Abstract: In order to solve the problems of task scheduling and buffer deadlock for a multi-load AGV system，a deadlock-free task scheduling scheme with task traveling time was proposed.Firstly，the task scheduling model was established with the objective of minimizing the delay rate and the imbalance degree of traffic loads.Secondly，the practical constraints in task scheduling were analyzed，and a prediction model was developed under the constraint of task traveling time.Thirdly，a multi-objective deadlock-free task scheduling method was proposed based on the AI-GWO algorithm for the task scheduling model.Deadlock prevention rules were used to prohibit tasks that would cause the buffer deadlock of workstations, and the AI-GWO algorithm was combined to perform the multi-objective optimization for the operation sequence of tasks.Finally，the tasks were assigned to different AGVs according to the balance degree of AGV loads.The simulation results show that the task-traveling time prediction model achieves high accuracy，and the task scheduling model and the deadlock-free scheduling method have the satisfactory optimization performance and computation efficiency.Therefore，the task punctuality rate of the material handling systems and the traffic-load balance degree of the path network are improved significantly.

Key words: automated guided vehicle（AGV）, task scheduling, multi-objective optimization, task traveling time, artificial immune-grey wolf optimization（AI-GWO） algorithm

摘要： 针对多载量自动导引车（AGV）系统的任务调度和缓冲区死锁问题，提出了考虑任务行程时间的防死锁任务调度方案。以最小化延迟率和交通负荷不均衡度为目标，建立了任务调度模型；分析了任务调度中的实际约束，并在任务行程时间约束下构建了预测模型；针对任务调度模型，提出了一种基于人工免疫-灰狼优化（AI-GWO）算法的多目标防死锁任务调度方法，利用死锁避免规则禁止即将引发工位缓冲区死锁的任务运行，并融合AI-GWO算法对任务执行顺序进行多目标优化；最后，根据AGV负载均衡度进行AGV任务分配。仿真结果表明，上述任务行程时间预测模型具有较高的准确率，任务调度模型及防死锁调度方法具有较好的优化性能和计算效率，从而显著提高了物流系统的任务准时率和路径网络的交通负荷均衡度。

关键词: 自动导引车, 任务调度, 多目标优化, 任务行程时间, 人工免疫-灰狼优化算法