A Prescriptive Machine Learning Method for Courier Scheduling on Crowdsourced Delivery Platforms

Adam Behrendt (), Martin Savelsbergh () and He Wang ()
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Adam Behrendt: School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Martin Savelsbergh: School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
He Wang: School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332

Transportation Science, 2023, vol. 57, issue 4, 889-907

Abstract: Crowdsourced delivery platforms face the unique challenge of meeting dynamic customer demand using couriers not employed by the platform. As a result, the delivery capacity of the platform is uncertain. To reduce the uncertainty, the platform can offer a reward to couriers that agree to be available to make deliveries for a specified period of time, that is, to become scheduled couriers. We consider a scheduling problem that arises in such an environment, that is, in which a mix of scheduled and ad hoc couriers serves dynamically arriving pickup and delivery orders. The platform seeks a set of shifts for scheduled couriers so as to minimize total courier payments and penalty costs for expired orders. We present a prescriptive machine learning method that combines simulation optimization for off-line training and a neural network for online solution prescription. In computational experiments using real-world data provided by a crowdsourced delivery platform, our prescriptive machine learning method achieves solution quality that is within 0.2%–1.9% of a bespoke sample average approximation method while being several orders of magnitude faster in terms of online solution generation.

Keywords: crowdsourced same-day delivery; machine learning; sample average approximation (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:57:y:2023:i:4:p:889-907

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