Solving the Traveling Salesman Problem at Warehouse Scale with AlphaEvolve
FM Logistic, a global logistics provider, used Google Cloud's AlphaEvolve to optimize their warehouse routing and outperform their previous best solution by 10.4%.
Why it matters
The successful application of AlphaEvolve demonstrates the potential for AI-powered optimization to drive tangible business impact in complex logistics and supply chain operations.
Key Points
- 1FM Logistic's warehouse spans 8 football fields and has over 17,700 picking locations
- 2Their existing routing algorithm worked well but was limited in coordinating routes across the full warehouse
- 3AlphaEvolve, an evolutionary coding agent, generated and refined new routing algorithms to beat the human-designed original
- 4The new algorithm delivered a 10.4% improvement in routing efficiency and 15,000+ fewer kilometers of warehouse travel per year
Details
FM Logistic faced the classic traveling salesman problem in their large warehouse operations, with dozens of operators navigating thousands of storage locations. Their existing routing algorithm made decisions step-by-step, limiting its ability to coordinate routes across the full warehouse. To improve this, they turned to Google Cloud's AlphaEvolve, an AI system that generates and refines algorithms autonomously using Gemini models. AlphaEvolve started with FM Logistic's existing algorithm as a baseline, then used an iterative process to introduce mutations and new logic, testing thousands of variations against a custom evaluation function. This allowed AlphaEvolve to develop a new routing algorithm that outperformed the previous best human-engineered solution by 10.4%, translating to significant operational efficiencies and cost savings for FM Logistic.
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