## 5 Experimental Results
### Experimental Results of Multi-Objective Optimization Scheduling
#### 5.2.4 Variable Edible Oil Weights with Emergency Tasks  
In this scenario, two additional priority tasks are introduced, including edible oil transportation from the tanker truck to the storage tank (Priority Task 1) and the intake of new edible oil from the refinery (Priority Task 2), as shown in Table 6. Under resource constraints, this study analyzes the effects of multiple limitations on scheduling performance and evaluates the efficiency of the proposed multi-objective optimization approach.  
In this scenario, the multi-objective optimization method was employed for the first scheduling iteration, with the task distribution shown in Figure 16a. Both the A* and [PERSON]'s algorithms identified Task 8 as the Pareto front solution during this iteration. The system set the resources occupied by Task 1, Task 2, and Task 8 as constraints to find paths for the remaining edible oil transportation tasks. The results of the second scheduling iteration, shown in Figure 16b, indicated that both algorithms selected Task 5 as the Pareto front. In the third iteration (Figure 16c), Task 4 was chosen as the Pareto front solution by  
\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 & 50 & Soybean Oil \\ Task2 & T5004 & T0061 & 30 & Soybean Oil \\ Task3 & T5005 & T0101 & 50 & Canola Oil \\ Task4 & T5010 & T0202 & 20 & Soybean Oil \\ Task5 & T5001 & T0201 & 20 & Palm Oil \\ Task6 & T5008 & T1101 & 30 & Soybean Oil \\ Task7 & T5011 & T0061 & 10 & Soybean Oil \\ Task8 & Two Tank Truck & T5002 & 20 & Degummed Oil \\ \hline \end{tabular}
\end{table}
Table 6: Work order list for variable oil weight with emergency tasks.  
Figure 15: Task scheduling time charts for fixed edible oil weight with emergency tasks: (**a**) multi-objective optimization scheduling with A* algorithm; (**b**) multi-objective optimization scheduling with [PERSON]’s algorithm; (**c**) FIFO scheduling.  
both algorithms. The fourth scheduling results, illustrated in Figure 16d, showed Task 2 as the Pareto front for both algorithms. At this point, the pipeline resources could no longer accommodate additional tasks. For the fifth through eighth scheduling iterations, pipeline resources were dynamically released based on the completion order of prior tasks, and Tasks 3, 1, 7, and 6 were scheduled sequentially, as shown in Figure 16e.  
Since the A* and [PERSON]'s algorithms produced identical task scheduling plans in this scenario, their multi-objective optimization scheduling time data are presented together in Figure 17a. In this schedule, Task 3 is executed immediately after Task 8 is completed, Task 1 begins following the completion of Task 4, Task 7 proceeds after Task 2 finishes, and Task 6 follows Task 3, ultimately completing all tasks. The estimated overall completion time is 423.76 min. By contrast, the time data for the FIFO scheduling method, shown in Figure 17b, estimate the total completion time to be 777 min.