Michael Wagner and co-author receive INFORMS award
Wagner, an associate professor at the University of Washington and an Amazon Scholar, wins the Urban Transportation Outstanding Paper Award.
A paper co-authored by Michael Wagner, associate professor of operations management at the University of Washington and an Amazon Scholar, has won the Urban Transportation Outstanding Paper Award from the INFORMS Transportation Science and Logistics (TSL) Society.
Wagner, a Neal and Jan Dempsey Fellow at the Foster School of Business, wrote “Crowdsourcing Last-Mile Deliveries” with his former doctoral student, Soraya (Nadia) Fatehi. Today, Fatehi is assistant professor of operations management in the Naveen Jindal School of Management at University of Texas at Dallas. Their paper, which will be published in the Manufacturing & Service Operations Management Journal, proposes “a novel robust crowdsourcing optimization model to study labor planning and pricing for crowdsourced last-mile delivery systems that are utilized for satisfying on-demand orders with guaranteed delivery time windows.”
“This paper shows that crowdsourcing deliveries can be effectively used to quickly get packages to customers in a cost-efficient manner while also providing drivers healthy compensation,” Wagner said.
INFORMS, or the Institute for Operations Research and the Management Sciences, held its annual meeting Oct. 24 to 27, and is “an international society for practitioners in the fields of operations research, management science, and analytics.” The TSL Society provides “a sustained, specialized focus on all topics of transportation science and logistics, including current and potential problems and contributions to their solution.”
Wagner joined Amazon as a Scholar in June 2020 in the Amazon Flex organization where his work involves combining his interests in crowdsourcing and optimization to help Amazon meet its delivery promises.
Wagner is an MIT graduate where he earned bachelor’s degrees in mathematics and electrical engineering/computer science, a masters in electrical engineering/computer science, and a PhD in operations research. His current research focuses on crowdsourcing and optimization under uncertainty, including dynamic programming, reinforcement learning, and robust optimization. His research has appeared in Management Science, Operations Research, Manufacturing & Service Operations Management, Mathematics of Operations Research, and Mathematical Programming.