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

Adam Behrendt(adam.behrendt***at***gatech.edu)
Martin Savelsbergh(martin.savelsbergh***at***isye.gatech.edu)
He Wang(he.wang***at***isye.gatech.edu)

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, i.e., to become scheduled couriers. We consider a scheduling problem that arises in such an environment, i.e., 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 offline 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 appriximation

Category 1: Applications -- OR and Management Sciences

Citation:

Download: [PDF]

Entry Submitted: 10/28/2021
Entry Accepted: 10/28/2021
Entry Last Modified: 10/28/2021

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