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A Prescriptive Machine Learning Method for Courier Scheduling on Crowdsourced Delivery Platforms by Adam Behrendt; Martin Savelsbergh; He Wang is a Engineering article available to read on EtoBox.

What is A Prescriptive Machine Learning Method for Courier Scheduling on Crowdsourced Delivery Platforms about?

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.

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

Author

Adam Behrendt; Martin Savelsbergh; He Wang

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Published

2023

Language

EN

Field

Engineering (Physical Sciences)