## 4 The Proposed AC-NSGA-III Algorithm
### Operator Design
#### 4.2.1 IRC Coding
Under the same algorithm conditions, to verify the superiority of the proposed order combination method, the AC strategy is compared with other order combination strategies (OCS [13] and NSDP [10]). (3) Comparison of look-forward optimization effects. Under the same algorithm conditions, to verify the cumulative optimization effect of look-forward processes in the rolling optimization process, the model considering objective function 3 is compared with the model without considering objective function (3). (4) Comparison of scheduling methods. In order to verify the superiority of the proposed scheduling method, comparative tests are conducted based on the example in 5.1. We carefully selected three scheduling methods with the same transport organization framework but different strategies. These scheduling methods come from different literature sources and have the following characteristics: \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline \(V\) & 68.45 dm3 \\ \(T_{0}\) & 11:00 \\ \(t_{n}^{s}\) & 5 min \\ \(N\) & 10 \\ \(P_{c}\) & 0.8 \\ \(P_{m}\) & 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Values of some parameters in the numerical experiment. * Scheduling method A ([PERSON] et al., 2019 [22]): This method focuses on multi-objective optimization, but does not take order consolidation strategy into account, and adopts deterministic scheduling strategy for solution under dynamic environments. * Scheduling method B ([PERSON] et al., 2022 [28]): This method also focuses on multi-objective optimization, does not incorporate order consolidation strategy, and implements deterministic scheduling method in dynamic environments. * Scheduling method C ([PERSON], 2019 [23]): This method focuses on single-objective optimization, does not involve an order consolidation strategy, and adopts a deterministic scheduling strategy in a dynamic environment with additional attention to after-effects. * The method proposed in this paper: the multi-objective optimization framework is introduced, the order consolidation strategy is considered comprehensively, and the uncertain scheduling method is adopted in a dynamic environment. In addition, this study further discusses the problem of after-effect, which improves the comprehensive adaptability and practicability of scheduling strategy. We selected objective function 1, expected order timeout rate, and objective function (2), expected customer waiting time, as evaluation indicators and analyzed the performance of each model through comparative experiments. ### Comparison of Results

* Comparison of optimization effect considering uncertainty factors. To evaluate the effectiveness of the proposed model, numerical experiments were conducted on the deterministic model (model-C) and the uncertainty model (Model-Unc) under each example. The two models were run 100 times, respectively. The data characteristics of total customer waiting time and order timeout rate were obtained as shown in the figure below. Figure 9 is box chart, showing the median, lower quartile, upper quartile, minimum, and maximum values. In the figure, the dashed lines represent the expected values of the solutions of model-C and Model-Unc under each example. As can be seen from the figure, on the one hand, the expected value of the solution under Model-Unc is better. On the other hand, the model-C solution has a larger fluctuation range than Model-Unc solution, and the unfeasible rate of model-C solution is higher. The uncertainty model proposed by us has a good immune effect to uncertainty factors. In other words, the solution of Model-Unc has good robustness in uncertain environment. To better reflect the optimization effect of the proposed model in uncertain environment, A1 is taken as an example. The mean value of the meal preparation time remained unchanged. As the standard deviation of the meal preparation time increased, the standard deviation trend of the solutions of model-C and Model-Unc was calculated. The experimental results are shown in the figure below.