• The platform, powered by a genetic algorithm, recommends optimal inventory levels and transportation strategies.
• The solution enables companies to simulate multiple tactical scenarios and minimize costs without incurring stockouts.
Cambridge (Massachusetts), March 10, 2026. The Intelligent Logistics Systems Lab (ILS) at the Massachusetts Institute of Technology (MIT) Center for Transportation & Logistics and Mecalux have developed an artificial intelligence-based simulator capable of optimizing inventory distribution across different warehouses within the same logistics network. The platform, called Genetic Evaluation & Simulation for Inventory Strategy (GENESIS), uses advanced machine learning models to analyze thousands of possible scenarios and determine the optimal stock level at each warehouse and when replenishment should occur.
The AI-based simulator takes into account variables such as forecasted demand in each region, transportation costs, and the operational capacity of each warehouse to test various inventory replenishment policies without affecting real-world operations. “The genetic algorithm enables multiple simulations to be run using different parameters until the most efficient logistics strategy is identified. Companies can compare scenarios and select the one that best fits their operations,” says Dr. Matthias Winkenbach, Director of Research at the MIT Center for Transportation & Logistics and the Intelligent Logistics Systems Lab. (....)