AI in logistics
MIT and Mecalux launch research project
The Center for Transportation & Logistics at the Massachusetts Institute of Technology (MIT) and Mecalux have launched a five-year cooperation project. The aim of the project is to accelerate the integration of self-learning artificial intelligence (AI) in logistics.
As part of MIT's Intelligent Logistics Systems Lab, both institutions will explore new application areas for these AI models. "The goal of our collaboration with Mecalux is to foster disruptive innovation and develop two high-impact use cases where AI is transforming decision making in the industry. We will train complex machine learning models that learn autonomously, drive cost savings, reduce the carbon footprint and improve service quality for customers," explains Dr. Matthias Winkenbach, Research Director of the MIT Center for Transportation & Logistics and Head of the Intelligent Logistics Systems Lab.
In the first year of this project, the teams from the Intelligent Logistics Systems Lab and Mecalux will develop two research projects. The first focuses on increasing the productivity of autonomous robots in warehouses. Using advanced simulations, optimization techniques and machine learning, the researchers will develop a "swarm intelligence". This will enable several robots to act as a single unit and make collective decisions. "We will develop a new generation of autonomous robots that learn from human behaviour in order to improve collaboration and efficiency in warehouses," emphasizes Winkenbach.
The second research project focuses on training models with self-learning AI. The Intelligent Logistics Systems Lab aims to develop systems that learn independently from fluctuations in demand and predict new customer purchasing habits. "Today's distribution systems do not take into account the full complexity of the logistics network and tend to make simplistic assumptions. This project will help companies with multiple warehouses, distribution centers and stores to automatically determine the most efficient way to deliver each order based on the real-time status of the distribution network," explains the head of the Intelligent Logistics Systems Lab.
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