## 5 Computational Experiments
### Plan Found by OPTIC  
Executing the OPTIC planner for the problem modeled in PDDL generates a complete solution plan, whose steps in finding the best possible solution are documented and available in the GitHub repository (see Section 6.2).  
The solution obtained through the methods used to solve the problem is graphically displayed in a unified structure over the time horizon. Figure 4 shows the legend used in the graph, which depicts the actions that occur during the production process. The activities depicted include production, quarantining, transportation, and demand fulfillment (at the DC). The representation excludes days when there was no relevant activity observed.  
The color scheme of Figure 4 represents different stages of the supply chain as follows: blue indicates input production, dark red represents the quarantine period, purple corresponds to derived production, and green signifies available stock. In transportation, the color varies according to the vehicle type (\(t\)), as follows: light blue for \(t_{1}\), light orange for \(t_{2}\), light green for \(t_{3}\), and yellow for \(t_{4}\). The arrival transport at the destination factory (\(f^{\prime}\)) follows the same color scheme but is enclosed in [ ] to distinguish it from departure transport.  
Figures 5-19 show the activities of the solution found by APS for this case study.  
We can observe that the solution proposed by APS starts production on the last day of quarantine of a previous production. As a consequence of this decision, there may be a delay in meeting the demand for inputs and/or dependent products.  
Figure 4: Legend for the figures that represent the solutions.  
We observed that APS has difficulties efficiently organizing production and using transport vehicles. These difficulties are inherent to a MILP like this, so we will see how the problem is solved using the CPLEX solver (Exact Method) and the GA.  
After the execution of the APS model, the system found a production plan but did not find a plan solution that was considered optimal. A critical point identified was the absence of production during the period in which the quarantine action was taking place. Nothing in the modeling restricts actions to co-occur with the quarantine period. This situation resulted in the underutilization of factories on the 30-day planning horizon, compromising the productive flow and efficiency of actions and impacting the non-fulfillment of demand.  
The state of the goal (b) requires that \(I_{DC,p,d}\geq DM_{p}\). Due to this, the production was interrupted only when we it fulfilled the demand in \(DC\), which resulted in the generation of stocks in the factories already producing. Table 5 shows the quantity of each product, in tons, that remains stored in the factories. An example of the planner's wrong decisions is that the demand for \(p_{1}\) is satisfied on \(d_{44}\). However, as \(p_{2}\) needs \(p_{1}\), the production of \(p_{1}\) continues until it supplies the demand for \(p_{2}\), generating unnecessary stock.  
Note that the production plan of OPTIC needs 65 days to satisfy all the demands in the \(DC\); in the following sections, we will see that this is more than twice the solutions obtained by the other methods. All files related to the modeling and execution of the planner are available on GitHub; see Section 6.2.