Route optimization in township logistics distribution considering customer satisfaction based on adaptive genetic algorithm.

With the development of the logistics economy, problems such as the timeliness of logistics distribution and the high cost of distribution have emerged. A new adaptive genetic algorithm is proposed to solve these problems. The p c and p m values of the algorithm are related to the number of iterations and the individual fitness values. To improve the local optimization ability of the algorithm, a large neighborhood search algorithm is proposed. In addition, this study establishes a soft time window town logistics distribution model with constraints. The model considers the optimal cost as the objective function and customer satisfaction as the influencing factor. In the experiment, the proposed adaptive genetic algorithm is compared with the traditional genetic algorithm, validating the effectiveness of the proposed algorithm. • A new mathematical model of customer satisfaction is proposed. • Accurate mathematical expression of the new customer satisfaction model is given. • Some improvements are made to the adaptive genetic algorithm. [ABSTRACT FROM AUTHOR]