## 3 System Architecture of the DSS
### Model Management
#### 3.3.1 Selection of an Optimal Product Mix under the AHP-TOC Heuristic  
The data inputs for the initial process, the AHP-TOC heuristic, are gathered from four distinct tables, specifically, the resource table, SBU tables, the MPS table, and the questionnaire table. These tables aggregate the pertinent data obtained from four external data sources, namely, production, customers, the market, and senior executives. Additionally, an internal data source, the master production schedule (MPS), contributes to the relevant data stored in the MPS table. In the course of executing the AHP-TOC heuristic, the MPS table serves as a repository for production orders and is accessed through ad hoc queries to compute the dynamic capacity utilization data. Operational information pertinent to production serves as the foundational data, primarily concerning resources such as machinery, workstations, and their respective capacities, as well as SBUs, including the processing time of products across various resources. Customer orders are pivotal for determining the periodic demand attributed to each SBU, considering both historical demand data and proactive demand forecasting endeavors. The actual prices at which the raw materials and final products are being traded in the market are directly associated with the company's raw materials cost and sales price. A salient characteristic of the proposed decision support system (DSS) lies in the integration of the decision-maker's perspective within the determination of the product mix. The involvement of the decision-maker in the solution process aligns with the initial phase of the second step of the theory of constraints (TOC) concept, specifically addressing the optimization of the system's constraints. The decision matrix, constructed using [PERSON]'s nine-point scale (Table 1), facilitates the representation of the decision-maker's viewpoint regarding profit and resource considerations (refer to Figure 2). The decision matrix further serves as the foundational step in prioritizing the relative importance of the first-level criteria within the AHP. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Extreme** \\ **Importance** \\ \end{tabular} & \begin{tabular}{c} **Viny Storms** \\ **Importance** \\ \end{tabular} & \begin{tabular}{c} **Stowing Importance** \\ \end{tabular} & \begin{tabular}{c} **Moderate** \\ **Importance** \\ \end{tabular} & \begin{tabular}{c} **Equal** \\ **Importance** \\ \end{tabular} & \begin{tabular}{c} **Moderate** \\ **Importance** \\ \end{tabular} & \begin{tabular}{c} **Storage Importance** \\ \end{tabular} & \
\begin{tabular}{c} **Very Storms** \\ **Importance** \\ \end{tabular} &
\begin{tabular}{c} **Extreme** \\ **Importance** \\ \end{tabular} \\ \hline \hline  & 91 & 81 & 76 & 64 & 54 & 41 & 34 & 21 & 11 & 12 & 13 & 14 & 15 & 16 & 17 & 18 & 19 \\ \hline  & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & Prok \\ \hline \end{tabular}
\end{table} 
Table 1: [PERSON]’s nine-point scale for pairwise comparison. Figure 1: Data flow diagram of the proposed methodology. The priority vector, also known as the relative importance, is computed by normalizing the eigenvector of the decision matrix. As the number of elements in the decision matrix is less than 3, it is not possible to assess the consistency ratio [55]. In our model, the consistency can be determined directly by examining the relative values of the elements. The AHP-TOC heuristic method is organized within a hierarchical framework of three layers. At the foundation, the initial layer systematically integrates the assessment of both resources and profit.