## 6 Conclusions  
This study successfully developed a multi-objective optimization-based scheduling decision algorithm for edible oil transportation and proposed the \"Edible Oil Transportation System Framework\". The framework not only effectively balances pipeline resource utilization, avoiding resource waste and overload, but possesses strong dynamic adaptability. It promptly detects valve failures and emergency tasks, enabling rapid responses and intelligent path re-planning to ensure operational stability and continuity, thereby minimizing production disruptions caused by unexpected events. The system integrates an advanced 3D digital twin human-machine interface, enabling decision-makers to intuitively monitor \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Experimental Scenario**} & \multicolumn{3}{c}{**Cost Function Value**} & \multicolumn{3}{c}{**Cost Reduction Ratio**} \\  & **Multi-Objective Optimization** & & & **Cost Reduction Ratio** \\  & **A*** Algorithm** & **Dijkstra’s Algorithm** & **FIFO** & **A*** Algorithm** & **Dijkstra’s Algorithm** \\ \hline (a) Fixed edible oil weight & 201 & 202 & 255 & 21.17\% & 20.78\% \\ (b) Variable edible oil weight & 215 & 215 & 260 & 17.31\% & 17.31\% \\ (c) Fixed edible oil weight & & & & & \\ with emergency task & 206 & 212 & 312 & 33.97\% & 32.05\% \\ constraints & & & & & \\ (d) Variable edible oil weight & & & & & \\ with emergency task & 213 & 213 & 322 & 33.85\% & 33.85\% \\ constraints & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experimental scenario improvement summary—Cost function value. scheduling status and resource allocation in real time. This significantly enhances management efficiency and decision accuracy, particularly in traditional industrial environments requiring real-time production scheduling. Built on the A* and Dijkstra algorithms, the system models pipeline valve resources and fluid viscosity as weighted factors to enable multi-objective scheduling for oil transportation. The quantification of valve resources effectively assesses node importance and prioritizes transportation routes. By incorporating viscosity coefficients to reflect fluid characteristics, the system optimizes fluid transmission sequences and resource allocation, substantially reducing pipeline resistance losses and time costs. The framework thoroughly considers multiple objectives, including task priority, transportation time, and resource utilization, and can flexibly adapt to variations in oil volume, emergency tasks, and diverse constraints. This not only shortens total task completion time but balances pipeline resource usage. Overall, the \"Edible Oil Transportation System Framework\" presents task execution status intuitively to decision-makers, further improving scheduling efficiency and accuracy. Furthermore, the proposed framework has demonstrated concrete results across various simulation and practical scenarios. Scheduling efficiency based on the A* algorithm improved by approximately 43%, while cost-effectiveness increased by about 21%, significantly reducing overall task completion time and enhancing resource utilization. These outcomes validate its adaptability and practical value in complex industrial environments. Although applicable to other fluid transportation domains, domain-specific knowledge requires adapting the proposed time and cost functions to achieve optimal scheduling. Future research could further integrate real-time data and intelligent algorithms to expand the system's application scope and to promote comprehensive upgrades to fluid pipeline transportation systems. Specific directions include intelligent management and real-time monitoring, deep learning-based scheduling optimization, and the integration of digital transformation with automated control systems. These advancements aim to transform factory management from reactive responses to proactive prevention and optimization, enabling traditional production lines to become smarter and more flexible.