Deep Learning for Fleet Management

Interesting example,  worth examining.   Other operations examples?

New Deep Learning Systems Profoundly Disrupt Fleet Management Operations

Deep learning is having a profound impact on the future of fleet management through greater efficiency.

Posted by  Ryan Kh  in Smart data collective. 

Deep learning tech is influencing and enhancing many industries, promising to provide insights into key business operations which were not previously possible to unearth. Transportation and logistics is a prime example.

The transportation analytics industry is projected to be worth $27 billion by 2026. One of the biggest applications of this technology lies with using deep learning to streamline fleet management.

Fleet management is one area that is especially well positioned to benefit from the latest data-driven analytical tools, so here is a look at just how much positive disruption is being caused in this market at the moment.

Improvements to efficiency & sustainability

Businesses which operate fleets of vehicles, whether small or large, are under increased scrutiny with regards to the sustainability of their operations at the moment.

There are a number of ways to go about improving the eco-friendliness of business fleets, with the long term aim of many organizations being to migrate to fully electric vehicles, leaving fossil fuel powered incumbents in the past where they belong. .. ' 

Ant Algorithm for Commercial Fleets

This kind of bio behavior mimicry was experimented with in some yard applications, found to work better for some path variability.

Ant Algorithms Help Fleet Operators Halve Emissions
The Engineer (U.K.)
July 27, 2020

Researchers at Aston University in the U.K. have developed software that imitates how ants share knowledge, in an effort to help cities and towns reduce emissions and achieve clean air targets. The researchers found that ants can keep a record of the best solutions to problems and update their knowledge similarly to how computer algorithms do so. The researchers were able to improve these ant algorithms to reduce the number of decisions they make and apply that knowledge to city-scale fleet-routing problems. Said Aston's Darren Chitty, "Algorithms based on the foraging behavior of ants have long been used to solve vehicle routing problems, but now we have found how to scale these up to city-size fleets operating over several weeks in much less time than before. It means much larger fleet optimization problems can be tackled within reasonable timescales using software a user can put on their laptop."