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

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. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Funding: This work was supported in part by the National Science Foundation [Grant 2145661].