Some Important Notes about Automatic Route Optimization
The automatic optimization of Routes requires solving a notorious and complex problem in computer science called “The Travelling Salesman Problem”. Truly solving the problem and obtaining the guaranteed best route for a set of points on a map is an extremely time and resource consuming problem. It is even more complex on road networks where the distance between two points might be different depending on which direction you drive between them due to one way road systems.
For this reason, the automatic optimization feature uses an approach which performs an estimate and gets a result that is not necessarily the very best solution but should be a good one and should help give your fleet significant fuel savings over time when used for a workers daily set of jobs. If your route is optimized automatically by LiGO it will let you know how much time you saved when it is finished.
Below is an example of an optimized route in Boise – that reduced total drive time by 50% for interoffice mail delivery at the City, and an example of an auto generated route geofence that you can use for other reports. This route optimization that had such an impact on Boise mail delivery took 5 seconds, and no GIS data processing.
Our take on route optimization is that there are two basic types
- GIS-based analytical optimization, similar to hydraulic modelling for water systems, that is complex, is done infrequently, and only as good as the underlying data.
- Day-to-Day optimization that creates “pretty good” routes, perhaps originally created from complex routing (number 1 above) and/or human experience, but is setup for easy to manage day-to-day operations.
LiGO uses data from 1) above if you have it, but is designed to support 2) above. Please call to discuss if you have any questions – or need more information.