Joint Matching and Pricing for Crowd-shipping with In-store Customers
Arash Dehghan, Mucahit Cevik, Merve Bodur, Bissan Ghaddar

TL;DR
This paper introduces a joint matching and pricing model for crowd-shipping using in-store customers, employing advanced neural methods to optimize delivery efficiency and costs in urban last-mile logistics.
Contribution
It develops a novel MDP framework combined with neural approximation techniques for joint order assignment and dynamic pricing in crowd-shipping, addressing real-world uncertainties.
Findings
Achieves up to 6.7% cost savings over fixed pricing policies.
Flexible delivery delays reduce costs by 8%.
Multi-destination routing cuts operational costs by 17%.
Abstract
This paper examines the use of in-store customers as delivery couriers in a centralized crowd-shipping system, targeting the growing need for efficient last-mile delivery in urban areas. We consider a brick-and-mortar retail setting where shoppers are offered compensation to deliver time-sensitive online orders. To manage this process, we propose a Markov Decision Process (MDP) model that captures key uncertainties, including the stochastic arrival of orders and crowd-shippers, and the probabilistic acceptance of delivery offers. Our solution approach integrates Neural Approximate Dynamic Programming (NeurADP) for adaptive order-to-shopper assignment with a Deep Double Q-Network (DDQN) for dynamic pricing. This joint optimization strategy enables multi-drop routing and accounts for offer acceptance uncertainty, aligning more closely with real-world operations. Experimental results…
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Taxonomy
TopicsVehicle Routing Optimization Methods · Urban and Freight Transport Logistics · Transportation and Mobility Innovations
