MIT-Mecalux research collaboration
Harnessing the power of AI to revolutionise logistics
The Intelligent Logistics Systems Lab brings together researchers from MIT’s Center for Transportation & Logistics and industry experts from Mecalux to develop and implement AI-based solutions targeting some of the most pressing and high-impact challenges in logistics.
The lab, founded with support from Mecalux, aims to drive advancements that enhance efficiency, sustainability, resilience and customer satisfaction within the logistics industry.
MIT-Mecalux joint research projects
- Enhancing warehouse robot productivity with machine learning.
- Optimising order distribution with self-learning AI models.
Testimonials
With the support of Mecalux, we are confident that our research will lead to groundbreaking advancements in the field.
The objective of our collaboration with Mecalux is to foster disruptive innovation and achieve highly impactful use cases where AI transforms industry decision-making.
Our partnership with MIT CTL will drive research-based innovation into practice and set new standards for operational excellence in the industry.
Research themes
-
Predictive intelligence
High-impact predictive capabilities powered by AI and ML, including the development of highly accurate near-term forecasting essential for highly responsive logistics services such as same-day and sub-same-day delivery.
-
Prescriptive intelligence
New methods and models that combine operations research with ML and AI to solve complex combinatorial optimisation problems critical for logistics, such as vehicle routing, inventory planning and network design in a richer context of non-trivial real-world objectives, constraints and uncertainties.
-
Autonomous intelligence
Control and impact of advanced logistics systems and technologies that can independently perform tasks, make decisions and learn from their environments without continuous human intervention. For instance, this includes mobile robots that assist or replace humans in warehousing or delivery activities and that operate autonomously in complex and dynamic settings.
-
Collective intelligence
Collective behaviour and coordination of autonomous systems or entities working together to solve a common problem. In the context of intelligent logistics systems, this involves the synchronisation and cooperation of multiple agents such as autonomous robots or crowd-sourced carriers to optimise system performance.
-
Augmented intelligence
How human decision-making can be enhanced by combining human intelligence with AI. Specifically, the lab intends to explore how decision support systems and operations management software can effectively combine human expertise with AI-driven insights.
