Domain: Logistics/Supply Chain Mgmt
Service Area: AI & ML
Context
In this scenario the goal was to automate the task of generating efficient delivery route to ensure timely deliveries of pre-orders and utilizing the remaining capacity of fleets to serve dynamically placed orders.
Solution
To achieve this particular use case, we have considered DVRPTW (Dynamic Vehicle Routing Problem in Time Window) technique. Within this approach, our model creates the Pools on top of the input clusters given and where we want to define the Fixed Routes on the basis of the pre-orders as well as like to cater the dynamic requests.
Outcomes
Different benefits has been achieved thanks to this model:
- Better time to serve the customer
- Optimization of the fleet cost to serve
- Introduced a new delivery model, in which the client was able to combine and serve both scheduled delivers and dynamic delivery requests