INDUSTRIAL AND SYSTEMS OPTIMIZATION IN THE ERA OF INTELLIGENT, RESILIENT, SUSTAINABLE, AND HUMAN-CENTERED INDUSTRY: AN EDITORIAL INSIGHT

Information systems research

Decision Intelligence in Collaborative Intelligent Manufacturing of smart and sustainable materials: A Comprehensive Review of Artificial Intelligence, Optimization, Digital Twins, and Human-Centered Decision-Making

The rapid transition from Industry 4.0 to Industry 5.0 has significantly increased the complexity of manufacturing systems while accelerating the development and deployment of smart and sustainable materials in advanced industrial applications. These emerging materials require intelligent decision-making approaches that integrate data, advanced analytics, optimization, digital twins, and human expertise throughout their design, production, monitoring, and lifecycle management. Although artificial intelligence, machine learning, digital twins, and mathematical optimization have independently advanced intelligent manufacturing, their isolated application often limits adaptability, explainability, and resilience. This study presents a systematic literature review of Decision Intelligence (DI) in collaborative intelligent manufacturing following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, synthesizing evidence from 140 peer-reviewed publications. The review comprehensively examines the evolution of Decision Intelligence, core enabling technologies, layered architectures, industrial applications, human-centered decision-making, implementation challenges, and emerging research trends. The findings indicate that effective Decision Intelligence requires the seamless integration of artificial intelligence, optimization, digital twins, knowledge graphs, explainable AI, and Human-in-the-Loop AI within a unified decision ecosystem that combines computational intelligence with human judgment. The proposed framework and research roadmap provide valuable theoretical insights and practical guidance for developing resilient, explainable, and human-centric Decision Intelligence systems that support the realization of Industry 5.0 manufacturing for smart and sustainable materials.

Stability- and Safety-Guaranteed Artificial Intelligence: Enhancing Renewable-Dominated Power System Operation and Control

As renewable energy becomes the dominant generation resource in modern power systems, system stability and safety pose critical challenges due to increased uncertainty, reduced inertia, and escalating system complexity. Artificial intelligence (AI) provides unprecedented capabilities for real-time control, predictive optimization, and adaptive decision making, yet its adoption in safety-critical power system applications is hampered by concerns over robustness, interpretability, and the absence of formal guarantees. This article outlines how stability- and safety-guaranteed AI approaches can bridge this gap to enable reliable integration of renewables. In this article, we review emerging methods that embed physics, control theory, and optimization constraints in AI models for power systems, discuss advances in certifiable robustness and Lyapunov-based learning, and chart the path for deployment in renewable-dominated grids. The article concludes with policy, regulatory, and research recommendations for ensuring that AI not only accelerates the clean energy transition but also preserves resilience and trust in critical infrastructure.

Modeling in the Era of AI-Driven Industrial Automation and Optimization

It is argued that first-principles models (FPMs) remain essential for mission- and business-critical workflows in industrial automation, both in process design and operations and hybrid intelligence integrates mechanistic rigor with data-driven insights to deliver smarter design, safer operations, and more sustainable processes.

Sustainable and Intelligent Machining of Advanced Materials: Emerging Trends and Future Directions

The rapid evolution of manufacturing technologies has intensified the need to align machining practices with sustainability and digital intelligence. Conventional machining processes, while productive, often result in high energy consumption, material waste, and limited adaptability to dynamic industrial demands. This review addresses the growing research gap in integrating sustainability principles with intelligent manufacturing systems for advanced materials. The primary objective of this study is to analyze and synthesize emerging trends, technologies, and frameworks that enable sustainable and intelligent machining. The review systematically examines recent advancements in Artificial Intelligence (AI), the Internet of Things (IoT), Additive and Hybrid Manufacturing, Digital Twins, and Cyber-Physical Systems (CPS). The methodology involves a comprehensive literature analysis of more than 130 peer-reviewed studies published between 2000 and 2025, emphasizing quantifiable sustainability metrics such as energy efficiency, CO2 emission reduction, and waste minimization. Key findings reveal that AI-driven predictive analytics, IoT-enabled monitoring, and additive-hybrid manufacturing platforms significantly enhance operational efficiency and resource utilization, while digital twin frameworks support real-time process optimization and lifecycle-based sustainability evaluation. A comparative evaluation highlights that these technologies collectively contribute to energy savings of up to 40%, CO2 reductions of 25%-35%, and waste minimization of 50% in selected industrial applications. The paper concludes with strategic recommendations for future research, including the standardization of sustainability metrics, transparent and explainable AI integration, and the development of unified digital ecosystems that combine data-driven intelligence with circular economy principles. This review thus provides a consolidated roadmap for achieving environmentally responsible, intelligent, and future-ready machining systems.

A REVIEW AND COMPARATIVE ANALYSIS OF AUTOMATED PROCESS CONTROL METHODS IN MODERN MANUFACTURING WITHIN THE CONTEXT OF INDUSTRY 5.0

Relevance, topic and main objective. The development of automated process control systems (APCS) is a critical factor for ensuring stability, efficiency, resilience, and human-centric operation in modern manufacturing, particularly within the emerging framework of Industry 5.0. This article provides a structured review and comparative analysis of classical and intelligent APCS methods based on extended criteria such as control accuracy, adaptivity, computational efficiency, sustainability impact, resilience, integrability, explainability, and human-in-the-loop compatibility. The main objective is to evaluate and classify the principal control approaches and highlight their evolution toward hybrid architectures integrating machine learning, digital twins, and advanced operator-interaction mechanisms. Methods. The study applies a multi-criteria analytical framework informed by theoretical research, industrial reports, and documented implementations in manufacturing systems. The analysis incorporates modernized controller models, including an intelligent PID loop, a multi-objective MPC scheme supported by digital-twin-based prediction, and a neural-network-based architecture enhanced with explainable AI and resilience management. Results. The findings show that none of the examined APCS methods is universally optimal. PID remains effective for stable, well-characterized processes; MPC excels in multivariable, constraint-dominated environments; fuzzy and adaptive systems offer flexibility for uncertain conditions; neural networks demonstrate strong nonlinear modeling and fault tolerance but require substantial computational and data resources. Conclusions. Each method exhibits context-dependent strengths, and the most promising direction for APCS development lies in hybrid solutions that integrate classical techniques with intelligent, interpretable, and digitally interconnected components. Future research should focus on unifying enhanced PID, MPC, and neural-network-based controllers into a single hybrid architecture suitable for adaptive, transparent, and resource-efficient control in Industry 5.0 environments.