随着现代制造业的飞速发展，企业在生产效率和生产能耗方面有越来越高的要求，智能生产车间的自动化程度逐渐提高。主要研究作业车间自动导引车（automated guided vehicle，AGV）的智能绿色物流调度问题。首先，建立以降低AGV能耗和最优AGV路径为目标的AGV物流调度优化模型；然后，提出一种以任务排序为约束的改进遗传粒子群算法；最后，以某针织车间的实际物流调度为例对文中方法进行验证。计算结果表明，文中提出的AGV物流调度模型能够较好地模拟AGV绿色调度耗能问题，提出的改进遗传粒子群算法具有较快的收敛速度和较好的寻优能力。
With the rapid development of modern manufacturing industry, enterprises are required to have higher production efficiency and lower production energy consumption, resulting in more improvement of the automation degree of intelligent production workshop. This paper mainly studied green intelligent logistics scheduling of automated guided vehicle(AGV) in job shop. AGV logistics scheduling optimization model was establised to reduce the energy consumption of AGV and optimize AGV path. A genetic particle swarm optimization (PSO) algorithm was proposed with task sequencing as the constraint condition. Finally, the actual logistics scheduling of a knitting shop was taken as example to verify the method proposed in this paper. The calculation results show that the AGV logistics scheduling model proposed can well simulate the AGV green scheduling energy consumption, and the improved genetic particle swarm optimization algorithm presents a faster convergence speed and a better optimization ability.