The practical test
Google DeepMind’s AlphaEvolve [3] is considered a milestone in AI-driven algorithm development. The system autonomously generates code, including for complex planning and optimization problems—exactly the kind of challenges that arise daily in production planning, logistics optimization, or energy management. A team from the Berlin-based MODAL research campus subjected AlphaEvolve’s solutions to a systematic practical test. The result: In all benchmark problems examined—from geometric packing problems to distance optimizations—classical mathematical optimization methods consistently achieved better results.
Let’s take the circle packing problem as an industry-relevant example: In practice, this corresponds to the optimal arrangement of components on workpiece plates or the efficient loading of containers. AlphaEvolve achieved a solution with a value of 2.63586275. Classical optimization yielded 2.63591551—a seemingly minimal difference of 0.002%. What appears marginal at first glance can have enormous real-world implications.
With millions of packing operations annually, even the smallest improvements add up to significant savings potential—for example, in material consumption, production time, or transportation costs. Similar superiority was demonstrated in other test problems: In the circular packing problem within a rectangle, classical optimization outperformed the AI result, as it did in the minimum distance ratio problem.
Even more relevant is the methodological difference: While AlphaEvolve trains highly problem-specific algorithms over many iterations, the optimization approach allowed proven and generic solver software—in this case, FICO Xpress—to be applied directly to the mathematical model created in just a few lines of code.
What does this mean for industrial practice?
In production control or supply chain optimization, reliable, reproducible results are essential. Production planning professionals must be certain that the algorithm will always deliver the same—and, above all, the optimal—result given the same input data. AI-generated heuristics, however, often incorporate elements of chance and produce slightly different results with each run. Mathematical optimization, on the other hand, is deterministic: same input, same result—every time. This reproducibility is not an academic luxury, but a fundamental requirement for certified processes and regulated industries.
When a production line comes to a standstill or a supply chain needs to be rerouted, every minute counts. Decision-makers must be able to understand why production stoppages, rising energy costs, and volatile supply chains occur: Today, industrial companies must make decisions in seconds that can cost or save millions. At the same time, the pressure to operate more sustainably and efficiently is growing—from optimized logistics routes and reduced setup times in manufacturing to intelligent energy management.
a specific solution is proposed. Classical optimization methods not only deliver a solution but also mathematical proof of its quality—including information on how far the found solution is from the theoretical optimum. This transparency builds trust and enables well-informed decisions.
Established optimization solvers often require only a few minutes on standard hardware. This is a decisive advantage for real-time decisions in production control or dynamic route planning. AlphaEvolve’s algorithms, on the other hand, are heuristics—they seek good solutions without a guarantee of optimality. They rely on exploratory diversity and creative approaches. Global optimization, in contrast, aims to provide not only better solutions but also mathematical proof of their quality. The superiority of systematic search strategies is particularly evident in highly constrained problems with complex constraints—as is typical for industrial applications.
Hybrid Intelligence as a Model for the Future
The results in no way diminish the value of AI in industrial optimization—on the contrary. Rather, they demonstrate how AI and classical methods complement each other optimally. The biggest hurdle in applying mathematical optimization is often translating the business problem into a formal mathematical model. This is where generative AI can play to its strengths:
It converts natural-language problem descriptions into precise optimization models, thereby making the power of mathematical methods accessible even to non-mathematicians. For example, production managers might formulate the problem in natural language: “I need to distribute 500 different products across three lines, where Line A can only process products weighing up to 50 kg, and all orders must be completed by Friday.” The AI translates this into a mathematical model, which the optimization software then solves.
According to a recent study, 78% of companies worldwide use AI in at least one business area—a significant increase from 55% the previous year [4]. This trend is also evident in the integration of AI into planning systems, with companies increasingly adopting hybrid approaches that combine fast AI-based heuristics with precise mathematical optimization. The key lies in intelligent orchestration: A higher-level system decides on a case-by-case basis which method or combination of methods promises the greatest success, depending on the problem at hand, time constraints, and requirements for precision and flexibility.
Practical Implications for Decision-Makers
For companies investing in optimization technologies, there are clear recommendations for action:
- Problem analysis must come before the choice of technology. Not every problem requires the latest AI. For highly structured problems with many constraints—such as in production planning or logistics—traditional methods are often superior.
- Investments should be directed toward hybrid systems that intelligently combine both approaches, rather than relying on a single technology. Software providers in the analytics sector often already offer integrated solutions for this purpose.
- The focus should be on the precise modeling of the business problem. The key to successful optimization often lies less in the algorithm than in the model itself. Here, AI can make a valuable contribution as a translation aid between natural language and mathematical formulation.
- Before critical business decisions are based on AI-generated solutions, these should be tested and validated against established methods.
Conclusion and Outlook
The future belongs neither solely to AI nor exclusively to classical optimization—it belongs to intelligent, adaptive systems that combine both approaches as the situation demands. For industrial practice, this means: less hype, more systematic evaluation. In an era where production efficiency and supply chain resilience determine competitiveness, companies cannot afford to sacrifice precision. The current method comparison by the MODAL Research Campus clearly demonstrates: The combination of AI-supported flexibility and mathematical rigor is becoming the decisive factor for success—not blind trust in the latest trend.
