The goal is to enable the design of supply chains that can set new standards of sustainability and affordability…
The Massachusetts Institute of Technology (MIT) Logistics Center is exploring the potential of machine learning (ML) and artificial intelligence (AI) to transform the future of logistics and freight operations.
This is being done by launching a new research lab called Intelligent Logistics Systems, which will explore several research streams that may lead to advanced approaches to addressing some of the industry’s most complex challenges.
For example, the lab will investigate advanced methods and tools capable of producing highly accurate short-term forecasts with high spatial and temporal resolution. Such short-term forecasting capabilities are critical to enabling same-day or earlier delivery and similar services designed to meet the challenging needs of consumers and business customers.
The cost of this laboratory was provided by Mecalux.
This innovation space will be led by Dr. Matthias Winkenbach, Director of MIT CTL Research. “We want to support the application of new technologies based on artificial intelligence and machine learning to tackle the most impactful real-world challenges facing companies and society,” says Winkenbach.
The activities of the new research lab at MIT CTL will enable the entire industry to design supply chains and logistics systems that provide advanced customer service and set new standards in terms of sustainability and cost-effectiveness.
Operational excellence relies on the seamless integration of autonomous technology into warehouse processes. Javier Carrillo, CEO of Mecalux, which funds the lab, says artificial intelligence and machine learning can be critical in planning and monitoring these resources.
The Intelligent Logistics Systems Lab at MIT CTL will also study the role of new technologies in controlling transportation systems, autonomous delivery, and in automating processes such as picking, sorting, packing, and shipping orders from warehouses or stores.
Another area of research and development will be hybrid methods at the intersection of operations research (OR) and ML. It aims to solve complex and multi-modal hybrid optimization problems that are critical to the success of the logistics industry, including vehicle routing, inventory planning, network design, transportation planning, and related issues.
