Automated Logistics Control Model Based on Improved Ant Colony Algorithm

被引：0
作者：
School of Business, Chongqing City Management College, Chongqing [1 ]
401331, China
机构：
[1] School of Business, Chongqing City Management College, Chongqing
来源：
Informatica | 2024年 / 16卷 / 13-26期
关键词：
ant colony algorithm; automated logistics; logistic chaos mapping; path optimization; Razvit je avtomatiziran logistični nadzorni model za optimizacijo logističnih poti; ki zniža stroške ter izboljša dostavo. Temelji na izboljšanem algoritmu mravelj in kaotičnem logističnem preslikavanju; stacker crane;
D O I：
10.31449/inf.v48i16.6371
中图分类号：
学科分类号：
摘要：
With the rapid development of modern logistics industry, traditional automated logistics control systems often lack transparency and visualization of the entire supply chain. It cannot comprehensively manage and optimize the entire supply chain. Therefore, an automated logistics control model is constructed based on ant colony algorithm and logistic chaotic mapping. By simulating the pheromone transmission process of ants, the optimal logistics transportation path is found. From the experimental results, the improved Ant Colony Algorithm (ACA) was tested on the DT100dataset, achieving an optimal solution within 200 iterations. Compared with traditional methods, the cost was reduced by 0.25 units. The distance solution image of the improved ACA was overall concave downwards, with a significant decrease after 20 iterations. The designed automated logistics control system used map APIs and sensor data to obtain real-time delivery routes. On average, each logistics node consumed 1.01% of electricity. Compared with traditional methods, it had the highest prediction accuracy, with an R2 of 0.98. In summary, the improved ant colony algorithm can optimize logistics delivery paths, reduce delivery time and cost, improve delivery efficiency, and reduce delivery energy consumption. © 2024 Slovene Society Informatika. All rights reserved.
引用
收藏
页码：13 / 26
页数：13
相关论文
共 20 条
  • [1] Han Q., Sun Y., Wu Q., Bai Z., Research on optimization model of logistics transportation truck path considering environmental impact: experimental data from xiqing district, Tianjin, Journal of Advanced Transportation, 2, 5, pp. 786-796, (2021)
  • [2] Delgoshaei A., Norozi H., Mirzazadeh A., Farhadi M., Pakdel G. H., Aram A. K., A new model for logistics and transportation of fashion goods in the presence of stochastic market demands considering restricted retailers capacity, RAIRO-Operations Research, 55, 9, pp. 523-547, (2021)
  • [3] Wang X., Guan N., Yang J., Image encryption algorithm with random scrambling based on one-dimensional logistic self-embedding chaotic map, Chaos Solitons & Fractals, 150, 3, pp. 111-117, (2021)
  • [4] Hwang W., Kang D., Kim D., Brain lateralisation feature extraction and ant colony optimisation-bidirectional LSTM network model for emotion recognition, IET Signal Process, 16, pp. 45-61, (2021)
  • [5] Brodetzki T. R., Brodetzki G., Feinerman O., Hefetz A., Worker demography and behavior in a supercolonial ant colony: The case of the desert ant Cataglyphis niger, Ethology, 8, 3, pp. 876-889, (2020)
  • [6] Guven A. F., Yrukeren N., Samy M. M., Design optimization of a stand-alone green energy system of university campus based on jaya-harmony search and ant colony optimization algorithms approaches, Energy, 253, 7, pp. 675-687, (2022)
  • [7] Moradi B., Kargar A., Abazari S., Transient stability constrained optimal power flow solution using ant colony optimization for continuous domains (ACO_R), IET Generation, Transmission & Distribution, 7, 9, pp. 786-798, (2022)
  • [8] Li C., Jiang K., Luo Y., Dynamic placement of multiple controllers based on SDN and allocation of computational resources based on heuristic ant colony algorithm, Knowledge-Based Systems, 241, 7, pp. 108-119, (2022)
  • [9] He W., Meng S., Wang J., Wang L., Pan R., Gao W., Weaving scheduling based on an improved ant colony algorithm, Textile Research Journal, 91, 6, pp. 543-554, (2021)
  • [10] Pontes A. S. D., Araujo A. S. D., Marinho W., Ant colony optimization for variable selection in discriminant linear analysis, Journal of Chemometrics, 77, 4, pp. 897-903, (2020)
12