Advances in Modeling, Analysis, and Control of Manufacturing Systems: Methods, Gaps, and Research Frontiers

Abstract

Abstract. This article presents a structured review of system-level methodologies for modeling, analysis, and control of manufacturing systems. It categorizes and synthesizes a broad spectrum of approaches, including classical analytical methods (e.g., queuing theory and Markov processes), simulation-based techniques, and recent developments in artificial intelligence and machine learning. The review is organized around an integrated framework that emphasizes interdependencies among system components, such as machines, buffers, robots, and human operators. By comparing methods across modeling, performance evaluation, diagnostics, and production control, this article identifies key research gaps and emerging directions. The goal is to guide future work toward holistic, resilient, and adaptive manufacturing systems capable of meeting the demands of Industry 4.0 and beyond.