## 4 The Proposed AC-NSGA-III Algorithm
### Operator Design
#### 4.2.1 IRC Coding
The results show that the following: (a) Compared with the deterministic model, the solution obtained by the MO-RPDRP model has good robustness and a better optimization effect in an uncertain environment. (b) The Pareto frontier of the model is better under the AC strategy. (c) The Pareto frontier of models that consider after-effects is better. (d) The proposed scheduling method has advantages over existing methods in terms of the order timeout rate, customer waiting time, and algorithm efficiency. Combined with the abovementioned results, our research demonstrates robustness, an AC strategy and after-effect strategy, and the improvement of algorithm efficiency in an uncertain environment. These findings not only promote the development of theoretical research, but also provide more effective solutions for practical scheduling problems, in addition to providing important practical significance for improving the efficiency of transportation organizations and customer satisfaction. As far as we know, based on an uncertain environment, the literature considering order combination for the rolling optimization of takeout, pick-up and delivery paths is still in the initial stages. Further research in related areas can be carried out in the future. For example, to achieve higher delivery efficiency, the collaborative optimization problem of multiple AGVs could be studied. Conceptualizing J.M., methodology Q.S., validation, T.L., data curation, P.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Basic scientific research Project of colleges and universities of Liaoning Province Department of Education in 2024, grant number Lj212410153031 and the APC was funded by the Liaoning Province Education Department. Not applicable. **Data Availability Statement:** Part of the data used in the experiment is shown in Appendix A. Please contact us for more detailed data. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Order Timeout Rate (\%)**} & \multicolumn{3}{c}{**Total Customer Waiting Time (min)**} \\ \cline{2-7}  & **Optimum** & **Worst** & **Mean** & **Optimum** & **Worst** & **Mean** \\ \hline Spatial Crowdsourcing; & 0.69 & 1.21 & 0.98 & 21.38 & 30.64 & 27.63 \\ Heuristic Algorithm (A) & & & & & & \\ DRVRP; & 0.54 & 0.94 & 0.87 & 20.13 & 28.80 & 25.38 \\ WR+WI (B) & & & & & & \\ VFCDP; & 0.51 & 0.99 & 0.82 & 18.75 & 27.67 & 21.47 \\ Heuristic Algorithm (C) & & & & & & \\ MO-RPDRP; & & & & & & \\ AC-NSGA-III (D) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of advantages of scheduling method. ## References

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