Machine learning-based geocoding & intelligent geofencing
By FarEye | October 13, 2022
Imagine a situation - You have 1,000 parcels to deliver to unique addresses and your truck has a capacity of 100 parcels. The number of possible combinations from one address to other could be {C(1000,100)}
63850511926305130236698511142022274281262900693853331776286816221524376994750901948920974351
797699894319420811933446197797592213357065053890 (A whopping 140 digits!)
A computer would take some days and a human - some years with a lot of coffee to check for the most optimized sequence based on distance and travel time for these many numbers of possibilities.
FarEye’s dynamic auto-routing is an answer to this huge challenge!
Routing requires accurate geocoding for both route planning and ETA calculations. FarEye uses sophisticated algorithms to self-learn the ‘correct’ address from attempted delivery addresses, success and failures. FarEye transforms a description of a location—such as a pair of coordinates, an address, or a name of a place—to a location on the earth's surface. The address is spatially displayed on the locations and patterns are recognized within the information. It converts English addresses into geographic coordinates as latitude & longitudes, which are used to mark positions on a map.
No personal information (PII) is tracked, which makes FarEye fully compliant with regulations.
As next steps, an intelligent geofencing is created in order to improve efficiency.
The manager can create different geofences of any shape or size, as a guide for auto-routing
This is typically used to mark out areas that should not be clubbed when creating routes
Routes are automatically recommended as per the load and segregation in the fences
The manager can combine multiple geo fences dynamically to optimize for that day’s volume and load
The manager can monitor unlimited locations, override and tweak the individual points on the route and reassign to different parcels
FarEye supports manual sticky routing where the manager can assign riders familiar with specific parts of the city/area or zone
All this and a million more algorithms and processes are run simultaneously in the back-end while fetching the most optimized routes for the complete fleet.
