Optimization of production sequences: Darwin's solution

Artificial Intelligence Survival of the Fittest: What AI-Driven Production Optimization Can Learn from Darwin

2025-06-30 A guest contribution by Fabio Eupen* | Translated by AI 6 min Reading Time

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Multiple machines, numerous orders, limitless combinations: The number of possible production sequences increases exponentially with each factor. So how can the optimum be found without calculating for days before starting production? Charles Darwin provides the answer.

The parameter space of a manufacturing process can be visualized as a hilly landscape, where the goal is to find the global optimum (highest peak).(Image: iStock) — The parameter space of a manufacturing process can be visualized as a hilly landscape, where the goal is to find the global optimum (highest peak).
(Image: iStock)

How do manufacturing companies find a way out of the crisis? One possibility: through increased productivity. But what to do when order books slowly fill up again, but production capacities then reach their limit and the existing machinery park nears its boundaries? So far, new investments have been the means of choice to increase productivity and stimulate growth, but high expenditures for new machines are hardly an option for many companies in a still volatile environment. They are left with only one path: to utilize existing capacities even more efficiently to accelerate production throughput and thus increase manufacturing volume without investing in the machinery park. Ideally, higher productivity not only paves the way out of the crisis but also improves the companies' competitive situation for the time afterward.

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