Fuzzy Control Decision-Making in Industrial Engineering: Mechanisms, Scenarios and Optimization Approaches

Fuzzy control decision-making (FCDM) is an intelligent paradigm that leverages linguistic variables to model knowledge, explicitly addressing epistemic uncertainty (incomplete system knowledge) and aleatoric uncertainty (stochastic noise), thereby enabling resolution of multi-objective optimization conflicts in complex industrial systems. Following the PRISMA protocol, this study conducts a systematic literature review of 123 peer-reviewed publications retrieved from IEEE Xplore, Web of Science, ScienceDirect, and Google Scholar over the period 1965–2026, with emphasis on developments in the past 15 years. Existing reviews predominantly focus on isolated subdomains (e.g., scheduling, maintenance, energy systems), lacking a unified cross-scenario synthesis and implementation framework for industrial FCDM. To address scalability challenges such as rule base explosion in high-dimensional spaces, the literature is analyzed with respect to hierarchical fuzzy architectures, rule pruning, and dimensionality reduction techniques. The primary contribution is a structured synthesis of FCDM mechanisms across four industrial domains, combined with a systematic examination of integration with Industrial Internet of Things (IIoT), Digital Twins, and Edge Analytics. Furthermore, a three-stage closed-loop framework is formalized as a unified optimization protocol and modular architecture with technical specifications for Industry 4.0 integration, comprising data preprocessing, fuzzy inference, and optimization-driven decision output with iterative feedback. Comparative evaluation against MILP, MPC, and DRL highlights the conditions under which FCDM provides superior robustness and interpretability.