A Proximal Policy Optimization Approach for Food Delivery Problem with Reassignment Due to Order Cancellation

Abstract

Unexpected cancellation of food delivery orders poses significant challenges to resource allocation planning and could lead to reduced revenue for service providers. This paper addresses this issue by developing an optimization framework that can reassign canceled orders to alternative customers with the objectives of maximizing revenue and minimizing resource wastage. The problem is formulated as a route-based Markov decision process, termed the Dynamic Routing and Pricing Problem with Cancellation (DRPPC). A solution approach based on the proximal policy optimization strategy is introduced as a computationally effective way of solving the optimization problem with the use of reinforcement learning techniques. Experimental results demonstrate that the proposed computational method outperforms selected benchmark approaches with higher revenue from varied customer segments. This investigation advances the intersection of urban logistics and reinforcement learning, offering actionable strategies for enhanced operational resilience in food delivery services.

Keywords: Order cancellations, Markov Decision Process, Proximal policy optimization, Food Delivery Problem