Jinglong Xiong, Yiping YuanThis email address is being protected from spambots. You need JavaScript enabled to view it., Yongsheng Chao, Ming Li, Adilanmu Sitahong, and Peiyin Mo
Intelligent Manufacturing Modern Industry College, Xinjiang University, Urumqi, 830017, China
Received: September 24, 2024 Accepted: February 1, 2025 Publication Date: April 30, 2025
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
Focusing on the lack of effective methods for collaborative scheduling of AGV (Automated Guided Vehicle) in ring spinning workshops. Under various constraints covering point demand constraint, path flow constraint, AGV capacity constraint, starting point constraint and variable constraint, an AGV scheduling model for spinning workshops is constructed. The model aims to minimize AGV moving distance and the maximum completion time. A model solution method based on simulated annealing ant colony optimization (SAACO) is proposed. SAACO combines the advantages of Simulated Annealing Algorithm (SA) and Ant Colony Algorithm (ACO), SAACO is not easy to fall into the local optimal solution and has higher model solving efficiency. The simulation of actual data shows that when the number of cans is 60, respectively compared with the traditional SA and ACO, the SAACO method reduces the total distance of AGV movement by 34.83 m and 24.13 m , the maximum completion time by 15 s and 13 s , the algorithm running time by 20%−30%. This approach can reduce the operational costs of AGV in spinning workshops, enhance workshop efficiency, and provide a novel solution for the cooperative scheduling of AGV in such settings.
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