Route Optimization which is based on Genetic Algotrithm and Tabu Search in order to find the best possible paths in order to pickup employees in certain time window.
- Given number of buses, passenger and bus stop locations. Develop a route optimization algorithm to determine route and schedule of buses subject to the provided constraints.
- System should cater to the real time changing demand of employees
- Both pickup and drop routes should be generated
- Heterogeneous Fleet of buses are considered
- Minimize Operational Cost
- Fuel cost is the dominant factor
- Best measured by time
- Number of Buses (hard)
- Bus Capacity (heterogeneous fleet, hard)
- Time window to reach office (hard)
- Time Window created using employee’s time windows (both hard and soft)
- Maximum Riding Time (hard)
- Minimum Occupancy (both hard and soft)
- Can be done manually
- Excel csv sheets is used to feed
- "python manage.py runserver"