Data-Driven Forecasting and Optimization Framework for Efficient Courier Payout

Effective courier payout management requires accurate forecasting and strategic optimization to ensure financial efficiency and fair compensation. This study presents a data-driven framework that integrates machine learning-based parcel volume forecasting with an optimization model to improve payout planning in logistics. Using historical data from January 2022 to December 2024, we trained an ensemble forecasting model to predict daily parcel volumes at the cross-dock level for 2025. The forecasted demand was then incorporated into a mathematical optimization model that accounts for key cost factors, regional disparities, and operational constraints.Unlike traditional static pricing approaches, our methodology leverages clustering techniques to segment cross-docks based on operational characteristics, ensuring that payout structures reflect real-world workload variations. By integrating predictive analytics with an adaptable optimization framework, this study argues that courier payouts can be managed more systematically, addressing inefficiencies in existing pricing structures.