This project applies the Capacitated Vehicle Routing Problem (CVRP) to enhance delivery logistics for a local restaurant in Waterloo. Utilizing mathematical modeling and optimization algorithms, we aim to boost operational efficiency, reduce costs, and elevate customer satisfaction.
xijk: Binary variable, 1 if vehicle i travels from location j to k, else 0.Loadij: Continuous variable for the load of vehicle i after visiting location j.
Minimize the total distance traveled by the fleet:
n: Number of vehiclesm: Number of addresses to deliver toDistancejk: Distance between address j and k
- Visit each location exactly once, excluding the depot.
- Start and end routes at the depot (local restaurant).
- Vehicle load should not exceed capacity.
- Load changes after each delivery.
- No subtours or depot revisits mid-route.
- Mathematical Optimization: Minimizes total travel distance with constraints.
- API Integration: Uses Google's Distance Matrix API for distance calculations.
- Visualization: Employs Folium for interactive mapping and Matplotlib for plotting.
- Python 3.x
- Pandas
- Google Maps API
- OR-Tools
- Folium
- Matplotlib
For a detailed analysis and insights, refer to the complete report: VRP_GITHUB/Report.pdf
- Banat Khural - GitHub
CO 370: Deterministic OR Models
Instructor: Martin Pei - Profile
- Efficiency Improvement: Reduction in operational costs and enhancement of customer service through route optimization.
- Practical Application: Real-world application of theoretical models, linking academic concepts with business needs.
- Business Impact: Highlights the significance of route optimization in local business competitiveness and efficiency.
- Future Scope: Discusses potential enhancements, including scalability and real-time traffic data integration.
For an interactive view of the routes, see the HTML map file: Map
(Note: The HTML file is best viewed in a web browser.)
THANK YOU