## 5 Experimental Results
### Experimental Results of Multi-Objective Optimization Scheduling
#### 5.2.2 Variable Edible Oil Weight  
In this experiment, each work order is assigned a random edible oil transport weight ranging from 10 to 50 tons. This setup aims to evaluate the proposed model's adaptability to varying transport demands and its effectiveness in allocating pipeline and valve resources accordingly. The assigned transport weights for each task are summarized in Table 4.  
Figure 11: Task scheduling time charts for fixed edible oil weights: (**a**) multi-objective optimization scheduling with A* algorithm; (**b**) multi-objective optimization scheduling with [PERSON]’s algorithm; (**c**) FIFO scheduling.  
Scheduling was performed using a multi-objective optimization approach, incorporating both the A* algorithm and [PERSON]'s algorithm. The task distribution results are as follows: the first scheduling iteration, shown in Figure 12a, identified Task 8 as the Pareto front solution for both algorithms. Upon examining the path planning results, differences between the two algorithms were observed for Tasks 2 and 6; however, since these tasks were not part of the Pareto front, they did not affect the scheduling plan. The second scheduling iteration, illustrated in Figure 12b, showed Task 5 as the optimal solution for both algorithms. In the third scheduling iteration (Figure 12c), Task 2 was identified as the Pareto front solution for both algorithms. The fourth iteration results, presented in Figure 12d, highlighted Task 4 as the primary selected task for both algorithms. In the fifth scheduling iteration (Figure 12e), Task 3 was selected as the Pareto front solution by both algorithms. After the fifth iteration, available pipeline resources reached their limit, preventing the execution of additional new oil transportation tasks. Consequently, subsequent scheduling had to wait until resources were released by the currently executing tasks. For the sixth through eighth scheduling iterations, the execution order for both algorithms was Tasks 6, 1, and 7, as shown in Figure 12f.  
\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 & 50 & Soybean Oil \\ Task2 & T5004 & T0061 & 30 & Soybean Oil \\ Task3 & T5005 & T0101 & 50 & Canola Oil \\ Task4 & T5101 & T0202 & 20 & Soybean Oil \\ Task5 & T5001 & T0201 & 20 & Palm Oil \\ Task6 & T5008 & T101 & 30 & Soybean Oil \\ Task7 & T5011 & T0061 & 10 & Soybean Oil \\ Task8 & Two Tank Truck & T5002 & 20 & Degummed Oil \\ \hline \hline \end{tabular}
\end{table}
Table 4: Work orders list for variable edible oil weights.  
Figure 12: Scheduling process for variable edible oil weight tasks: (**a**) 1 st iteration; (**b**) 2 nd iteration; (**c**) 3 rd iteration; (**d**) 4 th iteration; (**e**) 5 th iteration; (**f**) 6 th to 8 th iterations.  
Since both the A* algorithm and [PERSON]'s algorithm produced the same task scheduling plan in this scenario, the analysis results of their multi-objective optimization scheduling time data are presented in Figure 13a. Task 6 is executed immediately after the completion of Task 5, Task 1 begins after Task 4 finishes, and Task 7 follows directly after Task 6. The estimated total completion time for all tasks is 419.19 min. By contrast, the time data for the FIFO scheduling method is presented in Figure 13b, with an estimated total completion time of 757.34 min. Notably, Task 1 involves transporting a large volume of edible oil over 307.68 min, causing delays to subsequent tasks and further extending the overall completion time.