An Integrated Queuing Network Model for Optimizing Multi-Level AVS/RS Performance in Multi-Floor Manufacturing Environments

Abstract

The advancement of intelligent manufacturing has significantly increased the complexity and demand for efficient automated logistics systems in multi-floor manufacturing and warehousing environments. This paper presents a novel integrated queuing network model for automated vehicle storage and retrieval systems (AVS/RS) that allows for rapid performance estimation in multi-level and multi-depth configurations. The model bridges the gap between traditional simulation and analytical approaches, enabling quick evaluations during the design phase. The study conducts numerical experiments and simulation verifications to assess the impact of different rack configurations on system performance. Key findings include the optimal horizontal and vertical design of racks, showing that increasing the number of aisles and sub-tiers can enhance throughput and reduce cycle time. Additionally, it is shown that a lower number of main tiers leads to better performance, even with the same total number of tiers. The results offer valuable insights for designing efficient AVS/RS, contributing to improved flexibility and efficiency in automated logistics systems. This research has practical implications for optimizing storage solutions in complex, multi-floor manufacturing environments, ultimately enhancing operational efficiency.

Keywords

• Approximation algorithms
• analytical models
• integrated manufacturing systems
• material storage
• production
• queueing analysis