Save the date: November 29-30, 2022
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Dr. Matthias Winkenbach is the Director of the MIT Megacity Logistics Lab and a Research Scientist at the Massachusetts Institute of Technology (MIT) Center for Transportation & Logistics (CTL). He is also spearheading a new research initiative at MIT CTL at the intersection of supply chain and logistics, data science and visualization, and human decision-making – the MIT Computational and Visual Education (CAVE) Lab.
Modeling real-world urban logistics systems – using data to master the last mile
Urban mobility systems are facing a number of major challenges that threaten their future ability to sustain the economic and social activity of cities. First, urbanization is progressing at a rapid pace, with more than 85% of the global population expected to live in cities by 2030. Unprecedented levels of urban density, both in terms of population and economic activity, call for innovative mobility solutions for people and goods. Second, the boom in ecommerce and `on-demand consumerism´ impose an additional burden on urban logistics systems and the underlying infrastructure. Urban freight volumes are growing and becoming increasingly fragmented as customer expectations towards the speed, flexibility, reliability, and customization of their shipments are rising quickly. Existing planning tools and distribution approaches no longer allow for an effective consolidation of urban freight flows on cost-efficient and well-utilized vehicle routes. Third, the increasingly severe effects of urban mobility on the environment and public health require the adoption of cleaner, smarter and more efficient vehicles.
In this talk, Dr. Winkenbach will touch upon some of the quantitative methods and data sources employed by his team of researchers at the MIT Megacity Logistics Lab to solve real-world last-mile logistics problems, and to enable an optimal strategic design and operational planning of freight distribution systems in complex and volatile urban environments. Further, he will highlight the growing importance of data science methods for the optimal design and control of urban freight mobility systems. Using the example of several prototype applications under development at MIT CTL’s newly created MIT Computational and Visual Education (CAVE) Lab, he will demonstrate the potential of interactive information visualization for data- and analytics-driven supply chain and logistics decision making.