Logistics automation control based on machine learning algorithm
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In order to improve the logistics problem, taking automated logistics system as research platform, a new optimization algorithm is proposed for the route planning of multi-goods picking operation of stacker in stereoscopic warehouse. First, the hardware composition of the automated logistics system is introduced, and then the characteristics of the picking operation of the stacker are deeply analyzed. According to these characteristics, a mathematical model for the time cost of the sorting operation is set up. Various algorithms for solving the problem are analyzed and compared. Aiming at the advantages and disadvantages of ant colony system and parthenogenetic algorithm, the two algorithms are properly improved and fused, and a new improved algorithm—parthenogenetic ant colony algorithm is proposed. The validity is verified by the simulation experiment. The simulation is carried out in the Matlab environment, and the satisfactory optimization results are obtained. The simulation result shows that the algorithm is used to optimize the picking path of the stacker. Therefore, it is concluded that the parthenogenetic algorithm greatly reduces the time of the picking operation, and greatly improves the efficiency.
KeywordsAutomated stereoscopic warehouse Stacker Picking Route optimization Parthenogenetic ant colony algorithm Configuration
1. This study was supported by the National Natural Science Foundation of China (Nos. 61741203, 61163012 and 71462005); 2. Guangxi Natural Science Foundation (No. 2016GXNSFAA380243); 3. Guangxi innovation-driven development of special funds project (No. Gui Ke AA17204011); 4. Research Foundation of Guangxi Teachers Education University in 2017.
- 3.Dede, A., Giustina, D.D., Massa, G., et al.: Toward a new standard for secondary substations: the viewpoint of a distribution utility. IEEE Transacti. Power Deliv. 99, 1 (2017)Google Scholar
- 5.Ebben, M.J.R.: Logistic control in automated transportation networks. Prod. Eng. 5(4), 373–382 (2017)Google Scholar
- 7.Yin, Z., Du, C., Liu, J., et al.: Research on auto-disturbance-rejection control of induction motors based on ant colony optimization algorithm. IEEE Transact. Ind. Electron. 99, 1 (2017)Google Scholar
- 8.Volochienko, V.: Improvement of automated control systems for production and production logistics development. Управление 3(2), 22–26 (2015)Google Scholar
- 10.Vytautas, Ostasevicius, Rolanas, Dauksevicius: Intelligent systems, control and automation: science and engineering. Prov. Med. Surg. J. (1844–1852) 12(6), 154–159 (2016)Google Scholar