## 5 Experimental Results
### Experimental Results Analysis  
The proposed multi-objective optimization method in this study significantly improves oil transportation efficiency across different scenarios, as summarized in Table 7. Specifically, scenario (a) demonstrates that the A* algorithm's multi-objective optimization  
Figure 16: Scheduling process for variable edible oil weight with emergency tasks: (**a**) 1 st iteration; (**b**) 2 nd iteration; (**c**) 3 rd iteration; (**d**) 4 th iteration; (**e**) 5 th to 8 th iterations. Figure 17: Task scheduling time charts for variable edible oil weight with emergency tasks: (**a**) multi-objective optimization scheduling with A* and [PERSON]’s algorithm; (**b**) FIFO scheduling. scheduling improves efficiency by 43.51%, saving 96 min, thus confirming its effectiveness in reducing operation time under baseline conditions. In scenario (b), efficiency increased by 44.64%, with a time saving of 338 min, indicating that the proposed method achieves a 1.13% higher efficiency improvement compared to scenario (a) under more complex conditions. Under the constraints of scenario (c), the method maintained a 48.13% efficiency improvement, saving 116 min. This demonstrates its ability to rapidly adapt scheduling strategies amid emergency task insertions or unforeseen events, ensuring smooth task execution. Scenario (d) recorded an efficiency improvement of 45.46%, with a time saving of 353 min. For [PERSON]'s algorithm, the multi-objective optimization scheduling results in Table 7 show an efficiency improvement of 39.79% and a time saving of 88 min in scenario (a). Under the constraints of scenario (c), the method achieved a 46.88% efficiency improvement and saved 112 min. In scenarios (b) and (d), the results matched those of the A* scheduling. The results confirm the robustness of the multi-objective optimization scheduling method across various scenarios, consistently reducing operation time and improving system efficiency. Notably, the A* algorithm performs particularly well in scenarios (b) and (d), which involve variations in transportation volume. This is mainly because such volume changes better reflect real-world work order conditions, where oil quantities vary significantly between different orders. The multi-objective optimization scheduling effectively adjusts resource allocation dynamically, intuitively improving transportation time and resource utilization efficiency. Further summarized cost function results for the methods are presented in Table 8. Compared to the cost function values of the FIFO method, the A* algorithm achieved a cost reduction rate of 21.17% in scenario (a), while [PERSON]'s algorithm reduced costs by 20.78%. In scenario (c), A* attained a cost reduction rate of 33.97%, and [PERSON]'s algorithm achieved a 32.05% cost reduction. For scenarios (b) and (d), since the A* and [PERSON]'s algorithms produced identical task scheduling plans, they shared the same cost reduction rates of 17.31% and 33.85%, respectively. This experiment compares scheduling results using two path planning methods, A* and [PERSON]'s algorithm. In most scenarios, both methods produced similar scheduling outcomes. This similarity can be attributed to the limited degrees of freedom in the factory pipeline environment, which offers fewer routing options and thus increases the likelihood of identical plans. However, in scenarios (b) and (d), although the Pareto distributions of tasks after path planning differed significantly, the resulting work order schedules were identical. Conversely, in scenarios (a) and (c), some tasks appeared very close to each