# A Dynamic Adjusted Aggregate Load Method to Support Workload Control Policies
## 2 Literature Review  
This section does not present an extensive review of WLC (for a complete review of the literature, see [PERSON] et al. [13]; [PERSON] et al. [14]; [PERSON] and [PERSON] [15]), but the most important and recent studies of the aggregate workload computation are discussed. [PERSON] et al. [16] tested two methods to compute the aggregate workload: the classical aggregate load approach [17; 18] and the corrected aggregate load approach [11; 19]. The classical aggregate workload includes indirect load and direct load without distinguishing between the two. The corrected aggregate load considers the routing of the job. The load is corrected (using a reduced factor) according to the position of the work center in the routing. They studied the effect of the shop floor characteristics on the workload norm, considering the two aggregate workload methods. The corrected aggregate level is the better approach in all experiments conducted, as shown also in [PERSON] et al. [20]. The main drawback of the corrected workload is that the computation is static and therefore does not represent the real-time manufacturing system state. [PERSON] [21] proposed a corrected workload computation derived from the approach proposed by [PERSON] and [PERSON] [19] and [PERSON] et al. [11]. The approach proposed includes a correction of the workload that depends on the average utilization of the work centers. This approach works better for specific performance measures like the percentage parts in delay and the average time in the manufacturing system. The calculation of workload can be complex for small shops with limited resources [22]. Then, an alternative was proposed by [PERSON] [23] called the COntrol of BAlance by CArd-BASed Navigation (COBACABANA). This approach uses the card to limit the jobs in the shop floor. [PERSON] et al. [24] revised the COBACABANA approach to better adapt the method to a workload control method. Their simulations highlighted improvements of the throughput time, percentage tardiness and mean tardiness. The main limit is the restricted characteristics analyzed. [PERSON] et al. [25] proposed a model based on the CONWIP (Constant Work In Process) concept to face the workload problem. Their model considers a CONWIP card which represents a measure of workload rather than a job. The results have shown that the workload computations proposed in the literature are not directly adequate for their model. The main limit is the restricted characteristics analyzed in the manufacturing system. [PERSON] et al. [26] studied the effect of bottleneck shiftness on the performance of a manufacturing system under WLC control. They highlighted how the highest bottleneck utilization improves performance. [PERSON] et al. [27] discussed a review of the literature on autonomous production control methods. They argued that the methods suggested in the literature are not exploiting their full potential for decision-making in real-time. The main limits of the literature concern the use of adjusted workload computation in all studies proposed; this computation is static and does not take into account the real-time conditions of the shop floor. Then, the orders are released, considering dated information rather than the changes in the workload of the work centers. Moreover, the literature studied consider the manufacturing systems in restricted conditions such as the directional routing of the jobs, the case of only one bottleneck and a limited number of performance measures. In response, this research proposes a dynamic model to compute the aggregate workload of the work centers, allowing the release of the orders using updated information. The proposed model updates the workload information every time a job exits from a workstation, considering a non-directional routing of the jobs and a wider range of performance measures. Then, the first research question (RQ) is:  
RQ1: Can the proposed dynamic adjusted workload computation improve some performance measures of the manufacturing system?