## 3 Results of the Computational Experiment  
As a reference trial example let us take the following scheduling problem. The manufacturing procedure contains 13 operations that require 6 different resources. The schedule of the manufacturing procedure for a single order is shown in Figure 5. In this example, we want to place two orders at the depicted resources and start evaluating from the point 'all orders in a row' (see Figure 6). The results of evaluating branch-and-bound and gradient algorithm are placed in the following Table 1. The resulting schedules are depicted in Figure 7 (for branch-and-bound algorithm) and Figure 8 (for gradient algorithm). Figure 5: Gannt diagram for single order manufacturing procedure. Figure 6: Gannt diagram for starting point of two orders schedule optimization. Results of analysis gives us understanding that for a modest dimensionality scheduling problem both considered approaches are applicable. However, as the number of operations in manufacturing procedure grows and the number of orders increases, we will experience an enormous growth of algorithm iterations for enumeration (branch-and-bound) technique and the gradient-alike algorithm will obviously detect local optimum as the best achievable solution (which means the optimization results will differ more and more compared to the digits in Table 1). Rising difference between the results is shown in Table 2.  
Expanding the previous problem for a procedure of 500 operations in 2 orders we will get the results presented in Table 3.  
Figure 8: Gradient algorithm optimization result. Figure 7: Branch-and-bound optimization result. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Resulting “Makespan”** \\ **Objective Function** \\ \end{tabular} & \begin{tabular}{c} **Times of Orders Finished** \\ **Order 1** \\ \end{tabular} & \begin{tabular}{c} **Full Number of Operations Permutations** \\ **(Including Non-Feasible)** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Iterated Permutations** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Non-Feasible Permutations** \\ \end{tabular} &
\begin{tabular}{c} **Calculation Time,** \\ **Seconds** \\ \end{tabular} \\ \hline B\&B & 235 & 86 & 149 & 318 & 25 & 11 & 3.6 \\ gradient & 238 & 91 & 147 & 318 & 18 & 4 & 2.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Algorithms test results: small number of operations (13 operations). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Resulting “Makespan”** \\ **Objective Function** \\ \end{tabular} & \begin{tabular}{c} **Times of Orders Finished** \\ **Order 1** \\ \end{tabular} & \begin{tabular}{c} **Full Number of** \\ **Order 2** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Operations Fermions** \\ **Modulating Non-Feasible** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Iterated Permutations** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Non-Feasible Permutations** \\ \end{tabular} &
\begin{tabular}{c} **Calculation Time,** \\ **Seconds** \\ \end{tabular} \\ \hline B\&B & 235 & 86 & 149 & 318 & 25 & 11 & 3.6 \\ gradient & 238 & 91 & 147 & 318 & 18 & 4 & 2.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results for 2 orders with number of operations increasing.