# Low-Carbon Logistics Distribution Vehicle Routing Optimization Based on INNC-GA
## 6 Conclusions and Outlook  
This research examines a vehicle path optimization approach for low-carbon logistics while taking into account time window constraints. This study comprehensively analyzes the factors influencing time window constraints, vehicle load constraints, and speed constraints. A vehicle path optimization model is developed to minimize the total cost, incorporating the vehicle's fixed cost, transportation costs, and carbon emission costs. This study starts from the generation of initial solutions, without considering carbon emission costs, and then takes into consideration three aspects of carbon emissions costs in the analysis. To enhance the efficiency of the optimization process, an improved version of NNC's genetic algorithm was designed, and the following conclusions were obtained. 1. The quality of the initial solution plays a crucial role in the efficiency of the genetic algorithm. The initial solution produced by the INNC method in this study can directly generate feasible solutions that meet the constraints and exhibit a quicker convergence speed. 2. The results of the test cases demonstrate that the genetic algorithm with INNC initial population yields superior quality initial solutions compared with the random generation and random traversal insertion methods. Furthermore, it surpasses the random generation and random traversal insertion methods in vehicle utilization, total transport distance, total cost, and running time. 3. The genetic algorithm with INNC initialization population is utilized to simulate and solve both the model without considering carbon emission costs and the model considering carbon emission costs. The results show that the model, when taking into account carbon emission costs, reduces the carbon emission cost and the total cost by an average of 4.6914% and 0.3578%, respectively, although the transportation cost increases by an average of 0.5127%. Therefore, the vehicle path model that considers carbon emissions can lower the carbon emission cost and the total distribution cost, aligning with the requirements of a low-carbon economy and offering a valuable foundation for decision-making in the logistics industry within a low-carbon economy. Based on the limitations of this paper, further in-depth research can be carried out with regards to the following three aspects: (1) the model is simplified in this paper, and considering traffic congestion and mixed fleet vehicle path optimization models will be important research directions in the future; (2) this paper mainly focuses on the description of the research methodology and the results, so the search for a specific solution for the field of logistics under low-carbon emissions will be the next research focus; and (3) in the distance calculation, the actual road network is not taken into account, so how to incorporate the road traffic network and other practical factors into the model will be the focus of upcoming research. Conceptualization, F.C. and S.J. ; methodology, S.J. and W.G. ; software, S.J. ; validation, F.C., S.J. and W.G. ; formal analysis, F.C. ; investigation, S.J. ; resources, S.J. ; data curation, S.J. and W.G. ; writing--original draft preparation, S.J. and W.G. ; writing--review and editing, F.C. and S.J. ; visualization, S.J. ; supervision, F.C. and S.J. ; funding acquisition, S.J. All authors have read and agreed to the published version of the manuscript. This research is funded by the Major program for Innovation-Driven Development of Guangxi (GuikeAA19254030), the Guangxi Natural Science Foundation Project (2019 GXNS-FAA245011), the Guangxi Ten Thousand Talents Program project (NO. 2019047), and the Science and Technology Innovation Guidance Program Project of Guilin city (20210221). Not applicable.