*4.3. An instance of Distribution in Shanghai City*

A refrigerated logistics company in Shanghai has to deliver to 20 distribution points. The information about customers is shown in Table 10. Assuming that the traffic congestion in the distribution is not considered, the vehicle is driven at a uniform speed of 40km/h and the carbon trading price is fixed at 54 RMB/ton. Moreover, the potential value of the customer is not considered. The solution can be solved with G-NSGA-II and then compared with the results of CV-GVRP. This paper's model considered the variable speed, variable carbon trading price and customer value, resulting in an 8.18% reduction in the total cost, a 23.82% increase in customer value and a 4.05% reduction in time. The results are shown in Table 11. In addition, the number of vehicles used was two less. The two solutions are shown in Figures 15 and 16.

**Figure 15.** Preoptimization delivery routes.


**Table 10.** Shanghai Huangpu district distribution point information.

**Table 11.** Example solution result comparison.


**Figure 16.** Optimized delivery route conclusion.

#### *4.4. Discussion on the Model and the Algorithm*

In general, the experiments focused on two aspects: the algorithm and the model. To analyze the algorithm performance, six customer points were used as examples to verify the feasibility of the G-NSGA-II algorithm. To further explore the superiority of the G-NSGA-II algorithm, the small–medium–large-scale test sets were constructed using Solomon data. The solution results of the MOPSO, NSGA-II and G-NSGA-II algorithms were compared in convergence and diversity. By plotting boxplots in NPS, DM and MID and comparing their median and dispersion, the G-NSGA-II algorithm outperformed the others.

The CV model was used to analyze the influence of carbon trading uncertainty, timevarying network and customer potential value on the distribution schemes.


Finally, the CV-GVRP model and the G-NSGA-II algorithm were applied to practical problems, and a planning scheme was proposed for real-life cold chain distributions. Constructing a cold chain distribution model under a carbon trading policy was conducive to reducing carbon emissions and achieving sustainable developments in enterprises. Taking into account the time window and potential value of the customer could help improve the reputation and benefits of the business by providing timely services to important customers.

### **5. Conclusions**

From the perspective of variable carbon trading prices, this paper built a multiobjective optimization model, the CV-GVRP, which aimed to minimize the total cost and maximize customer value, considering both customer value and the time-dependent network. This model was an improvement of the traditional cold chain one, which was mainly applicable to fresh agricultural products with a short shelf life, low temperature and simple requirements for distribution conditions, such as leafy vegetables and fruits. By setting different ranges of carbon trading prices, it was found that flexible carbon trading prices positively affected companies in reducing their carbon emissions. Compared to static networks, the CV-GVRP was found to be advantageous in the total cost savings and customer value growth. Moreover, the CV-GVRP could improve customer satisfaction at a lower cost than models that do not take customer value into account. Small, medium and large-scale examples of c, r and rc, respectively, were constructed using Solomon data. Using the nine examples, G-NSGA-II, NSGA-II and MOPSO were used to solve this model. The values of NPS, DM and MID showed that the G-NSGA-II algorithm performed better than the others.

Future research can incorporate additional factors into the consideration of customer value, such as service quality and product quality. For the calculation of variable speed, only the congestion case was considered. However, the speed limits, emergencies and traffic control also affect vehicle speed in real life. This study considered only the carbon trading policy; in the future, the carbon tax policy and carbon trading policy can be used in

coordination to reduce carbon emissions. To better address practical problems, there is still a lot of space to enrich the details of this model.

**Author Contributions:** D.W.: Conceptualization, Funding Acquisition and Writing—Review, Editing and Original Draft; J.L.: Methodology, Resources, Software and Writing—Original Draft; J.C.: Formal Analysis and Validation; D.H.: Supervision and Project Administration. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the China Education Ministry of Humanities and Social Science Research Youth Fund project (No. 18YJCZH192), the Special Project of National Characteristic Freshwater Fish Industrial Technology System for Construction of Modern Agricultural Industrial Technology System (No. CARS-46). Major project of National Social Science Fund "Research on the development strategy of China's deep blue fishery under the background of accelerating the construction of a marine power" (No. 21 & ZD100).

**Institutional Review Board Statement:** Not applicable.

**Data Availability Statement:** All experimental data in this paper came from: https://www.sintef. no/projectweb/top/vrptw/solomon-benchmark/ (accessed on 18 April 2008).

**Conflicts of Interest:** The authors declare no conflict of interest.
