# Simheuristics Approaches for Efficient Decision-Making Support in Materials Trading Networks
## 8 Evaluation and Results  
In order to test the proposed approaches, we used a database from a distribution network of an international material trading company. The database was extracted from their enterprise resource planning system and was verified and validated according to a procedure model presented by [PERSON] et al. (2018). In this research, we filtered this database down to a subset of five sites and 30 SKUs. This database represents the database in Figure 1. Then, we adapted the _evolutionary algorithm_ to utilise the actions' DSI. This information is assigned to the action types, and from there then it is inherited to the derived actions from the action types. The selection probability of actions is changed based on their DSI. For example, considering the actions' type of changes, the actions are classified as structural and parametrical actions. The selection probability of these classes is changed according to Equations (1) and (2). An action is selected randomly from the respective selected class. Thus, the selection probability of actions is biased based on their type of changes. We compared a completely random selection of actions with a biased selection of actions in the construction of action plans in the EA's initial generation. The number of generations that were required to stagnate and the quality of the corresponding recommended action plans were recorded for the comparison. The quality of the action plans has been evaluated based on the costs and the service level. Fifty individuals form a generation in the EA. The crossover and mutation probabilities were set to 0.8 and 0.3, respectively. The crossover forms, CR, were the one-point crossover, the two-point crossover, and the uniform crossover. In mutation, MU, one randomly selected action was replaced, or multiple actions were selected to be replaced. Each experiment setup was run ten times. Table 3 records the average performance and demonstrates the gap analysis of the difference between random selection and the biased selection of actions. Next, we have tested the approach that exploits the definition of equivalent action plans. In this experiment, the EA selected actions randomly, and the evaluation of an action plan was skipped if an equivalent action plan was previously evaluated. Figure 9 shows the time distribution during an experiment run when the crossover and the mutation probabilities were set to 0.8 and 0.3, respectively. The number of simulation runs decreased by more than 15%. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Random Selection**} & \multicolumn{3}{c}{**Biased Selection**} & \multicolumn{3}{c}{**Cap (Biased \(-\) Random)**} \\
**CR** & **MU** & \(\mathbf{c}\) (€) & \(\mathbf{sl}\) (\%) & \(\mathbf{N_{s}}\) & \(\mathbf{c}\) (€) & \(\mathbf{sl}\) (\%) & \(\mathbf{N_{s}}\) & \(\mathbf{c}\) (€) & \(\mathbf{sl}\) (\%) & \(\mathbf{N_{s}}\) \\ \hline
1-point & 1-action & 90,420 & 84.58 & 76 & 90,309 & 84.74 & 31 & \(-\)111 & 0.2 & \(-\)45 \\
1-point & multi & 91,414 & 84.65 & 41 & 90,045 & 84.79 & 41 & \(-\)1369 & 0.1 & 0 \\
2-point & 1-action & 90,630 & 84.72 & 73 & 90,806 & 84.81 & 34 & 176 & 0.1 & \(-\)39 \\
2-point & multi & 90,673 & 84.69 & 65 & 91,340 & 84.83 & 43 & 667 & 0.1 & \(-\)22 \\ uniform & 1-action & 90,086 & 84.75 & 89 & 88,039 & 85.11 & 37 & \(-\)2047 & 0.4 & \(-\)52 \\ uniform & multi & 91,699 & 84.72 & 63 & 92,292 & 85.03 & 32 & 593 & 0.3 & \(-\)31 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between random selection of actions and biased selection in an evolutionary algorithm (EA) experiment using the gap analysis (CR = crossover form, MU = mutation form). For the experimentation with the _DQN agent_, the authors assumed that one training episode for the reinforcement learning agent consists of taking three actions. Hence, once the three actions have been taken, the simulation is reset to its initial state and the agent can start a new training episode.