How FM Logistic tackled the traveling salesman problem at warehouse scale with AlphaEvolve

FM Logistics' application of AI-driven optimization to the traveling salesman problem reveals something critical we often overlook: operational efficiency problems are increasingly becoming data problems. When companies move from rule-based routing to learning-based systems, they're not just improving KPIs—they're fundamentally changing how their data infrastructure needs to be designed. This shift demands that data engineers think beyond batch pipelines. You'll need real-time feature computation, online inference serving, and feedback loops that capture routing outcomes back into model training. The architectural implication is significant: your data warehouse can't be siloed from your ML infrastructure anymore. This pushes teams toward integrated platforms like Vertex AI, where transformation, feature stores, and model serving live in the same ecosystem. For most organizations, this means moving away from point solutions and embracing end-to-end data platforms that reduce context-switching. The practical takeaway: if you're still manually orchestrating Airflow DAGs feeding models that nobody's monitoring, you're building the old way. Invest in observable, integrated platforms that treat optimization problems as first-class citizens in your data strategy.