Forward and reverse courier logistics distribution path planning based on multi-objective optimization genetic algorithm

Jingtian Tang; Chenxi Xu

doi:10.54097/hset.v31i.4809

Authors

Jingtian Tang
Chenxi Xu

DOI:

https://doi.org/10.54097/hset.v31i.4809

Keywords:

multi-objective model, reverse logistics, genetic algorithm.

Abstract

In order to optimize the express logistics distribution path and improve the distribution efficiency, the multi-objective optimization model of express logistics distribution path with mixed time windows is proposed. The model takes into account the requirements of express substations for on-time arrival, express enterprises' control of logistics costs, quantifying customer satisfaction with on-time arrival rate, and constituting logistics costs with fixed costs and transportation costs. Based on the multi-objective optimization genetic algorithm, the Pareto solution set is obtained, and the optimal solution is selected by combining four evaluation methods. The feasibility of the method is demonstrated by applying the method to the case of a region in North China.

Downloads

Download data is not yet available.

References

Tian Shuaihui, Ou Liying. Multi-objective optimization of urban distribution paths with time windows based on improved genetic algorithm [J]. Logistics Technology, 2021, 44(11):7.

Chang H-P, Li W-Y, Dong F-G, et al. Multi-objective optimization of cold chain logistics distribution path based on NSGA-II[J]. Transportation Technology and Economy, 2022, 24(2):10.

Xu S. F., Jiang M. Y., Deng Y. R.. Research on dynamic path optimization of integrated reverse logistics cooperative distribution [J]. Journal of Management Science, 2021,24(10):106-126.

Deb K , Pratap A , Agarwal S , et al. A fast and elitist multiobjective genetic algorithm: NSGA-II [J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2):0-197.

Deb K , Jain H . An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints[J]. IEEE Transactions on Evolutionary Computation, 2014, 18(4):577-601.

Gen M,CHeng R. Genetic Algorithms and Engineering Optimization[M]. New York: John Wiley & Sons,2000

Yuan Yuguo. Research on the "last mile" vehicle routing problem for simultaneous pickup and delivery [J]. Journal of Huaihua University, 2020, 39(6):6.

Wang Jie, Fei Peng, Chen Kai. Supply ship route planning based on dynamic demand under mixed time windows [J]. Journal of Chongqing Jiaotong University: Natural Science Edition, 2021.

Shao Kenan, Lv Chengyao, Zhang Shuaishuai, et al. A hybrid optimization algorithm based on cold chain low-carbon logistics path [J]. Computer Technology and Development, 2021, 31(2):6.

Zhang YN. A method for solving TSP problems based on the Geatpy library [J]. Electronic Technology and Software Engineering, 2021(3):2.