Flexible Logistics Sorting System Allocation via Hybrid GA-PSO with Dynamic Clustering

Zhaoqing Li; Jinbing Wang; Lei Zhang

doi:10.54097/62beh769

Authors

Zhaoqing Li
Jinbing Wang
Lei Zhang

DOI:

https://doi.org/10.54097/62beh769

Keywords:

Product specification distribution; similarity coefficient of specifications; inertia weight factor; genetic particle swarm dynamic clustering algorithm.

Abstract

To address the challenges posed by the large sorting volume of cigarettes in tobacco logistics distribution centers and the significant impact of cigarette specification allocation on the overall processing time of orders, this study aims to optimize the allocation of each sorting zone and enhance sorting efficiency. A mathematical model is developed with the objective of minimizing the similarity coefficients of specifications within each zone, which is then solved using an enhanced genetic particle swarm dynamic clustering (GA-PSO-K) algorithm. Initially, the similarity coefficient of each specification is improved by incorporating the sorting quantity of each specification into the fitness function. Subsequently, the inertia weight factor in the particle swarm algorithm is adaptively adjusted to enable its dynamic variation. Finally, cross-variance is introduced into the genetic algorithm to expand the solution search space, and the results are compared with those of other algorithms using Matlab. The algorithm’s performance is simulated and validated in an EM-plant environment. In the context of data simulation verification at a tobacco logistics distribution center, the processing time for order handling with the GA-PSO-K algorithm was reduced to 234.5 seconds, significantly outperforming traditional methods and effectively improving the efficiency of flexible logistics sorting. This algorithm leverages the advantages of both particle swarm optimization and genetic algorithms, demonstrating improved convergence and solution quality, thus offering a novel approach for flexible logistics product specification allocation.

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References

[1] DE KOSTER R, LE-DUC T, ROODBERGEN K J. Design and control of warehouse order picking: A literature review[J]. European Journal of Operational Research, 2007, 182(2): 481—501.

[2] ZHANG Yi-gong, WU Yao-hua, GENG Yao-hua. Item assignment optimization of automated picking system based on synchronized zoning strategy[J]. Computer Engineering and Applications, 2011, 47(03): 240—243+248.

[3] LIU Jing-ming, HAN Li-chuan, HOU Li-wen. Cluster Analysis Based on Particle Swarm Optimization Algorithm[J]. Systems Engineering-Theory and Practice, 2005, (06): 54—58.

[4] LI Jian-ming. Research on Items Assignment Method for Zone Automated Sorting System of Logistics Distribution Center[D]. Lanzhou: Lanzhou Jiaotong University, 2017: 16—31.

[5] WANG Yan-yan, WU Yao-hua, WU Ying-ying. SKU Picking Quantity Splitting Optimization of Parallel Automatic Picking System[J]. Journal of Mechanical Engineering, 2013, 49(16): 177—184.

[6] FEAZELLE E H. Stock location assignment and order picking productivity[D]. Georgia Institute of Technology, 1989.

[7] PETERSON C. G, GERALD A. Considerations in order picking zone configuration[J]. International Journal of Operations & Production Management, 2002, 27: 793—805.

[8] WU C. An association-based clustering approach to order batching considering customer demand patterns[J]. Omega, 2005, 33(4): 333—343.

[9] GARFINKEL M. Minimizing Multi-zone orders in the correlated storage assignment problem[D]. Georgia: Georgia Institute of Technology, 2005.

[10] LIU C M. Clustering techniques for stock location and order-picking in a distribution center[J]. Computers & Operations Research, 1999, 26(10-11): 989—1002.

[11] YANG Jiao. Simulation study of tobacco heterogeneous multi-mode sorting system[D]. Guiyang: Guizhou University, 2022: 37—49.

[12] LI Lu-sha. Research on multi-stranded complex tobacco sorting system[D]. Guiyang: Guizhou University, 2022: 22—42.

[13] JANE C, LAIH Y W. A clustering algorithm for items assignment in a synchronized zone order picking system[J]. European Journal of Operational Research, 2005, 166(2): 489—496.

[14] ZHANG Chang-yong, LIU Jia-yu. Optimization of Aviation Container Loading Based on Improved Genetic Algorithm[J]. Packaging Engineering, 2022, 43(11): 253—260.

[15] XU Chen, KANG Xue, YANG Ling. Multi-temporal phase declouding processing based on particle swarm K-means clustering algorithm[J]. Journal of Chengdu University of Information Technology, 2020, 35(04): 424—429.

[16] WU Yan-kai, ZHANG Wei, WANG Ya-gang. Parameter Tuning of Two-degree-of-freedom PID Controller for Plate Cylinder Temperature Based on GA-PSO[J]. Packaging Engineering, 2020, 41(05): 185—191.