# Research on O2O Takeout Delivery Route Optimization Problem Based on Improved Genetic Algorithm
## 6 Conclusions  
The O2O takeout market in China is huge, and the delivery route of orders is an important part of O2O takeout services, which is of great significance for promoting the healthy development of O2O takeout enterprises. In addition, as the direct executors of order delivery, the interests of riders cannot be ignored. Therefore, an optimization problem for O2O takeout delivery route, which comprehensively considered the interests of three parties including the platform, customers, and riders, was addressed in this paper. To address this, we established a multi-objective optimization model aimed at minimizing platform costs, maximizing average customer satisfaction, and maximizing average rider earnings. To solve this model, the \(\varepsilon\)-constraint method and the weighted sum approach were first combined to transform it into a single-objective optimization model. Subsequently, an improved genetic algorithm incorporating a local search method was designed. To verify the effectiveness of the key components of the algorithm, several comparative experiments were conducted. The experimental results indicate that, compared with using the partial matching crossover and order crossover strategies alone, the fitness value of the hybrid crossover strategy decreased by at least approximately 8.12%; compared with using the shortest distance insertion and random insertion strategies alone, the fitness value of the hybrid insertion strategy decreased by at least approximately 4.32%; compared with the genetic algorithm without incorporating a local search strategy, the fitness value of the genetic algorithm integrated with a local search strategy decreased by approximately 40.09%. These results demonstrate the effectiveness of the algorithm's components. To analyze the impact of model parameters on the experimental results, some other comparative experiments were conducted. From the comparative experiments on satis  
Figure 6: Curves of platform costs and average rider earnings with changing weights: (**a**) curve of platform costs with changing weights; and (**b**) curve of average rider earnings with changing weights. action, it can be concluded that, as the lower bound of average customer satisfaction increases from 60% to 90%, both the platform costs and the average rider earnings increase. A satisfaction level of 80% serves as a good reference setting for balancing the interests of all participants. From the comparative experiments on the number of riders, it can be obtained that, as the number of riders increases from 6 to 12, the platform costs, average rider earnings, and average customer satisfaction all increase. A rider number of 10 can be considered as a good reference setting for balancing the interests of all participants. From the comparative experiments on the weights of platform costs and rider earnings, it can be seen that, as the weight of costs (earnings) increases from 0.1 to 0.9, the platform costs (average rider earnings) decrease (increase) continuously. A weight of approximately 0.7 can be used as a reference when there is a greater emphasis on the platform costs (average rider earnings). However, the model presented in this paper also has certain limitations, such as not taking into account real-time takeout orders and vehicle speed limits on roads. Therefore, in future research, these factors can be further incorporated into the model. Conceptualization, X.B. ; Methodology, Y.C. and X.B. ; Software, Y.C. ; Validation, Y.C. and X.B. ; Formal analysis, Y.C. and X.B. ; Investigation, Y.C. ; Resources, X.B. ; Data curation, Y.C. ; Writing--original draft, Y.C. ; Writing--review & editing, Y.C. and X.B. ; Visualization, Y.C. ; Supervision, X.B. ; Project administration, X.B. ; Funding acquisition, X.B. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Natural Science Foundation of China under grant number 11701363. Not applicable.