Global digitalisation is undeniable and has become increasingly apparent in recent years. We are facing the fourth industrial revolution. This means, among other things, that we have to work with more and more data, requiring more complex techniques to analyze it, leading to the need to improve how information is displayed to the user and how the user interacts with digital products.
The first step in developing an optimisation solution is to find out where there is a suitable scenario to work on, where and why a business process is lacking, and what are those indications that will help us to focus the shot, to bet on projects that will be clear winners.
Did you know that calculating university or college timetables is one of the most difficult problems to solve? In this post, we present the fundamental aspects of this problem and explain how we can improve the schedules of educational institutions.
The column generation method is used to efficiently solve complex combinatorial problems as diverse as cutting metal bars, designing personnel shifts, routing vehicles, scheduling production or planning site visits by sales or maintenance teams. In this article, we will use a simple example to explain how this method works.
The production scheduling problem is an operational problem that must be addressed very frequently.
A few years ago we published on this blog a riddle attributed to Einstein that only 2% of people are said to be able to solve. Here we explain how to solve it using prescriptive analytics.
As an supplement to his course at the UPM ETSII, our cofounder Dr. Álvaro García records and shares short videos and playlists on Operations Research, both in English and Spanish.
When money is invested in an application to achieve savings or increase the income of a company, it is convenient to estimate its Return of Investment to decide if acquiring or developing the application is worth it.
In the discipline of Operations Research, the Ant Colony Optimization algorithm (ACO) is a technique to solve complex combinatorial problems inspired by the behaviour shown by ants in nature, swarm intelligence.
At baobab soluciones we took the challenge of improving the current solution using linear programming techniques.