## 5 Experimental Results
### Experimental Results of Multi-Objective Optimization Scheduling
#### 5.2.1 Fixed Edible Oil Weight  
In this scenario, the work orders are as shown in Table 3, where all variables remain constant, including the edible oil weight and task priorities. The objective is to serve as a baseline test for evaluating the scheduling performance under static conditions. First, scheduling and analysis were performed using a multi-objective optimization approach that incorporated both the A* algorithm and [PERSON]'s algorithm. The Pareto front obtained from the first scheduling iteration is shown in Figure 9(a), where the horizontal axis represents the cost function and the vertical axis represents the time function. In the case of the A* algorithm, Task 8 appeared on the Pareto front, marked by a red \"\(\otimes\)\". Likewise, in the [PERSON] algorithm result, Task 8 was also located on the Pareto front, indicated by a red \"\(\times\)\". As a result, Task 8 was extracted from the job list and placed into the execution queue as the first task to be performed. The pipeline resources allocated to Task 8 were subsequently used as constraints for the scheduling of the remaining tasks. A second scheduling iteration was then conducted to determine the next optimal task. As shown in Figure 9(b), the task distribution from the second scheduling iteration revealed that both the A* algorithm and [PERSON]'s algorithm identified Task 5 as the Pareto-optimal solution in this round. Therefore, Task 5 was added to the execution queue as the second task. The pipeline resources allocated to Tasks 8 and 5 were set as constraints for subsequent scheduling. In the third scheduling iteration, illustrated in Figure 9(c), the task distribution indicated that both the A* algorithm and [PERSON]'s algorithm selected Task 2 as the Pareto-optimal solution in this round. Accordingly, Task 2 was added to the execution queue as the third task. The pipeline resources allocated to Tasks 8, 5, and 2 were used as constraints for the subsequent scheduling iterations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task ID** & **Source Tank** & \
\begin{tabular}{c} **Target** \\ **Tank** \\ \end{tabular} &
\begin{tabular}{c} **Oil Transfer** \\ **Weight (ton)** \\ \end{tabular} & **Oil Type** \\ \hline 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 \hline \end{tabular}
\end{table} 
Table 3: Work orders list for fixed edible oil weight. During the fourth scheduling iteration, depicted in Figure 10d, the task distribution revealed that, for the A* algorithm, both Task 1 and Task 4 lay on the Pareto front. Among them, Task 1 required fewer pipeline resources than Task 4, while Task 4 had a shorter transportation time than Task 1. Although Task 1 involved fewer pipeline resources, its cost function value differed from that of Task 4 by less than 4. Since Task 1 did not have the shortest transportation time among the candidates, Task 4 was selected as the fourth task in the A* execution sequence. For [PERSON]'s algorithm, both Task 1 and Task 3 appeared on the Pareto front. After evaluation, Task 3 was selected as the fourth task. Additionally, due to the absence of an available oil pipeline path for Task 7, it was temporarily excluded from scheduling and is therefore not shown in Figure 10d. The pipeline resources occupied by Tasks 8, 5, 2, and 4 were used as constraints for subsequent scheduling iterations under the A* algorithm, while those associated with Tasks 8, 5, 2, and 3 were set as constraints for [PERSON]'s algorithm. The task distribution resulting from the fifth scheduling iteration is shown in Figure 10e. For the A* algorithm, only Task 3 had an available oil pipeline path for execution; thus, Task 3 was added to the execution queue as the fifth task.