Tang Qi, Wang Huan
School of Management, Shenyang University of Technology, Shenyang, 110870, China.
Sci Rep. 2025 Jan 2;15(1):5. doi: 10.1038/s41598-024-82870-1.
The production stage of an automated job shop is closely linked to the automated guided vehicle (AGV), which needs to be planned in an integrated manner to achieve overall optimization. In order to improve the collaboration between the production stages and the AGV operation system, a two-layer scheduling optimization model is proposed for simultaneous decision making of batching problems, job sequences and AGV obstacle avoidance. Under the AGV automatic path seeking mode, this paper adopts a data-driven Bayesian network method to portray the transportation time of AGVs based on the historical operation data to control the uncertainty of the transportation time of AGVs. Meanwhile, a time window is established to control the risk of AGV delay, and a data-driven Bayesian network is constructed to optimize the two-layer scheduling model of automated job shop and AGV. To solve the model, we design an improved particle swarm algorithm combining genetic operators, crossover operators and elite retention operator. The results show that the model in this paper can effectively improve the collaboration between the production stage and AGV operation system within the shop floor, and successfully solve the actual operation scale case to enhance the effectiveness of the production and transportation system.
自动化车间的生产阶段与自动导引车（AGV）紧密相连，需要进行综合规划以实现整体优化。为了改善生产阶段与AGV操作系统之间的协作，提出了一种两层调度优化模型，用于同时决策批量问题、作业序列和AGV避障。在AGV自动寻路模式下，本文采用数据驱动的贝叶斯网络方法，基于历史运行数据描绘AGV的运输时间，以控制AGV运输时间的不确定性。同时，建立时间窗口来控制AGV延误风险，并构建数据驱动的贝叶斯网络以优化自动化车间和AGV的两层调度模型。为求解该模型，设计了一种结合遗传算子、交叉算子和精英保留算子的改进粒子群算法。结果表明，本文模型能够有效改善车间内生产阶段与AGV操作系统之间的协作，并成功解决实际运行规模案例，提高生产与运输系统的有效性。
