A Prior and Posterior Order Postponement Framework for the On-Demand Food Delivery Problem

The rapid expansion of the on-demand food delivery (OFD) market has led the service providers to manage large-scale dynamic order dispatching. Postponing the dispatch of orders is an effective strategy to alleviate the pressures from sudden order surges and to enhance decision-making quality. This paper addresses the OFD problem with order postponement to minimize travel distances and delayed deliveries, focusing on deciding which orders to postpone and which rider to assign for each remaining order at each decision point. We propose a prior and posterior postponement framework that separates the postponement decision-making process into two phases to balance computational efficiency and decision quality. In the prior phase, multiple knowledge-based postponement rules are designed to quickly filter out orders unsuitable for immediate dispatch. In the posterior phase, a data-driven postponement strategy using reinforcement learning is developed to further optimize long-term objectives. Particularly, an action-oriented phase-specific reward shaping method is designed by analyzing the intrinsic nature of the order postponement process, which helps customize the postponement duration for each order to achieve better postponement performance. Extensive numerical ablation and comparative experiments using real-world data demonstrate that the proposed postponement approach is able to improve customer satisfaction, delivery efficiency, and rider experience better than existing methods. Managerial insights are provided regarding the value of order postponement, key factors for designing effective postponement strategies, and practical ready-to-use postponement tactics.