## 3 Results
### Practical Demonstration
Figure 10: Photo of the TRI tool assembly process, post-intervention. Figure 11: Tool assembly scenario at TRI after introduction of a mobile robot and augmented reality. Figure 12 shows the duration of eight production runs: four process instances with only the standard BPMS and four instances with the allocation extension enabled. To be clear, all eight process instances are with the HORSE System prototype, but four are without the agent allocation extension. Therefore, all eight instances serve as proof of the HORSE System's ability to orchestrate a manufacturing process with a mobile robot (KMR) and a human operator guided by augmented reality. Furthermore, the four instances on the right-hand side of Figure 12 shows further improvements that may be gained by adding a dynamic selection of agents based on process status. In such a case, the process management system dynamically decides, based on the process status, which agent (a human or a robot) should execute a task. In this specific case, the tasks done by the role 'tool collector' may either be executed by a human operator or by the mobile robot. The system decides on the fly to whom the task will be assigned to, based on the task requirements, agent capabilities, current availability, and the current process status. Although not conclusive proof, the results displayed in Figure 12 do suggest that process improvement may be gained from dynamic selection of agents to perform tasks. This suggestion is in line with the sentiment displayed by [79; 80; 81]. Delving into the durations of individual tasks sheds some light on the potential benefits. The results displayed in Figures 12-14 are directly extracted from the event log of the process management module of the HORSE System. Figure 12: Comparison of the tool assembly process duration comparison without dynamic agent allocation and with dynamic agent allocation enabled. Figure 13 shows the duration of assemble and disassemble tasks, performed by different agents. No significant difference can be found between the duration of tasks performed by the two groups of operators. Moving on to the fetch and return tasks, the durations are shown in Figure 14. In this case, a clear difference is visible between the fetch and return tasks performed by the mobile robot and the human operator. The difference in process duration shown in Figure 12 can be directly attributed to the difference in fetch and return task duration. Essentially, the agent allocation algorithm determined that the faster fetching and returning performed by the human is sometimes more supportive of the process objectives set by the process supervisor, e.g., in case the human operator is not busy (dis)assembling a tool and available to execute the fetch or return task. Although the results obtained from the demonstration are quite compelling, no process performance improvement can be proven yet. The sample size is too small, due to the lengthy duration of a single process execution. Nevertheless, the results do demonstrate that the HORSE System prototype delivers the expected orchestration of several agents and that potential process performance improvements are promising.