Leveraging digital twin and dynamic scheduling for enhanced human–robot collaboration

Industry 5.0 represents a paradigm shift toward human-centric, resilient, and sustainable production systems. At the core of this transformation lies digital twins, which enable predictive and prescriptive analytics in real time, improving decision-making capabilities such as visibility, transparency, and collaboration. By integrating advanced AI algorithms for data interpretation and facilitating seamless human–machine interactions, digital twins address critical challenges in modern industrial systems. This article explores the transformative role of digital twins in operational decision-making, focusing on their ability to optimize workflows, and foster collaboration between humans and robots. Through a dual-layer methodology macro-level task scheduling for efficiency and consideration of human factors and micro-level real-time control for adaptability, digital twins offer a powerful framework for aligning human and robotic capabilities while mitigating human fatigue and improving decision transparency. Highlighting applications in digital transformation, optimization, and human–AI collaboration, this study emphasizes how digital twins enhance operational visibility and resilience. The findings contribute to the evolution of Industry 5.0, offering innovative solutions for integrating predictive models and human-centered approaches in decision-making, redefining the future of sustainable and collaborative industrial systems.