## 5 Experimental Results
### Experimental Results of Multi-Objective Optimization Scheduling
#### 5.2.3 Fixed Oil Weight with Emergency Tasks  
This scenario is designed to evaluate the responsiveness and scheduling efficiency of the proposed Edible Oil Transportation System Framework in handling unexpected events. By simulating the addition of high-priority emergency tasks, the impact on scheduling outcomes is analyzed, as shown in Table 5. The two added high-priority tasks are (1) transportation of edible oil from a tanker truck to a storage tank (Priority Task 1), and (2) inbound delivery of new edible oil from a refinery (Priority Task 2). These tasks are expected to occupy several critical pipeline nodes, thereby increasing the overall value of the cost function. The Edible Oil Transportation System Framework proposed in this study employs a multi-objective optimization approach for scheduling. In the first scheduling iteration, as shown in Figure 14a, both the A* and [PERSON]'s algorithms identified Task 8 as the Pareto front solution. The system set the resources occupied by Task 1, Task 2, and Task 8 as constraints to plan the oil transportation routes for the remaining tasks. Following the second scheduling iteration, illustrated in Figure 14b, both algorithms recognized Task 5 as the new Pareto front solution. The third scheduling results, depicted in Figure 14c,  
Figure 13: Task scheduling time charts for variable oil weights: (**a**) multi-objective optimization scheduling with A* and [PERSON]’s algorithm; (**b**) FIFO scheduling. \begin{table}
\begin{tabular}{c c c c c} \hline
**Task ID** & **Source Tank** & **Target Tank** & **Oil Transfer Weight (Ton)** & **Oil Type** \\ \hline Priority Task1 & One Tank Truck & T5004 & 10 & Canola Oil \\ Priority Task2 & Refinery Oil Tank & T5008 & 10 & Soybean Oil \\ Task1 & T5007 & T0504 & 10 & Soybean Oil \\ Task2 & T5004 & T0061 & 10 & Soybean Oil \\ Task3 & T5005 & T0101 & 10 & Canola Oil \\ Task4 & T5010 & T0202 & 10 & Soybean Oil \\ Task5 & T5001 & T0201 & 10 & Palm Oil \\ Task6 & T5008 & T101 & 10 & Soybean Oil \\ Task7 & T5011 & T0061 & 10 & Soybean Oil \\ Task8 & Two Tank Truck & T5002 & 10 & Degummed Oil \\ \hline \end{tabular}
\end{table} 
Table 5: Work order list for fixed oil weight with emergency tasks. showed Task 2 selected by the A* algorithm, while [PERSON]'s algorithm selected Task 3. In the fourth iteration (Figure 14d), the A* algorithm chose Task 3 as the Pareto front solution, whereas [PERSON]'s algorithm scheduled Task 1. By the fourth scheduling iteration, the available pipeline resources could no longer support additional task executions. Therefore, the fifth iteration considered dynamic reallocation of resources released upon completion of prior tasks. In this iteration, the A* algorithm ultimately selected Task 4 as the optimal task, while [PERSON]'s algorithm scheduled Task 2, as shown in Figure 14e. During the sixth scheduling iteration (Figure 14f), three tasks met the execution criteria. According to the multi-objective optimization algorithm's evaluation, A* selected Task 1 as part of the new Pareto front, whereas [PERSON]'s algorithm scheduled Task 4. Finally, in the seventh and eighth iterations, both methods sequentially executed Tasks 7 and 6, completing the overall final scheduling plan, as illustrated in Figure 14g. Based on the multi-objective optimization scheduling time data, the results for the A* algorithm are shown in Figure 15a. Task 4 immediately followed the completion of Task 5, Task 1 began after Task 2, Task 7 proceeded after Task 3, and Task 6 finished last after Task 4. According to the chart data, the estimated overall task completion time was 124.65 min. The results for [PERSON]'s algorithm, presented in Figure 15b, show that Task 2 followed Task 5, Task 4 started after Task 3, and Task 7 was executed immediately after Task 1. The estimated total task completion time was 127.65 min.