Across the country, hundreds of thousands of drivers deliver packages and parcels to customers and companies each day, with many click-to-door times averaging only a few days. Coordinating a supply chain feat of this magnitude in a predictable and timely way is a longstanding problem of operations as researchers work to optimize the last leg of delivery routes.

This is because the last phase of the process is often the costliest due to inefficiencies such as:
• Long distances between stops
• Weather delays
• Traffic
• Lack of available parking
• Customer delivery preferences
• Partially full trucks

These inefficiencies became even more exaggerated and evident during the pandemic. But with newer technology and more individualized and nuanced data, researchers are able to develop models with better routing options. However, they must balance the computational cost of running them.

Matthias Winkenbach, MIT principal research scientist, director of research for the MIT Center for Transportation and Logistics (CTL), and a researcher with the MIT-IBM Watson AI Lab, discusses how artificial intelligence could provide better and more efficient solutions.

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