Simulation Budget Allocation for Improving Scheduling and Routing of Automated Guided Vehicles in Warehouse Management

Simulation budget allocation is a widely used technique for evaluating and optimizing dynamic discrete event stochastic system via efficient sampling. In warehouse management, the scheduling and routing algorithms of automated guided vehicles aim to optimize order assignments and travel paths under certain constraints to achieve specific objectives. However, these algorithms often rely on deterministic optimization methods, neglecting the dynamic and stochastic nature of warehouse management systems. In this work, we propose an efficient method that integrates simulation budget allocation methods with deterministic scheduling and routing algorithms to enhance overall system performance. The proposed method leverages the benefits of both simulation optimization and deterministic optimization techniques, accounting for the inherent uncertainty of the system. We adopt a discrete event simulation model used in the Case Study Competition of the 2022 Winter Simulation Conference, where the objective is to minimize the adjusted average order cycle time (ACT) in a given warehouse simulation scenario. Numerical examples demonstrate that the use of simulation budget allocation methods can significantly further reduce the ACT, thereby highlighting the effectiveness of our proposed method.