OpComm: A Reinforcement Learning Framework for Adaptive Buffer Control in Warehouse Volume Forecasting

2025-12-17

AI Summary

To address resource misallocation and delivery delays in last-mile logistics caused by inaccurate parcel volume forecasting at distribution stations, this paper proposes a closed-loop “Prediction–Decision–Feedback–Explanation” framework. It employs LightGBM for high-accuracy demand forecasting; designs a context-aware reinforcement learning model based on Proximal Policy Optimization (PPO), incorporating an asymmetric reward mechanism to optimize dynamic buffer allocation; introduces a novel generative explainability module that integrates SHAP-based feature attribution with large language models to enable policy traceability and human-AI collaboration; and incorporates Monte Carlo feedback for online, adaptive policy updating. Evaluated across 400+ real-world stations, the framework reduces Weighted Absolute Percentage Error (WAPE) by 21.65%, significantly mitigates under-buffering incidents, and enhances operational transparency and decision responsiveness.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Online Learning & Bandits

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Accurate forecasting of package volumes at delivery stations is critical for last-mile logistics, where errors lead to inefficient resource allocation, higher costs, and delivery delays. We propose OpComm, a forecasting and decision-support framework that combines supervised learning with reinforcement learning-based buffer control and a generative AI-driven communication module. A LightGBM regression model generates station-level demand forecasts, which serve as context for a Proximal Policy Optimization (PPO) agent that selects buffer levels from a discrete action set. The reward function penalizes under-buffering more heavily than over-buffering, reflecting real-world trade-offs between unmet demand risks and resource inefficiency. Station outcomes are fed back through a Monte Carlo update mechanism, enabling continual policy adaptation. To enhance interpretability, a generative AI layer produces executive-level summaries and scenario analyses grounded in SHAP-based feature attributions. Across 400+ stations, OpComm reduced Weighted Absolute Percentage Error (WAPE) by 21.65% compared to manual forecasts, while lowering under-buffering incidents and improving transparency for decision-makers. This work shows how contextual reinforcement learning, coupled with predictive modeling, can address operational forecasting challenges and bridge statistical rigor with practical decision-making in high-stakes logistics environments.

Problem

• Forecasts package volumes at delivery stations to reduce errors
• Uses reinforcement learning to adaptively control buffer levels
• Enhances interpretability with AI summaries for decision support

Innovation

• LightGBM regression for station-level demand forecasting
• PPO agent with penalized reward for buffer control
• Generative AI layer for interpretability and summaries