dsd-route-optimization

Optimize Direct Store Delivery (DSD) routes by applying a Vehicle Routing Problem with Time Windows (VRPTW) framework using OR-Tools. This ensures efficient delivery schedules and minimizes operational costs for retail distribution.

Route optimization accounts for driver merchandising tasks by integrating the time drivers spend stocking shelves and performing in-store service requirements directly into the mathematical modeling constraints.

VRPTW handles delivery time windows and vehicle capacities by treating them as critical operational constraints within the mathematical model, ensuring feasible and deterministic route planning for supply chain logistics.

Plan daily delivery routes for hundreds of convenience stores by leveraging OR-Tools to process store data and depot locations, ensuring timely deliveries and optimal truck loading based on variable demand.

This route optimization approach supports dynamic routing based on variable demand in CPG distribution by adjusting deterministic route planning to accommodate pre-sell models and fluctuating delivery requirements.

You need Python libraries like OR-Tools, NumPy, Pandas, and SciPy to solve logistics delivery planning problems, as they provide the necessary mathematical modeling and data processing capabilities for the VRPTW framework.