## 4 Human-Machine-Logistics Collaborative Scheduling Method Considering Workers' LFE
### Real-Time Self-Organization of Human-Machine-Logistics Resources for Real-Time Tasks  
In the dynamic environment of an intelligent production workshop, the collaborative logic between manufacturing resources is complex and variable. This paper considers not only processing resources and logistics resources but also the factor of workers. The impact of workers' learning and forgetting effects further complicates the dynamic properties of all manufacturing resources in the workshop, making it difficult to predict and increasing the flexibility needed for the dynamic reorganization of manufacturing resources. To clearly describe the dynamic self-organization of individual manufacturing resources for tasks based on their current status and the uncertainty of task arrival in production workshops, a human-machine-logistics dynamic self-organization method for real-time tasks is proposed. This method is based on D2D technology and is guided by real-time task demands in the workshop. It integrates and matches available service resources according to the service capabilities of human-machine-logistics, achieving real-time self-organization of resources for specific tasks.  
The construction of the self-organization method for human-machine-logistics resources involves two components:  
Self-Organization Strategy for Real-Time Tasks: When a workpiece enters the workshop, all its operations are divided into individual tasks. Each operation task requires a set of services: one logistics service and one processing service (the processing service requires both a worker and a machine). Each operation task requires a worker, a machine, and an AGV to provide production and logistics services.  
Real-Time Self-Organization of Human-Machine-Logistics Resources Based on D2D Interactions: The resource interaction mode in the intelligent workshop is shown in Figure 2, where tasks and represent two consecutive operations of task \(k\). The completion of each operation requires a service group composed of a production machine, a worker, and a logistics device to provide support. When facing the real-time task, the scheduling center can query the available production resource set and the available logistics resource set based on the task type. Machines initiate communication with logistics units via D2D technology and quickly form service groups based on real-time information and their respective service capabilities. Obtain the set of service groups that meet the service requirements of task \(O_{n}^{k}\). The most suitable service group is selected to execute task \(O_{n}^{k}\). Before the completion of task, the processing resource publishes the next task \(O_{n+1}^{k}\). The D2D-based human-machine-logistics self-organization mechanism includes four key elements: a service object, task publisher, production service provider, and logistics service provider. The service group formation process also includes two roles: Group Leader and Group Member. The Group-Leader is responsible for gathering Group Members, while Group Members provide feedback on task costs to the Group Leader. Production resources, typically fixed equipment connected to industrial buses, are less costly in terms of communication overhead. Therefore, the production resource in the service group for the current operation acts as the Group Leader and the task publisher for the next operation \(O_{n}^{k}\). After completing the collaborative task, the service group dissolves, and the resources are released for reallocation to new tasks.