# Low-Carbon Logistics Distribution Vehicle Routing Optimization Based on INNC-GA
## 5 Comparison and Analysis of Experimental Results
### Impact of Low Carbon Constraints  
To further investigate the impact of carbon emissions on vehicle distribution paths and the performance of the INNC algorithm, this study focuses on the first 50 customer points from each of the six arithmetic sets: C101, C201, R106, R206, RC108, and RC208. Two scenarios are considered: one without considering carbon emission costs (referred to as X) and the other accounting for carbon emission costs (referred to as Y). Computational simulations are conducted, and the results are presented in Table 5. To enhance comprehension of cost variations, the increases and decreases of each arithmetic set are calculated \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **RGM** & **RTIM** & **INNC** \\ \hline The algebra of finding feasible solutions & 256 & 3 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Constraint Violation. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Number of** & **Total Cost** & **Total Transport Distance** & **Fixed Cost** & **Running Time** \\  & **Vehicles(N)** & **(TC)** & **(TTD)** & **(FC)** & **(RT)** \\ \hline RGM & 14.00 & 9.88 K & 3.22 km & 8.00 K & 14.06 s \\ RTIM & 9.00 & 6.47 K & 2.47 km & 4.50 K & 11.74 s \\ INNC & 8.00 & 5.68 K & 2.10 km & 4.00 K & 11.68 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effect of the Initial Solution on the Genetic Algorithm. separately, and their average values are used to represent the final outcome, as outlined in Table 6.  
(1) Comparison of solution results  
The analysis of graph 6 reveals that incorporating carbon emission costs into the vehicle path optimization solution results in higher transport costs compared with solutions that do not consider carbon emissions. However, the carbon emission cost and total cost are notably lower than those of models that do not factor in carbon emissions. By examining Tables 4 and 5, the following conclusions can be derived:  
(1) The results show that the inclusion of carbon emission costs in the target model leads to an average increase in transport costs of 0.5127%. However, there is a significant reduction of 4.6914% in the carbon emission cost and a reduction of 0.3578% in the total cost compared with the model without carbon emission costs. Cost reduction for logistics companies can firstly improve competitiveness: in a competitive market, cost advantage is an important factor in attracting and retaining customers; secondly, it can optimize operational efficiency: this helps to reduce wastage of resources, improve productivity, and enable logistics operators to run their business more efficiently; thirdly, it can reduce operational risk: cost control not only improves profitability, but also makes companies more risk resilient and able to cope with market fluctuations and uncertainties; fourthly, it is in line with regulatory requirements: many countries and regions have relevant environmental regulations that require companies to reduce carbon emissions, and logistics operators can better comply with regulations and legal issues. Overall, cost reduction plays an important role for logistics companies, so logistics operators should pay attention to cost control and environmental awareness to achieve better business results and social benefits. (2) The target model after considering carbon emission costs reduces carbon emission costs, although the transport costs and transport distances increase. This not only helps logistics companies to achieve sustainable development by conserving resources and