Research on O2O Takeout Delivery Route Optimization Problem Based on Improved Genetic Algorithm

This paper considers the vehicle routing optimization problem for O2O takeout delivery services. In the problem, takeout orders are divided into priority orders and ordinary orders based on whether customers have purchased the on-time delivery service. By comprehensively considering the three parties of platform, customers, and riders, a multi-objective function is established to minimize platform costs, maximize average customer satisfaction, and maximize average rider earnings. For the problem, the ε-constraint method and the weighted sum approach are first employed to transform the multi-objective problem into a single-objective one. Then, an improved genetic algorithm integrated with a local search method is designed. The effectiveness of the proposed algorithm is validated by using instances adapted from the existing literature. By setting the lower bounds for average customer satisfaction, as well as the weights for platform costs and average rider earnings, it can provide flexible and effective decision-making references for decision makers.