# Improved Genetic Algorithm for Solving Green Path Models of Concrete Trucks
## 5 Conclusions  
This paper establishes a fuel consumption model for concrete mixer trucks when multiple concrete-mixing plants work together to distribute concrete, and solves the model using an improved genetic algorithm. In order to enhance the genetic algorithm's capability to solve the mathematical model, various techniques such as the adaptive elite retention strategy, adaptive crossover and mutation operators, and immune selection have been incorporated. The above simulation analysis results indicate that the path model established can reduce the fuel consumption of distribution trucks overall; additionally, the improved genetic algorithm can quickly and effectively solve the model.  
Summarizing the above, we can draw the following three main conclusions:  
1. The concrete tanker fuel consumption model we developed is valid;
2. The distribution fuel consumption is reduced by 21.25% compared with the original scheme, which proves that our established mathematical model of the low-carbon co-operative distribution of concrete multi-mixing stations effectively reduces the overall fuel consumption of distribution vehicles;
3. IGA converges 26.25% faster than GA, which proves that our proposed improved genetic algorithm can solve the established mathematical model more quickly and efficiently than the standard genetic algorithm.  
In future works, concrete companies need electric trucks to transport concrete because traffic restrictions in some cities and the popularity of electric vehicles are on the rise; however, there is limited research on the collaborative delivery of concrete between electric and fuel vehicles. Taking into account the specificity of the time window and part of the city's fuel vehicle restrictions, future research on electric concrete tankers and fuel concrete tanker vehicle ratios and co-ordinated scheduling will be of great significance. On the one hand, it can effectively circumvent the vehicle restrictions to the inconvenience of the concrete enterprises, and, on the other hand, the use of electric concrete tankers will greatly reduce the overall carbon emissions of the concrete distribution process.  
In the process of concrete delivery, the delivery time is very important, but, due to some uncertain factors in the transportation process, the delivery time is difficult to know, so it is still necessary to study the method of estimating the delivery time. Furthermore, since the values of the parameters in this paper directly affect the performance of the algorithm, the sensitivity of the input parameters to the performance of the final algorithm can be analyzed [35]. In future research, the sensitivity of these parameters to the algorithm performance can be calculated, and parameters with a high sensitivity to the algorithm performance can be selected for optimization. Insensitive parameters can be set to fixed values to reduce the dimension of the optimization parameters, improve optimization efficiency, and improve algorithm performance.  
**Author Contributions:** Methodology, J.Y., H.Z. and B.Y.; software, H.Z., B.Y., L.S. and Y.G.; validation, J.Y., H.Z. and J.M.; formal analysis, H.Z., J.M. and B.Y.; resources, J.Y., J.S. and L.S.; writing original draft preparation, J.Y., H.Z., J.S. and Y.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The project was funded by the National Natural Science Foundation of China: Research on Enterprise Resource Location Optimization Based on the Internet of things (71371172) and Henan 2023 Research and development of key technologies for intelligent distribution of ready-mixed concrete materials and cloud platform system (232102220089).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original data in this paper are provided by Zhengzhou Sanhe Hydraulic Concrete Machinery Co., Ltd.  
**Conflicts of Interest:** The authors declare no conflict of interest.