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

arXiv: https://arxiv.org/abs/2507.01749 · HTML: https://mlsanity.com/paper/2507.01749/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [Neural Approximate Dynamic Programming for On-Demand Ride-Pooling](https://mlsanity.com/paper/1911.08842/) (2019)
- [A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching using Deep Reinforcement Learning](https://mlsanity.com/paper/2010.01755/) (2020)
- [Approximate Dynamic Programming for Planning a Ride-Sharing System using Autonomous Fleets of Electric Vehicles](https://mlsanity.com/paper/1810.08124/) (2018)
- [Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage using Approximate Dynamic Programming](https://mlsanity.com/paper/1402.3575/) (2014)
- [Dynamic Pricing and Matching for Two-Sided Queues](https://mlsanity.com/paper/1911.02213/) (2019)
- [PassGoodPool: Joint Passengers and Goods Fleet Management with Reinforcement Learning aided Pricing, Matching, and Route Planning](https://mlsanity.com/paper/2011.08999/) (2020)
- [Two-stage Stochastic Matching and Pricing with Applications to Ride Hailing](https://mlsanity.com/paper/2210.11648/) (2022)
- [Future Aware Pricing and Matching for Sustainable On-demand Ride Pooling](https://mlsanity.com/paper/2302.10510/) (2023)
- [Neural Approximate Dynamic Programming for the Ultra-fast Order Dispatching Problem](https://mlsanity.com/paper/2311.12975/) (2023)
- [Joint Pricing and Matching for Resource Allocation Platforms via Min-cost Flow Problem](https://mlsanity.com/paper/2404.19241/) (2024)

## Abstract (arXiv)

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.