Computational and Visual Education (CAVE) Lab
The CAVE lab provides students, researchers, and decision makers with a more intuitive understanding of and access to quantitative methods to support strategic design, tactical planning and operational decision problems in the supply chain and logistics domain and related fields. Based on a newly created physical lab space at MIT CTL equipped with state-of-the-art visualization technology, the lab is developing interactive visual interfaces to data and analytical tools, addressing complex supply chain and logistics problems.
The lab enables research advances in three major domains:
Development, improvement and application of traditional quantitative methods in supply chain, logistics, and transportation decision making (network design, distribution systems, inventory management, risk management, etc.)
Adaptation and application of advanced data science methods (machine learning, network science, etc.) to large and diverse datasets to characterize, understand, predict, and improve the performance of complex supply networks, transportation and logistics systems
Behavioral analysis of human decision making in supply chain management, transportation and logistics in light of interactive visualization being used as a tool to communicate, analyze, and manipulate context- and problem-related information