Dynamic Pricing for Multi-Modal Meal Delivery Using Deep Reinforcement Learning

In this paper, we develop a dynamic pricing mechanism for a meal delivery platform that offers multiple transportation modes for order deliveries. We consider orders from heterogeneous customers who select their preferred delivery mode based on individual generalized cost (GC) functions, where GC captures the trade-off between price and delivery latency for each transportation option. Given the logistics of the underlying transportation network, the platform can utilize a pricing mechanism to guide customer choices toward delivery modes that optimize resource allocation across available transportation modalities. By accounting for variability in the latency and cost of modalities, such pricing aligns customer preferences with the platform’s operational objectives and enhances overall satisfaction. Due to the computational complexity of finding the optimal policy, we adopt a deep reinforcement learning (DRL) approach to design the pricing mechanism. Our numerical results demonstrate up to 143% higher profits compared to heuristic pricing strategies, highlighting the potential of DRL-based dynamic pricing to improve profitability, resource efficiency, and service quality in on-demand delivery services.