# Deep Reinforcement Learning for Selection of Dispatch Rules for Scheduling of Production Systems
## 4 Industrial Pilot Case  
### Pilot Case Description  
A real production system from the bicycle industry is employed in order to validate and evaluate the performance of the DRL framework. The production environment is a wheel assembly shop floor, with a set of operations, product types and resources. The shop floor consists of four assembly lines, responsible for wheel assembly. Before the assembly operation, a manual process must be performed. Depending on the product type, some types of wheels go through a final tire-fitting process, and then they are sent to the warehouse. The department is capable of producing 12 types of wheels. Each type of wheel has a specific processing time in the assembly line. The total processing time for a complete wheel assembly, as well as the suitability, is presented in Table 1. The shop floor of the production system is presented in Figure 3.  
For the representation of the production system, a simulation environment is developed. The production system's variables and the wheel attributes must be defined. Additionally, the input rules of the assembly lines have to be defined. These input rules are the dispatch rules that are used to formulate the action space. Three particular rules are chosen for this study in order to offer a thorough evaluation. These three rules are the Most Tasks Remaining per Job (MTRI), Longest Processing Time (LPT) and Shortest Processing Time (SPT). These guidelines each provide unique benefits and insights into the effectiveness and efficiency of resource management systems.  
* SPT: The resource selects the job that consists of the tasks that have the shortest processing times among all suitable tasks. The SPT rule is selected because it focuses on minimizing the processing time for individual tasks. This method helps to maintain a constant flow of work and reduces waiting times for subsequent tasks, making it especially useful in situations where quick task turnaround is essential.  
Figure 2: Scheduling framework architecture.  
* LPT: The resource selects the job that consists of the tasks that have the longest processing times among all suitable tasks. The LPT rule, as opposed to the SPT, is used to investigate the effects of prioritizing jobs with the longest processing times. This method is particularly effective in settings where finishing long-duration tasks is crucial for sustaining overall system efficiency and avoiding extended delays.
* MTRJ: The resource selects the job that consists of the most remaining suitable tasks. The MTRJ rule is chosen to assess how well jobs are prioritized according to the quantity of tasks that remain. The MTRJ rule is especially helpful in situations where completing multiple tasks is key to the system's overall progress.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Product** & **Product** & **Assembly** & **Assembly** & **Assembly** & **Assembly** \\
**Type ID** & **Type** & **Line 1 (min)** & **Line 2 (min)** & **Line 3 (min)** & **Line 4 (min)** \\ \hline
1 & A & 10 & 15 & 0 & 10 \\ \hline
2 & B & 5 & 0 & 0 & 5 \\ \hline
3 & C & 8 & 0 & 6 & 8 \\ \hline
4 & D & 12 & 18 & 8 & 0 \\ \hline
5 & E & 0 & 6 & 4 & 2 \\ \hline
6 & F & 0 & 5 & 0 & 0 \\ \hline
7 & G & 0 & 7 & 0 & 10.5 \\ \hline
8 & H & 6 & 9 & 0 & 0 \\ \hline
9 & I & 0 & 0 & 13 & 0 \\ \hline
10 & J & 0 & 4 & 0 & 0 \\ \hline
11 & K & 0 & 5 & 2 & 3 \\ \hline
12 & L & 14 & 7 & 10 & 0 \\ \hline \end{tabular}
\end{table}
Table 1: Product types, suitability and processing times.  
Figure 3: Wheel assembly department shop floor design.  
Each production line consists of parallel machines that can perform the assembly process, machines for manual operation and, lastly, machines for the tire-fitting process, if this is needed based on the process plan.