# A Dynamic Adjusted Aggregate Load Method to Support Workload Control Policies
## 4 Simulation Model  
The simulation models use the same parameters proposed in several works in literature ([PERSON] et al., 2015; [PERSON] et al., 2015). For each model, the workload norm that leads to better performance measures is evaluated. The benchmark model is the model with the classical adjusted workload computation. The models tested are shown in Table 1 with the following characteristics:  
- The order release, which includes the evaluation of the norm on the bottleneck of all workstations;
- The three pre-shop ranking methods: EDD, SPT and workload balance;
- The classical corrected and proposed (dynamic) workload computation. Table 2 reports the characteristics that are the same as proposed in [PERSON] et al. (2015). The models reported in Table 1 are replicated for one, two and three bottleneck cases. The exponential interarrivalparameter allows us to obtain an average utilization of about 80%, and the due date is assigned to take into account the total processing time of the jobs. The experimental classes are 36, considering 12 models for three bottleneck cases. Each class is tested for different values of the workload norm to establish the value that leads to better results. The better value of the workload norm is 14 and it is the same for all models tested. The results discussed are the better obtained with the 14 as workload norm. For each experiment class, a number of replications required to assure a 5% confidence interval and 95% of a confidence level for each performance measure have been conducted. Each combination of experiment class is characterized by over 2000 replications and about 8 h of computation time (4 GHz Intel Core i7 and 8 Gb RAM). The performance measures evaluated are the following. A set of measures concerns the due date of the jobs: percentage of tardy jobs, the standard deviation of lateness, average lateness (unit time) and the total time of lateness. The total time of lateness is computed by multiplying the total product for the percentage in delay and the average lateness for a single product. The other performance measures are as follows:  
* Total throughput time (unit time): this is the total time from the enter to the exit of the order from the manufacturing system. * Average shop floor throughput time (unit time): this is the time from the release time of the order in the manufacturing system and the exit from it. * Average machine queues (number of parts): this is the number of parts in the queue of each machine. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Order Release** & **Pre-Shop Pool Ranking** & **Workload Computation** \\ \hline Benchmark1 & Bottleneck norm & EDD & O�sterman et al. (2000) \\ \hline Benchmark2 & All workstations norm & EDD & [PERSON]�[PERSON] et al. (2000) \\ \hline Benchmark3 & Bottleneck norm & SPT & [PERSON]�[PERSON] et al. (2000) \\ \hline Benchmark4 & All workstations norm & SPT & [PERSON]�[PERSON] et al. (2000) \\ \hline Benchmark5 & Bottleneck norm & Balance & [PERSON]�[PERSON] et al. (2000) \\ \hline Benchmark6 & All workstations norm & Balance & [PERSON]�[PERSON] et al. (2000) \\ \hline Dyn1 & Bottleneck norm & EDD & Dynamic WL \\ \hline Dyn2 & All workstations norm & EDD & Dynamic WL \\ \hline Dyn3 & Bottleneck norm & SPT & Dynamic WL \\ \hline Dyn4 & All workstations norm & SPT & Dynamic WL \\ \hline Dyn5 & Bottleneck norm & Balance & Dynamic WL \\ \hline Dyn6 & All workstations norm & Balance & Dynamic WL \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model simulated.