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 pc and pm 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. (c) 2022 Published by Elsevier B.V. on behalf of International Association for Mathematics and Computers in Simulation (IMACS).

Automated summary

This study proposes a new adaptive genetic algorithm to solve issues regarding the timeliness and high cost of logistics distribution in the logistics economy. A large neighborhood search algorithm is introduced to enhance local optimization ability. Additionally, a soft time window town logistics distribution model considering optimal cost and customer satisfaction is established. Experimental results demonstrate the effectiveness of the proposed adaptive genetic algorithm.