## 4 Human-Machine-Logistics Collaborative Scheduling Method Considering Workers' LFE
### Adaptive Real-Time Scheduling Considering Workers' Learning and Forgetting Effects
#### 4.3.1 Adaptive Real-Time Allocation Considering Workers' Learning and Forgetting Effects  
The real-time matching between task demands and service combinations is the essence of adaptive scheduling for human-machine-logistics collaboration and is central to implementing multi-resource task allocation. Based on the division of manufacturing resource groups, this method selects the optimal service group as the task service provider for real-time tasks, aiming to enhance the ability of multi-resource collaboration to respond to disturbances and improve the efficiency of manufacturing execution. In selecting the optimal service group, the worker's work efficiency, which is affected by learning and forgetting effects, must be considered as a key factor influencing the service group's capability. Based on prior research [39], this paper proposes an adaptive scheduling method considering the learning and forgetting effects of workers. The proposed algorithm is a real-time task allocation method based on minimizing service cost and dynamic priority. It is also an adaptive weighted task allocation strategy, aiming to match human-machine-logistics combinations with real-time tasks while considering the impact of workers' learning and forgetting effects. The method is described as follows. For a real-time task, the states of the production resource set and logistics resource set are as follows:  
\[\overline{G}^{t}=\left\{g_{j}^{t}\Big{|}g_{j}^{t}=\left(h_{j}^{t},m_{j}^{t} \right),j=1,\ 2,\ \ldots,\ \big{\}}\right\} \tag{15}\]  
\[\overline{A}^{t}=\left\{a_{i}^{t}\big{|}i=1,\ 2,\ \ldots,\ 1\right\} \tag{16}\]  
Figure 2: Real-Time Self-Organization of Human–Machine–Logistics Resources Based on D2D Interactions. At time \(t\) the set of service groups that meet the requirements of task can be obtained \(RT_{Z}\):  
\[RT_{Z}=\big{\{}\big{(}g_{j},a_{i}\big{)}\big{|}g_{j}\in\overline{C},a_{i}\in \overline{A}\big{\}} \tag{17}\]  
Assume it is a set of service combinations. Among them, \(RT_{Z}=\{rt_{z}|z=1,\,2,\,\ldots,\,Z\}\), \(rt_{z}=\big{(}g_{j},a_{i}\big{)}=\big{(}h_{j},m_{j},a_{i}\big{)}\), \(Z=I\times J\). In human-machine collaborative work, the service time of the machine tool changes with the worker's efficiency, and the worker's efficiency is influenced by their individual learning and forgetting effects. At time t, based on the worker real-time state model and the corresponding Formula (1), the actual service time of the task on the processing equipment can be obtained:  
\[p_{nj}^{tk}=p_{nj}^{k}\bigg{\{}max\bigg{[}1+\big{(}T_{j}^{cum}\big{)}^{\frac{ \mu^{t,l}}{j}},\theta_{j}\bigg{]}-1\bigg{\}}e^{-p_{j}^{k}T_{j}^{l=cum}}+p_{nj} ^{k} \tag{18}\]  
Furthermore, we can obtain that the worker's real-time processing time is \(p_{nj}^{tk}\) and the machine's actual processing energy consumption is \(e_{j}^{act}p_{nj}^{tk}\). Assume that task is processed on machine and task will be processed on machine \(m_{j}\). Assume that an AGV provides transportation services for task and \(L_{i}^{pos}\) is the position of the AGV when it starts executing task \(O_{n}^{k}\). The distance between machine \(m_{j}\) and is denoted as \(Dist\Big{(}L_{j}^{pos},L_{i}^{pos}\Big{)}\). The time cost for the AGV to execute the pickup task is represented as: \(p_{nil}^{tta}=Dist\Big{(}L_{j}^{pos},L_{i}^{pos}\Big{)}/\overline{v}\). The time cost for the AGV to execute the delivery task is represented as \(p_{nil}^{ksen}=Dist\Big{(}L_{j}^{pos},L_{j}^{pos}\Big{)}/\overline{v}\). The total transportation time cost is \(p_{ni}^{k}=p_{nil}^{tta}+p_{nil}^{ksen}\). The transportation energy consumption is \(e_{i}^{act}p_{ni}^{k}\).