DAMMaxxing

Admin window landing page: Data loading and regulable cluster distance, for single-stop, mutli-delivery sequences
Routing Companion, Data summary and Warnings for the Admin
Score Breakdown (explicable decision-making metrics), and 3D view of the load
Load summary: Pallets' load, warehouse routing for optimized pallet loading
Vehicle, Warehouse and Pallet layout overview
Driver window: Route Summary with port to Google Maps app/web.
Comparison between our optimized route and the standard route
Delivery companion and vehicle load 3D visualization.
Vehicle loading preview (mobile)
Data upload section + max walk distance (mobile)
3D warehouse layout (mobile)
Routning companion: Next stop info + albarain + Maps redirection (mobile)
Reception form and signature
Delivery Albarain (mobile)
Routing metrics for the Admin (mobile)
Google Maps redirection (mobile)

Inspiration

Our inspiration comes from the challenge provided by Estrella Damm.

What it does

The main idea of the project centers around devising a single problem comprising storage, loading and routing. The system devises an all-around plan for logistics coordinators, warehouse managers, and deliverers, so every accountable position in the process is informed and paricipates in the delivery from a single platform.

How we built it

We used Python, FastAPI and Google Studio API to build our core algorithm. Then used Tree (Javascript lib) to show the 3D layouts and Javascript to launch the website.

Challenges we ran into

Deciding on the degree of optimization required to not hinder neither the loading nor the routing optimization.
Address mapping to geolocation coordinates.
Deciding which features would aid the company's job without unnecessarily complicating the platform

Accomplishments that we're proud of

Integration of diverse APIs, clustered location picking and 3d packing, with options to deploy on mobile and a tracking system with an authenticized delivery record via receiver's signature.