This project presents a logistics optimization system designed to improve transportation efficiency through route optimization and data-driven decision making.
Using heuristic approaches to the Vehicle Routing Problem (VRP), the model demonstrates measurable reductions in distance traveled and quantifies the resulting economic impact.
Transportation operations often rely on non-optimized routing, leading to:
- Increased fuel consumption
- Higher operational costs
- Inefficient fleet utilization
This system applies:
- Nearest Neighbor (baseline routing)
- 2-opt optimization (route improvement)
To minimize total travel distance.
Geographic distances are calculated using the Haversine formula, which accounts for Earth's curvature — making the model applicable to real-world GPS coordinates.
Simulation based on:
- 12 delivery points
- Urban distribution environment
- Daily logistics operations
| Metric | Baseline | Optimized |
|---|---|---|
| Distance (km) | 120 | 98 |
| Reduction | — | 18% |
Estimated impact:
- Daily savings: ~$150 MXN
- Annual savings: ~$45,000 MXN
- Route crossings significantly increase total distance
- Simple heuristics can reduce costs without complex systems
- Optimization can be applied in small and medium logistics operations
| Area | Detail |
|---|---|
| Algorithm implementation | Nearest Neighbor, 2-opt (VRP heuristics) |
| Mathematical modeling | Haversine distance, cost modeling |
| Data analysis | Efficiency metrics, % reduction |
| Visualization | Route maps with matplotlib |
| Logistics domain | Fleet routing, supply chain optimization |
- Python
- Optimization heuristics (VRP)
- Data modeling
- Transportation planning
- Fleet optimization
- Supply chain efficiency improvement
- Cost reduction analysis
Multi-vehicle routing with depot and route allocation:
Emmanuel Beristain Guzmán — GitHub