TLDR: A research paper proposes a new AI-driven system combining Neural Approximate Dynamic Programming (NeurADP) for matching and Deep Double Q-Network (DDQN) for dynamic pricing to optimize crowd-shipping using in-store customers as couriers. This approach significantly reduces delivery costs, improves efficiency, and adapts to real-world uncertainties like order and shopper arrivals, and offer acceptance, offering a cost-effective solution for urban last-mile logistics.
The article discusses a new approach to last-mile delivery, focusing on “crowd-shipping” where in-store customers act as couriers. This system aims to make deliveries more efficient and cost-effective, especially in urban areas. The core idea is to leverage people who are already at a store and heading home to deliver online orders along their route.
The paper introduces a sophisticated model called a Markov Decision Process (MDP) to manage this system. This model accounts for various uncertainties, such as when new orders arrive, when potential couriers (shoppers) become available, and whether these shoppers will accept a delivery offer. To tackle these complexities, the researchers propose a combined strategy using two advanced AI techniques: Neural Approximate Dynamic Programming (NeurADP) for assigning orders to shoppers, and a Deep Double Q-Network (DDQN) for setting dynamic prices for these deliveries.
This integrated approach allows the system to make smart decisions about which orders to give to which shoppers and how much to pay them, even allowing for multiple deliveries per shopper. The goal is to minimize the store’s total operational costs, including payments to couriers and penalties for undelivered orders.
The research highlights that this joint optimization strategy significantly improves delivery cost efficiency. For instance, it can save up to 6.7% compared to systems with fixed pricing and about 18% over simpler, short-sighted approaches. The study also found that allowing flexible delivery times and enabling couriers to make multiple stops further reduces costs by 8% and 17% respectively.
The findings suggest that dynamic and forward-looking policies are crucial for successful crowd-shipping systems. This model offers practical guidance for businesses looking to improve their urban logistics, reduce congestion, and lower environmental impact by utilizing existing travel patterns. It provides a blueprint for retailers to enhance their delivery services, cut costs, and maintain customer satisfaction by tapping into the idle capacity of their in-store customers.
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You can read the full research paper for more technical details and experimental results: Joint Matching and Pricing for Crowd-shipping with In-store Customers.
