Academic Journal of Engineering and Technology Science, 2023, 6(2); doi: 10.25236/AJETS.2023.060206.
Wu Xiangfeng
Shandong Polytechnic, Jinan, Shandong, China
At present, the traditional method of logistics distribution center location is mainly achieved through dynamic planning of location. When the scale of the problem is large, it is easy to produce large deviation, and the optimization effect is not ideal. For this reason, a method design of logistics distribution center location based on particle swarm optimization is proposed. This method effectively improves the learning factor and location formula of particle swarm optimization, and establishes a mathematical model of logistics distribution center location. The experimental results show that the proposed method has higher convergence and better search ability.
particle swarm algorithm; logistics delivery center; site selection method; learning factor; site selection model
Wu Xiangfeng. Design of logistics distribution center location method based on particle swarm optimization. Academic Journal of Engineering and Technology Science (2023) Vol. 6, Issue 2: 40-44. https://doi.org/10.25236/AJETS.2023.060206.
[1] Wu J P, Liu L S, Gao J J, et al. Study on cascade hydropower alternative schemes based on multi-objective particle swarm optimization algorithm [J]. Energy Reports, 2020, 6:235-242.
[2] Peng D, Tan G, Fang K, et al. Multiobjective Optimization of an Off-Road Vehicle Suspension Parameter through a Genetic Algorithm Based on the Particle Swarm Optimization [J]. Mathematical Problems in Engineering, 2021, 2021(9):1-14.
[3] Hannachi M, Elbeji O, Benhamed M, et al. Optimal tuning of proportional-integral controller using particle swarm optimization algorithm for control of permanent magnet synchronous generator based wind turbine with tip speed ratio for maximum power point tracking [J]. Wind Engineering, 2021(2):45.
[4] Yang W, Chen L, Wang Y, et al. Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism [J]. Computational Intelligence and Neuroscience, 2020, 2020:1-26.
[5] Jafari M, Salajegheh E, Salajegheh J. Optimal design of truss structures using a hybrid method based on particle swarm optimizer and cultural algorithm [J]. Structures, 2021, 32:391-405.
[6] Zheng S, Zhou X, Zheng X, et al. Improved Quantum-Behaved Particle Swarm Algorithm Based on Levy Flight [J]. Mathematical Problems in Engineering, 2020, 2020:1-10.