###### Abstract  
In the era of Industry 5.0, human-centric manufacturing necessitates deep integration between workers and intelligent workshop scheduling systems. However, the inherent variability in worker efficiency due to learning and forgetting effects poses challenges to human-machine-logistics collaboration, thereby complicating multi-resource scheduling in smart workshops. To address these challenges, this study proposes a real-time task-driven human-machine-logistics collaborative framework designed to enhance multi-resource coordination in smart workshops. First, the framework incorporates a learning-forgetting model to dynamically assess worker efficiency, enabling real-time adjustments to human-machine-logistics resource states. Second, a task-driven self-organizing approach is introduced, allowing human, machine, and logistics resources to form adaptive groups based on task requirements. Third, a task slack-based matching method is developed to facilitate real-time, adaptive allocation of tasks to resource groups. Finally, the proposed method is validated through an engineering case study, demonstrating its effectiveness across different order scales. Experimental results indicate that, on average, completion time is reduced by no less than 10%, energy consumption decreases by at least 8%, and delay time is reduced by over 70%. These findings confirm the effectiveness and adaptability of the proposed method in highly dynamic, multi-resource production environments.  
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[color=gray!20,] [color=gray!20,] [color=gray!allocated and managed in real time through cloud platforms, enhancing collaboration. The dynamic interaction among workers, production equipment, and logistics creates a complex and evolving system, adding to the challenges of resource scheduling.  
To manage the complexity and uncertainty caused by fluctuations in worker efficiency, scheduling systems must be flexible and adaptive, allowing for quick adjustments in resource allocation and scheduling strategies in changing environments. Developing accurate and dynamic scheduling models is crucial. These models help smart scheduling systems respond effectively to complex production demands and environmental changes, optimizing the coordination of production, logistics, and human resources to improve efficiency. They also offer adaptable solutions for smart manufacturing systems, enhancing responsiveness and sustainability.  
Most current smart workshop scheduling systems focus primarily on production [8] and logistics [9] resources, overlooking the crucial role of workers. Without a deep understanding of worker efficiency fluctuations and their dynamic nature, these systems struggle to incorporate the human-centric principles of Industry 5.0, making them inadequate for meeting the needs of smart workshops in complex environments.  
To address these issues, this paper proposes a real-time human-machine-material collaborative scheduling method considering workers' learning and forgetting effects (HMLCS). The real-time scheduling method includes three main components: First, a mathematical model of learning and forgetting is used to calculate workers' real-time efficiency, which updates the real-time state of manufacturing resources accordingly. Second, the real-time state of human-machine-material resources is modeled, and a method for grouping these resources is proposed. Finally, an adaptive scheduling algorithm that accounts for worker learning and forgetting is presented. The paper is structured into three main parts: (1) building a collaborative scheduling framework for smart workshops that considers worker learning and forgetting; (2) proposing a real-time scheduling method considering these effects; and (3) validating the proposed strategy through a case study of a coal machinery company's workshop using a simulation platform built with Tecnomatix Plant Simulation software.