Multi-Objective Evolutionary Optimization of Interpretable Rules for Smart Manufacturing Process Control
Keywords:
multi-objective optimization, evolutionary algorithms, interpretable rules, smart manufacturing, process control, explainable AI, governanceAbstract
The increasing complexity of modern manufacturing systems demands process control strategies that simultaneously reconcile multiple conflicting objectives while maintaining transparency for human decision-makers. This paper presents a comprehensive examination of multi-objective evolutionary optimization for the generation of interpretable rule sets in smart manufacturing process control. We articulate a systems-level framework that integrates concepts from multi-objective evolutionary computation, fuzzy rule-based systems, and explainable artificial intelligence to produce control policies that are both efficient and auditable. The discussion foregrounds the structural trade-offs among accuracy, interpretability, fairness, robustness, and sustainability, and delineates the architectural choices that influence the deployability of such models in real-world industrial infrastructures. By analyzing the governance implications of embedding evolutionary rule discovery into production environments, we highlight how algorithmic transparency can address regulatory and ethical requirements while enabling continuous adaptation to shifting operational conditions. The paper further explores the socio-technical dimensions of the approach, including the allocation of decision authority between autonomous controllers and human operators, the role of standards in ensuring interoperability, and the lifecycle concerns that arise when learned rules must be maintained across equipment generations. In doing so, we offer a forward-looking perspective on the design of intelligent, accountable, and sustainable manufacturing control systems that are capable of evolving in concert with their underlying production environments.
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