Joint Matching and Pricing for Crowd-shipping with In-store Customers

• URL: http://arxiv.org/abs/2507.01749v1
• Date: Wed, 02 Jul 2025 14:27:32 GMT
• Title: Joint Matching and Pricing for Crowd-shipping with In-store Customers
• Authors: Arash Dehghan, Mucahit Cevik, Merve Bodur, Bissan Ghaddar,
• Abstract summary: This paper examines the use of in-store customers as delivery couriers in a centralized crowd-shipping system.<n>We propose a Markov Decision Process (MDP) model that captures key uncertainties, including the arrival of orders and crowd-shippers.<n>We show that the integrated NeurADP + DDQN policy achieves notable improvements in delivery cost efficiency.
• Score: 2.7950888004779064
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 demonstrate that the integrated NeurADP + DDQN policy achieves notable improvements in delivery cost efficiency, with up to 6.7\% savings over NeurADP with fixed pricing and approximately 18\% over myopic baselines. We also show that allowing flexible delivery delays and enabling multi-destination routing further reduces operational costs by 8\% and 17\%, respectively. These findings underscore the advantages of dynamic, forward-looking policies in crowd-shipping systems and offer practical guidance for urban logistics operators.

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