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Technical Proceedings of the Amazon Last Mile Routing Research Challenge
| dc.contributor.author | Winkenbach, Matthias | |
| dc.contributor.author | Parks, Steven | |
| dc.contributor.author | Noszek, Joseph | |
| dc.contributor.editor | Winkenbach, Matthias | |
| dc.contributor.editor | Parks, Steven | |
| dc.contributor.editor | Noszek, Joesph | |
| dc.date.accessioned | 2021-09-02T21:49:19Z | |
| dc.date.available | 2021-09-02T21:49:19Z | |
| dc.date.issued | 2021-09-02 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/131235 | |
| dc.description.abstract | In the spring of 2021, Amazon hosted the Last Mile Routing Research Challenge with support from the MIT Center for Transportation & Logistics. The goal of the challenge was to encourage participants to develop innovative approaches to produce solutions to a real-world route sequencing problem, leveraging a large set of real operational data released by Amazon. All finalists were asked to submit short technical papers to document their methodological approach and its expected performance. This proceedings document provides a consolidated overview of the papers submitted by the participants. | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | cities | en_US |
| dc.subject | urban | en_US |
| dc.subject | last mile | en_US |
| dc.subject | delivery | en_US |
| dc.subject | logistics | en_US |
| dc.subject | routing | en_US |
| dc.subject | optimization | en_US |
| dc.subject | data | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | research challenge | en_US |
| dc.subject | Amazon | en_US |
| dc.subject | e-commerce | en_US |
| dc.title | Technical Proceedings of the Amazon Last Mile Routing Research Challenge | en_US |
| dc.type | Other | en_US |
