THE OPTIMIZE EDIT
THEOPTIMIZEEDIT
HOW WE USE AI
Here at Optimize, we talk a lot about our powerful artificial intelligence-based algorithms, but how do we harness AI within our technology and apply it to our industry?
As some of you may be aware, we have been beavering away behind the scenes working on a shiny new algorithm with improved performance as well as additional new functionality. Firstly, let’s dive into the nerdy part about how our new algorithm has improved performance. There are two key metrics we can measure the performance of our algorithm. The first is speed i.e., how fast an algorithm can return results, and the second is the quality of the outputs, usually measured as cost, which is a function of distance, time and number of vehicles used. We are currently in a phase of testing our new algorithm and pushing it to its limits, and as part of this process, we can see for nearly all use cases tested so far, improvements in both metrics compared to our non-AI based algorithm, on less powerful servers.
So, how have we achieved these improvements? Well, whilst we are not going to reveal all our secrets, we can go a little bit into how our algorithm functions makes use of AI search techniques such as Monte-Carlo methods Our optimisation algorithms fall into a category called "Anytime Algorithms", in short these are algorithms which generate a solution then continuously try to make improvements to the solution, their name comes from the fact they can be stopped at any time. In general, the longer these algorithms are allowed to execute the better the solution. With our new algorithm we also embrace the fact that there is a wide variety in the types of problems we are trying to solve, and instead of trying to create a one size fits all algorithm, we have many subalgorithms and for each iteration a sub-algorithm is selected.
HOW WE USE AI
This is where Monte-Carlo methods come in. We cannot write a rule set for every type of problem to select a sub solver, so we use Monte-Carlo methods to intelligently select a sub-algorithm for each iteration. In short, this works by randomly selecting a sub-algorithm based on a weighting, and after the iteration has completed, weightings are adjusted, and the cycle continues. This method allows sub-algorithms which perform better for a given problem to be selected more often and vice versa for less performant ones. As the weightings also adjust over time this also has the advantage that if a particular sub-algorithm can no longer make improvements its weighting will drop allowing for another to take its place. i.e., machine learning.
As a consequence of building a new algorithm from the ground up, this has also given us the opportunity to start introducing new features One feature we wish to highlight is that of mobile hubs A hub location is normally fixed, where a delivery (or pickup) can be routed through In our solution, a mobile hub can be a larger vehicle on the move, where smaller more efficient vehicles rendezvous with this vehicle to perform the last mile. This introduces a new challenge as this essentially breaks a job into two parts where the order of completion is critical. For example. you cannot pick up a parcel from a hub that has yet to be delivered there. To facilitate this, we have introduced a new constraint that can be applied to jobs making them dependent on a pre-requisite job to be completed first. Another use case could be where equipment and supplies need to be delivered to a customer location before an engineer can visit to perform an installation.
If you are interested in exploring more, you can visit our website using the link below and see our algorithms in action; https://optimizenow.ai/#project-section
