## 4 Human-Machine-Logistics Collaborative Scheduling Method Considering Workers' LFE
### Adaptive Real-Time Scheduling Considering Workers' Learning and Forgetting Effects
#### 4.3.2. AGV Conflict-Handling Mechanism  
In intelligent workshops, AGVs and machines handle transportation and processing tasks in a multi-task environment. In such environments, it is possible that multiple tasks may simultaneously select the same AGV and machine, resulting in resource contention. However, since machines are fixed resources and have buffers as storage units, even when multiple tasks contend for the same machine, as long as different AGVs are responsible for transportation, each task can still be transported to the current optimal machine in the first instance. The presence of the buffer ensures that this situation does not cause task blocking, so the overall scheduling is only slightly affected.  
In contrast, AGVs, being mobile resources with limited capacity, cannot handle multiple tasks simultaneously. When multiple tasks select the same AGV for transportation, the AGV can only process these tasks sequentially. This not only leads to task blocking but also changes the slackness of subsequent tasks due to the transportation delay of the preceding task. This can cause the selected service group to no longer be the optimal choice, having a more significant impact on the overall scheduling process.  
To resolve this issue, a resource conflict handling mechanism is proposed in this paper. By reasonably allocating AGV resources, the system ensures that higher-priority tasks are handled first, effectively avoiding resource conflicts and task delays.  
When multiple tasks in the scheduling pool are assigned to the same AGV for logistics execution, the system selects the task with the highest urgency based on the task priority selection rule. At time \(t\), the priority of task is calculated as  
\[F_{0}^{t}=\theta_{O_{n}^{k}}\times\frac{T_{O_{n}^{k}}^{tf}-T_{min}^{tf}}{T_{ max}^{tf}-T_{min}^{tf}}+(1-\theta_{O_{n}^{k}})\times\frac{W_{O_{n}^{k}}^{tf}-W_{ min}^{tf}}{W_{max}^{tf}-W_{min}^{tf}} \tag{28}\]  
where represents the slackness of task at time \(t\), \(T_{O_{n}^{k}}^{tf}\) represents the processing time of task and \(W_{O_{n}^{k}}^{tf}\) represents the energy consumption for processing task \(O_{n}^{k}\). \(T_{min}^{tf}\) and \(W_{min}^{tf}\) represent the minimum service time and energy consumption among all tasks waiting to be executed at time \(t\), while \(T_{max}^{tf}\) and \(W_{max}^{tf}\) represent the maximum values.  
This mechanism ensures that the most urgent tasks are processed promptly, avoiding delays in important tasks due to resource conflicts. For tasks that are not selected, the system unbinds them from the current AGV and returns them to the scheduling pool for reallocation. These tasks' statuses are updated based on real-time feedback and are re-entered into the resource allocation process based on resource availability. This mechanism ensures that not selected tasks are handled flexibly, preventing long-term blocking in resource contention. After being returned to the scheduling pool, the system continuously monitors the resource status of the task. If the resource status changes (e.g., another AGV becomes available), the system re-evaluates the not selected task, adjusts its priority, and reallocates resources according to real-time conditions.