The Massachusetts Institute of Technology’s Center for Transportation and Logistics (MIT CTL) has launched a new lab to explore high-impact applications of data-driven technologies in the logistics industry, with the founding of the lab supported by seed funding from intralogistics group Mecalux.
According to an official release from Mecalux, MIT CTL’s Intelligent Logistics Systems Lab will specifically explore the potential of machine learning (ML) and artificial intelligence (AI) to transform the future of logistics operations and goods transportation.
Mekalux will provide technical insight and support from its software and automation experts over the coming years, the statement added.
“The new lab will explore several research areas that have the potential to lead to cutting-edge approaches to tackling some of the industry’s most complex challenges. For example, the lab will investigate cutting-edge methods and tools that can generate highly accurate short-term forecasts at high spatial and temporal resolution. These short-term forecast capabilities are essential for enabling services such as same-day or near-same-day delivery, designed to meet the increasingly demanding needs of both consumers and commercial customers,” the release stated.
The Innovation Space will be led by Matthias Winkenbach, director of research at MIT CTL. “We want to support the application of new AI- and ML-based technologies to address the most impactful real-world challenges facing business and society,” Winkenbach says.
“The work of MIT CTL’s new laboratory, established with the support of Mecalux, will enable the entire industry to design supply chain and logistics systems that deliver cutting-edge customer service and set new standards in terms of sustainability and cost-effectiveness.”
“Operational excellence depends on the seamless integration of autonomous technologies into warehouse processes. AI and machine learning will be crucial in planning and monitoring these resources,” said Javier Carrillo, CEO of Mecalux.
The lab will investigate the role of new technologies in automating processes such as picking, sorting, packing and shipping orders from warehouses and stores, as well as managing autonomous transportation and delivery systems.
Another area of research is the development of hybrid methods at the intersection of operations research (OR) and machine learning. The goal is to solve increasingly complex and multifaceted combinatorial optimization problems that are critical to the success of the logistics industry, such as vehicle routing, inventory planning, network design and transportation planning, the release added.
