A Short-Term Predict-Then-Cluster Framework for Meal Delivery Services
This work addresses operational optimization for on-demand meal delivery platforms, offering incremental improvements through a tailored framework that integrates forecasting and clustering.
This study tackles the problem of optimizing real-time operations for on-demand meal delivery services by proposing a short-term predict-then-cluster framework that uses ensemble-learning for demand forecasting and novel clustering methods (CKMC and CCHC-ICE) to generate dynamic clusters based on predicted demand and geographical proximity. Evaluations on European and Taiwanese case studies show the methods outperform traditional time series approaches in accuracy and computational efficiency, with a simulation study demonstrating significant enhancements in delivery efficiency through proactive strategies like idle fleet rebalancing.
Micro-delivery services offer promising solutions for on-demand city logistics, but their success relies on efficient real-time delivery operations and fleet management. On-demand meal delivery platforms seek to optimize real-time operations based on anticipatory insights into citywide demand distributions. To address these needs, this study proposes a short-term predict-then-cluster framework for on-demand meal delivery services. The framework utilizes ensemble-learning methods for point and distributional forecasting with multivariate features, including lagged-dependent inputs to capture demand dynamics. We introduce Constrained K-Means Clustering (CKMC) and Contiguity Constrained Hierarchical Clustering with Iterative Constraint Enforcement (CCHC-ICE) to generate dynamic clusters based on predicted demand and geographical proximity, tailored to user-defined operational constraints. Evaluations of European and Taiwanese case studies demonstrate that the proposed methods outperform traditional time series approaches in both accuracy and computational efficiency. Clustering results demonstrate that the incorporation of distributional predictions effectively addresses demand uncertainties, improving the quality of operational insights. Additionally, a simulation study demonstrates the practical value of short-term demand predictions for proactive strategies, such as idle fleet rebalancing, significantly enhancing delivery efficiency. By addressing demand uncertainties and operational constraints, our predict-then-cluster framework provides actionable insights for optimizing real-time operations. The approach is adaptable to other on-demand platform-based city logistics and passenger mobility services, promoting sustainable and efficient urban operations.