Abstract: In the condition of Job Shop, aiming at minimizing the maximum completion time, considering the influence of handling equipment and the limitation of buffer on the processing process, an integrated scheduling model of Job Shop processing and handling with limited buffers is established, and an improved artificial bee colony optimization algorithm is designed to solve the problem. Firstly, the mechanism exchange between the role of leader bee and follower bee is introduced in the algorithm, which can give better consideration to global wide optimization and local precise optimization. Secondly, based on the particularity of the problem, the process is not only a processing task but also a handling task, so the coding method is based on the process coding, which is convenient for decoding and calculation in the process of algorithm operation. Then, in the decoding process, in order to improve the efficiency of the algorithm, the heuristic information is designed to determine decoding processing tasks and handling tasks. Finally, the recommended range of population size of G-ABC algorithm is given by comparing the standard test cases. The paper proves the validity of G-A BC algorithm, the effectiveness of heuristic information and the influence of buffer capacity setting on scheduling results.

Key words: Job Shop, limited buffers, handling equipment, integrated scheduling, bee colony al gorithm

摘要： 在Job Shop环境下,以最小化最大完工时间为目标,考虑了搬运设备与有限缓存区对加工过程的影响,建立了带有限缓存区的Job Shop加工与搬运集成调度模型,并设计了改进的人工蜂群优化算法求解此问题。首先,在算法中引入了引领蜂和跟随蜂角色互换的机制,可更好的兼顾全局广泛寻优和局部精确寻优。其次,基于问题的特殊性,工序既是加工任务也是搬运任务,所以在编码方式上采取基于工序编码,便于算法运行过程中解码计算。然后,在解码过程中,为提高算法运行效率,设计了如何确定解码加工任务和搬运任务的启发式信息。最后,通过标准测例实验比较,给出了本文G-ABC算法种群规模的建议取值范围,并证明了G-ABC算法的有效性,启发式信息的有效性以及缓存区容量设置对调度结果的影响。

关键词: Job Shop, 有限缓存区, 搬运设备, 集成调度, 蜂群算法