At a time when industry and technology are in a constant state of change, so are our ideas. Over years of experience, our ideas have been evolving and have led us to create concepts and methodologies that we have been putting into practice to solve them.

These solutions have to solve the most important needs of the industry, which are complex and have numerous variables that influence their efficiency. Among these variables are the resources used, space distribution, process times, order demand, costs, or task priorities.

Understanding the interactions between all these variables can be complicated, especially when they do not follow linear relationships and we do not know how one change can influence future processes.

For years, the industry has had the ability to use Simulation to manually create different combinations of these variables, evaluate the results and detect patterns of behavior. But what if we were to delegate this task to a tool capable of intelligently searching for the combinations of variables that yield the best results?

This is where genetic algorithms become relevant.

What are Genetic Algorithms?

Genetic algorithms (GAs) are a type of heuristic optimization algorithm inspired by the process of natural selection, conceived by John Holland in the 1960s. These algorithms have the ability to test different scenarios and combine those that come closest to a predefined objective, such as minimizing costs or maximizing returns. Throughout this process, they generate an “offspring” of solutions that come closer and closer to the optimal solution.

Its use is interesting, as it is an automatic process that can handle many input variables in a simple way, intelligently searches for solutions and explores scenarios that would seem counter-intuitive at the outset.

Hybrid approaches that combine these algorithms with cutting-edge technologies such as Digital Twins make it possible to provide answers to large needs common in the industry.

The value GAs provide to the industry

Genetic algorithms are especially useful for solving complex optimization needs, finding the best solution within a set of possibilities.

For example, in task sequencing or assignment, in what order should we prepare orders to reduce costs? Let’s imagine we have 20 orders to prepare. We can use a genetic algorithm that tries different sequences of preparation and quickly approximates the optimal solution. If we tried to perform this task manually, the number of possible combinations would be equivalent to 20 factorials, so we would need supercomputers to be able to test all the options.

Another example could be to determine the number of resources needed, how many vehicles should we use in a courier distribution fleet to maximize deliveries per hour and minimize costs and delivery times? In this case, genetic algorithms could also help us in assigning tasks to each vehicle, automatically generating an efficient work plan.

In addition, we could also use genetic algorithms to determine the optimal layout of spaces, what would be the layout of spaces in a hospital that minimizes the distance traveled by patients? Or where should we place machines in an industrial plant to reduce waiting times?

Among all its advantages, its versatility to be applied to various types of needs, from the simplest to the most complex, as well as the ability to discover the best solutions in changing environments or with incomplete information, or the efficiency in identifying optimal solutions in a reasonable time, stand out. However, all these functionalities present challenges; for example, the complexity of their implementation, which requires specific knowledge, or the choice of the appropriate parameters, which influence the performance of the algorithm itself. In short, genetic algorithms are a fast and efficient way to optimize resources, time and investment in any complex environment, and an indispensable tool in the digitization of industry.