Energy-Efficient Design and Control of AS/RS for Industry 4.0

Abstract

The evolution of Automated Storage and Retrieval Systems (AS/RS) into intelligent, energy-aware cyber-physical
systems marks a turning point in intralogistics. This paper presents a structured review of modeling, optimization,
and control strategies for AS/RS, focusing on the convergence of kinematic performance, artificial intelligence (AI),
and carbon reduction objectives. We first revisit foundational travel-time and retrieval-time models before
highlighting how recent advances integrate simulation, metaheuristics, and reinforcement learning for adaptive
scheduling. AS/RS are then analyzed as cyber-physical systems enabled by digital twins, supporting real-time
decision-making and energy management. Particular attention is given to energy-saving strategies, including
regenerative hardware, AI-based routing, and sustainable layout configurations. Finally, the paper outlines key
research challenges—interoperability, AI robustness, and system-level sustainability—and proposes future directions
to close the loop between design, operation, and environmental performance. By synthesizing over 40 recent studies,
this work provides a comprehensive framework for designing next-generation AS/RS aligned with Industry 4.0 and
low-carbon logistics goals.