A Short-Term Predict-Then-Cluster Framework for Meal Delivery Services

2025-01-11

AI Summary

This paper addresses two core challenges in on-demand food delivery: inaccurate short-term demand forecasting and suboptimal spatial partitioning for workforce dispatch at urban scale. To tackle these, we propose a synergistic optimization framework integrating short-horizon demand forecasting with geospatial clustering. Methodologically, we introduce a novel constrained dynamic clustering approach—CKMC/CCHC-ICE—that jointly models demand uncertainty (via quantile regression), geographic proximity, and operational hard constraints (e.g., capacity, coverage). Demand prediction employs XGBoost and LightGBM for multivariate point and quantile forecasts, while clustering combines constrained K-means with adjacency-enhanced hierarchical clustering. Empirical evaluation across multiple cities in Europe and Taiwan demonstrates an 18.7% reduction in forecast error and a 3.2× improvement in clustering efficiency. Simulation results further show that proactive dispatch of idle delivery resources improves average delivery time by 22.4%.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

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.

Problem

• short-term demand prediction
• urban area clustering
• delivery efficiency

Innovation

• Short-term Prediction
• Urban Zoning Algorithm
• Efficient Delivery Optimization