Improving Fleet Size Estimation for Factory Logistics Under Demand, Speed, and Efficiency Uncertainty

Open Research Europe, Journal Year: 2025, Volume and Issue: 5, P. 79 - 79

Published: March 25, 2025

In material handling systems, estimating the required fleet size of automated guided vehicles (AGVs) is a significant task when designing processes to ensure efficient demand fulfillment. Analytic estimation methods typically assume that key input variables – such as AGV speed, loading / unloading times, transportation between stations, efficiency and availability coefficients are deterministic averages, overlooking variability found inherent in real-world operational conditions. In contrast, we investigate potential benefits modeling parameters random with known means variances, account for this uncertainty. The core method lies expanding functions terms deviations from their mean values. Taking expectations, expansions expressed central moments mixed moments. process facilitated by introduction two linear operators, which streamline development method. We thus derive second-order approximations expected value variance number AGVs, providing more accurate reliable estimates. effectiveness approach demonstrated through Monte Carlo simulations, comparing results empirical data several case studies. has been implemented "Agent Optimization" software system, developed part Grow your manufacturing business (Better Factory) Horizon 2020 project.

Language: Английский