Simulation-Based Validation of Machine Learning Optimization Models in Warehouse Operations

Abstract

Deploying machine learning optimization models in warehouse operations carries significant operational risk, as suboptimal decisions can cascade into fulfillment failures, labor inefficiencies, and customer experience degradation at scale. Traditional offline evaluation metrics such as accuracy, RMSE, or reward convergence are insufficient to capture real-world operational constraints such as inventory bottlenecks, stochastic order arrivals, picking congestion, labor shift variability, and equipment limitations. This paper presents a simulation-based validation methodology for machine learning (ML) optimization models prior to production deployment in warehouse environments. The proposed framework integrates discrete event simulation (DES) with ML-based decision policies to evaluate system-level performance under realistic operational conditions. Unlike conventional validation pipelines that rely solely on historical datasets, the proposed approach embeds ML models into a virtual warehouse environment where order fulfillment processes, storage allocation, and picking strategies are dynamically simulated. The study emphasizes risk quantification through scenario-based stress testing, where variations in order volume, SKU distribution, labor availability, and equipment downtime are introduced systematically. Key performance indicators (KPIs) such as order cycle time, throughput rate, picker utilization, and SLA violation rate are measured across multiple simulation runs to assess robustness. A hybrid architecture combining reinforcement learning-based optimization and stochastic simulation is introduced. The model evaluates policy decisions at each decision epoch, while the simulation environment provides feedback in the form of system state transitions. This enables iterative validation before real-world deployment. The results demonstrate that simulation-based validation significantly reduces deployment risk by identifying performance degradation scenarios that are not observable in offline training metrics. Furthermore, the framework enables early detection of policy instability under peak load conditions, improving operational resilience. The contribution of this work lies in bridging the gap between ML optimization theory and warehouse operational realities by introducing a structured validation pipeline that ensures safer deployment of intelligent systems in supply chain environments. The approach provides a scalable methodology for enterprise-grade warehouse automation systems where operational errors carry high financial and service-level costs.