How logistics companies can benefit from machine learning and evolutionary computing
Updated: Jul 18, 2019
For logistics companies, efficient deliveries have been a challenge for a long time. Some use too many vehicles; others need to drive too many miles, and some spend too much time at the drop-off point. But no matter what the problems are - they are costing these companies too much money. Luckily, technological innovation, specifically in machine learning and evolutionary computing, is helping them to address these challenges head on.
Many large logistic companies have been in the business for many years, having already made hundreds of thousands of deliveries. This means they are sitting on a treasure trove: when all the information on completed deliveries is gathered, combined and cleaned up, this historical data can be analyzed. Thanks to machine learning, algorithms that are used by computer systems to perform specific tasks without explicit instructions, logistics companies are able to process this data, and extract valuable information from it.
Combining historical and actual data
While this can also be (and has been in the past) done by hand, algorithms will be able to analyze much larger amounts of historical data in a much shorter time window. Moreover, they are able to combine historical data with actual information on traffic and weather conditions, but also with things like time-based road restrictions. For example, in some cities, large delivery trucks are not allowed on certain roads during certain hours. Sometimes, they are not allowed to use the road at all. But if the logistics company wrongfully assumes it is allowed to use the road, this can lead to detours, delays and missed delivery time windows. Therefore, the algorithm takes this into account and prevents such problems from happening.
Using Machine Learning capabilities, companies will also be able to trim down ‘service time’: the minutes spend on queuing, unloading a delivery from the truck and waiting for the customer to accept it. Many logistics companies work with average service time estimates when they plan, but these are never accurate. Properly measuring how much time is expected to be spent at each location, and predicting such across different time slots and seasons, is too complex to do by hand. However, using a machine learning model to analyze historical data, companies are able to accurately predict the amount of service time they are most likely to need on a specific route on a specific day in a specific region. This can have a significant impact on service time optimization.
Another reason to have computers analyze the available data, is evolutionary computing. Most computer algorithms are relying on human intelligence, reflecting how our brains would react to certain information: in an intuitive way. In evolutionary computing, algorithms work irrespective of data patterns. That allows them to respond counterintuitive – i.e., in a way the human brain would never respond. Thanks to evolutionary computing however, the computer will be able to suggest options that may sound illogical but turn out to be best.
Evolutionary Computing can also help decide very efficiently how many vehicles should be assigned to each region. If records show that a larger number of customers in a specific region has been opting for same day delivery, the logistic company will be able to deliver all packages on time. Either by making sure it has enough trucks available, or by rerouting vehicles and having them come by the warehouse and quickly loading them with new orders that came in while they were away.
Combining AI Disciplines to achieve more
The combination of Machine Learning and Evolutionary Computing, two key disciplines of Artificial Intelligence, can become a real game changer and companies started to adapt this trend and explore such capabilities in an attempt to achieve more in an increasingly competitive and noisy market. Above are just some of the ways in which artificial intelligence can help optimize logistics distribution and enable effective delivery management.
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