This project solves a Vehicle Routing Problem (VRP) for optimizing the pickup and drop-off of staff members using a fleet of vehicles with varying capacities.

• Optimized Routing: Minimize the total distance traveled by all vehicles.
• Capacity Constraints: Ensure vehicles do not exceed their passenger capacities.
• Demand Fulfillment: Ensure all staff members are picked up and transported to the depot.
• Customizable Configuration: Define vehicle capacities, starting points, and depot location.

• Python 3.x
• ortools library for solving the VRP
• geopandas and matplotlib for visualizing the routes and the map

pip install ortools geopandas matplotlib

• Each vehicle has a defined capacity, limiting the number of passengers it can carry at any given time.
• The capacity of each vehicle is specified and must be adhered to throughout the routing.

• Each staff member location has a demand of one passenger.
• The depot and vehicle starting points have a demand of zero.
• The total load on a vehicle is the sum of the demands of all visited locations, excluding the depot.

• The depot is the starting and ending point for all routes.
• Vehicles must start and end their routes at the depot.

• Each vehicle has a specified starting location.
• All vehicles end their routes at the depot.

• The distances between locations are calculated using the Euclidean distance formula.
• The total distance for each vehicle's route is the sum of the distances between consecutive locations on that route.

• The routing model must ensure that all staff members are picked up and transported to the depot.
• Each vehicle must respect its capacity constraint while picking up staff members.
• The routes must minimize the total distance traveled by all vehicles.

• The solver uses a specific search strategy to find the optimal or near-optimal routes.
• The search strategy used is "PATH_CHEAPEST_ARC," which builds a solution by repeatedly connecting the nearest node.

• The depot does not contribute to the vehicle load.
• The demand of the depot is always zero and is excluded from the load calculation.

The routes are visualized on a map of Mauritius, showing the paths taken by each vehicle, the distances between stops, and the vehicle loads.

This project is licensed under the MIT License - see the LICENSE file for details.

• Google OR-Tools
• GeoPandas
• Matplotlib
• Natural Earth Data
• GADM