Logistics automation control based on machine learning algorithm

• Published in: Cluster computing Vol. 22; no. Suppl 6; pp. 14003 - 14011
• Main Authors: Yu, Xiaomo, Liao, Xiaoping, Li, Wenjing, Liu, Xinquan, Tao, Zhang
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2019; Springer Nature B.V
• Subjects: Ant colony optimization; Automation; Communication; Computer Communication Networks; Computer Science; Conveyor lines; Economic development; Efficiency; Genetic algorithms; Integer programming; Logistics; Machine learning; Manufacturing; Operating Systems; Optimization algorithms; Picking; Processor Architectures; Product life cycle; Route optimization; Route planning; Simulation; Software; Sorting algorithms; Traveling salesman problem; Warehouses; Automated stereoscopic warehouse; Stacker; Configuration; Picking; Parthenogenetic ant colony algorithm; Route optimization
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-018-2169-0

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.