| --- |
| library_name: sklearn |
| tags: |
| - logistics |
| - xgboost |
| - route-optimization |
| - sklearn |
| - tabular-regression |
| - workload |
| - fairrelay |
| datasets: |
| - Cainiao-AI/LaDe-D |
| - electricsheepafrica/africa-synth-retail-and-ecommerce-last-mile-delivery-data-nigeria |
| license: mit |
| --- |
| |
| # FairRelay — Workload Scoring Model |
|
|
| Part of the **[FairRelay](https://github.com/MUTHUKUMARAN-K-1/FairRelay)** AI logistics platform. |
|
|
| ## Model Description |
|
|
| Workload Scoring Model |
|
|
| **Type**: XGBRegressor Pipeline |
| **Framework**: scikit-learn Pipeline + XGBoost |
| **Task**: Regression |
|
|
| ## Performance |
|
|
| - **R²**: 0.9969 |
| - **MAE**: 4.2338 |
| - **RMSE**: 5.3418 |
| - **CV R² (5-fold)**: 0.9969 ± 0.0001 |
|
|
|
|
| ## Input Features |
|
|
| | Feature | Importance | |
| |---------|-----------| |
| | `num_packages` | 0.0205 | |
| | `total_weight_kg` | 0.0118 | |
| | `num_stops` | 0.3539 | |
| | `avg_fragility` | 0.0035 | |
| | `total_distance_km` | 0.0008 | |
| | `route_difficulty_score` | 0.5454 | |
| | `estimated_time_minutes` | 0.0640 | |
|
|
|
|
| ## Usage |
|
|
| ```python |
| from skops import io as sio |
| from huggingface_hub import hf_hub_download |
| |
| # Download and load |
| model_path = hf_hub_download(repo_id="muthuk1/fairrelay-workload-scoring", filename="model.skops") |
| untrusted = sio.get_untrusted_types(file=model_path) |
| model = sio.load(model_path, trusted=untrusted) |
| |
| # Predict |
| import numpy as np |
| features = np.array([[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]) |
| prediction = model.predict(features) |
| ``` |
|
|
| ## Training Data |
|
|
| - **Cainiao-AI/LaDe-D**: Real last-mile delivery data from Shanghai (KDD 2023) |
| - **Africa Synth Last-Mile**: Synthetic Nigerian delivery data |
| - **FairRelay Synthetic**: Physics-informed synthetic data calibrated to FairRelay's deterministic formulas |
|
|
| ## Part of FairRelay |
|
|
| FairRelay is an AI-powered logistics platform for fair load consolidation and dispatch: |
| - 🚚 5-agent load consolidation pipeline (KMeans + OR-Tools CP-SAT) |
| - ⚖️ 8-agent fair dispatch pipeline (Gini optimization) |
| - 📊 XGBoost effort prediction + Thompson Sampling bandit |
| - 🌱 EV-aware routing with battery constraints |
|
|
| Built for **LogisticsNow Hackathon 2026** — Challenge #5: AI Load Consolidation |
|
|
| ## License |
|
|
| MIT |
|
|
