) else: orders_sorted = orders for order in orders_sorted: allow_rotation = not order.get('fragile', False) packer.add_item(Item( order['id'], order['dims'][0], order['dims'][1], order['dims'][2], order['weight'] )) packer.pack(bigger_first=True, distribute_items=False) return packer.bins[0] ML improvements:
- Reinforcement Learning (PPO, SAC) — stowage policy that avoids local optima.
- Graph Neural Network — container as a graph of placed objects, predicting the best next position.
Why Reinforcement Learning outperforms heuristics by 15–20% in packing density
Heuristics like First Fit Decreasing deliver acceptable solutions in seconds but lose to RL algorithms by 15–20% in packing density on average. An RL agent learns on simulations with thousands of loading variations, accounting for unloading order and fragility. Unlike heuristics, it adapts to your specific cargo. Compare: with 50 orders, heuristics fill 78% of volume, RL — 93%. Our AI system reduces trips by 12–20% compared to manual planning and achieves 93% packing density versus 78% with traditional heuristics, a 15% improvement.
| Method | Packing density | Calculation time | Adaptation to constraints |
|---|---|---|---|
| First Fit Decreasing | 75–80% | < 1 sec | No |
| Reinforcement Learning (PPO) | 90–95% | 2–3 sec | Yes |
| Graph Neural Network + RL | 93–98% | 3–5 sec | Yes (considers geometry) |
Consolidating LTL into FTL using ML clustering and forecast
Less-than-Truckload shipments are merged into Full-Truckload via DBSCAN clustering by direction and deadlines. An ML model predicts the emergence of companion cargo within 24–48 hours. Decision: form a trip now or wait. The algorithm decides based on a fill threshold: if projected fill exceeds 80% — ship, otherwise defer.
Step-by-step implementation plan for AI loading optimization
- Audit of data and processes — collect historical orders, vehicle parameters, constraints. Build a simulation environment.
- Develop 3D packing + RL — train the model on your data, validate on a holdout set.
- LTL/FTL consolidation module — clustering and ML forecast for companion cargo.
- Integration with TMS via REST API — automatic transfer of loading plans.
- Testing and personnel training — pilot launch, adjustments, workshops.
What's included in the work?
| Stage | Result |
|---|---|
| Audit of data and processes | Report with current loading metrics |
| Development of 3D packing + RL | Model with ≥95% accuracy vs optimal |
| Consolidation module | Route and decision optimizer |
| Integration with TMS (REST/EDI) | Loading plans delivered to drivers |
| AR instructions (optional) | App for loaders |
| Personnel training | Documentation, 2-day workshops |
| 3-month support | Algorithm performance guarantee |
Timeline and cost
Development of the basic system (3D packing + consolidation) — from 2 to 3 months. Full functionality with ML forecast and AR — up to 5 months. Cost is determined individually after the audit. A pilot study costs $5,000 and is deducted from the full implementation. We will evaluate your project within 2 business days — contact us.
Why choose us?
- Deep expertise in AI/ML for logistics — 10+ years of experience, 50+ projects delivered, 5 years on the market.
- Proven approaches: 3D Bin Packing Problem, Reinforcement Learning.
- Result guarantee on historical data — we run a pilot before launch.
- Average client savings — 15% of transport costs.
Have questions? Get a consultation with our AI engineer — we will detail the specifics for your task.