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fairrelay-consolidation-v2
lordvisorad/fairrelay-consolidation-v2
fairrelay-consolidation-v2 is a machine learning model from lordvisorad. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Advanced AI + Optimization Logistics Consolidation Engine for the FairRelay platform.
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Updated May 21, 2026
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At a glance
- Access
- Public
- Created
- May 16, 2026
- Updated
- May 21, 2026
- SHA
- 02e261bb
Model card
From the Hugging Face model README
FairRelay Consolidation Engine V2
Advanced AI + Optimization Logistics Consolidation Engine for the FairRelay platform.
Architecture
Ingest → Validate → Feature Engineering → Compatibility Graph →
AI Scoring → Constraint Clustering → Hybrid Optimization →
3D Loading Check → Simulation → Explainability → Feedback Learning
Modules
| Module | Description |
|---|---|
schemas.py | Complete data models (Shipment, Vehicle, Group, LoadPlan, Run, Explanation) |
validation/ | Input validation (coordinates, weights, time windows, cargo rules) |
feature_engineering/ | Pairwise features (route overlap, time overlap, cargo compat, capacity fit) |
graph_builder/ | Compatibility graph with dense subgraph detection |
clustering/ | Constraint-aware clustering (respects capacity, cargo rules) |
optimizer/ | Hybrid solver: CP-SAT (exact) + Greedy + Local Search |
loading/ | 3D loading planner (dimensions, stacking, fragility, unloading sequence) |
explainability/ | Why grouped? Why rejected? What constraint? With suggestions |
simulation/ | Multi-scenario comparison + rolling-horizon replanning |
feedback_learning/ | Operator feedback → parameter adaptation |
pipeline.py | Full pipeline orchestrator |
api/consolidation_v2.py | FastAPI endpoints |
Performance
- 6 shipments optimized in 9.5ms (CP-SAT exact solver)
- 67% trip reduction with 78% average utilization
- Scenario simulation: 4 strategies compared in 22ms
- Rolling replan: event-driven reoptimization in <15ms
API Endpoints
| Method | Path | Description |
|---|---|---|
| POST | /api/v1/consolidate | Full consolidation pipeline |
| POST | /api/v1/consolidate/simulate | Compare scenarios (tight/balanced/aggressive/eco) |
| POST | /api/v1/consolidate/replan | Event-driven replanning |
| POST | /api/v1/consolidate/feedback | Submit operational feedback |
| GET | /api/v1/consolidate/explain/{run_id} | Get decision explanations |
| GET | /api/v1/consolidate/insights | Learning insights + corridor patterns |
| GET | /api/v1/consolidate/compatibility/{id} | Shipment compatibility scores |
| GET | /api/v1/consolidate/history | Replan history |
Integration
Drop consolidation_v2/ into brain/app/ and add to main.py:
from app.api.consolidation_v2 import router as consolidation_v2_router
app.include_router(consolidation_v2_router)
Generated by ML Intern
This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.
- Try ML Intern: https://smolagents-ml-intern.hf.space
- Source code: https://github.com/huggingface/ml-intern
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = 'lordvisorad/fairrelay-consolidation-v2'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.
- Created
- May 16, 2026
- Updated
- May 21, 2026