## 4 Computational Experiments and Results
### Performance of the Proposed Heuristic Method
#### 4.1.2 Comparison of Results  
The results obtained from the proposed heuristic based on DBR are compared against the results obtained from NEH and CDS heuristic methods based on problem instances of each category of problem. For each problem category, ten different data sets of demand and due date are made from their respective ranges to make ten scenarios that are solved using NEH, CDS, and the proposed DBR based heuristic method. The scheduling results of the product models for each customer based on the NEH, CDS, and DBR based heuristic methods for scenario 1 of problem category TS are illustrated in Figure 4. The results of the schedules of product models of customers based on a three day planning horizon for NEH and CDS algorithms can be seen in Figure 4, whereas the schedule obtained from the proposed heuristic based on the DBR method has a product of customer orders for each day's planning horizon. These results show that more customer orders can be completed if the proposed heuristic based on DBR is used for scheduling in each planning horizon. The production order release sequence of product models with the release quantity of the products and the release time obtained from the proposed heuristic based on the DBR method for one scenario of problem category TS for a three day (days 1, 2, and 3) plan are shown in Figure 5.  
The results based on the schedule of product models and the customer orders using NEH, CDS, and the proposed heuristic with their NP values for each problem category are presented in Figure 6. The buffer size used for the DBR model is considered as fixed for all problem categories, and the results for NP values of each problem category against 10 different scenarios are presented in Figure 6. It can be seen from Figure 6 that the value of NP for each problem category in different scenarios obtained from the proposed heuristic is higher compared to the NP values obtained from NEH and CDS schedules. This is due to the buffer placed on the CCR machine. Moreover, the buffer can reduce the time of production and can give the products to customers after preparing on CCR and the downstream machines of CCR. Furthermore, the schedule on a daily basis can further \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Problem Category**} & \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Day**} & \multirow{2}{*}{**Customer**} & \multicolumn{4}{c}{**Demand of**} & \multicolumn{2}{c}{**Probability of**} \\  & & & & & & \multicolumn{2}{c}{**Product Model**} & \multicolumn{2}{c}{**Due Date**} \\ \hline \multirow{4}{*}{TS} & & & & A & B & C & D & E & T = 0.85 & R = 0.15 \\ \cline{3-10}  & & & 1 & 18 & 22 & 2 & 32 & 29 & 895 & 3926 \\  & & & 2 & 22 & 2 & 38 & 32 & 39 & 766 & 4901 \\  & & & 3 & 25 & 31 & 23 & 6 & 15 & 1062 & 2821 \\ \cline{2-10}  & & & 1 & 4 & 1 & 42 & 10 & 11 & 719 & 5581 \\  & & & 2 & 9 & 18 & 29 & 0 & 33 & 1102 & 3549 \\  & & & 3 & 16 & 34 & 15 & 15 & 2 & 1296 & 1852 \\ \cline{2-10}  & & & 1 & 5 & 26 & 14 & 31 & 4 & 1379 & 5702 \\  & & & 2 & 29 & 21 & 31 & 34 & 11 & 1199 & 2646 \\  & & & 3 & 12 & 18 & 34 & 28 & 4 & 1234 & 3287 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Demand of product models from different customers received on three consecutive days.