云制造环境下的动态调度  

Dynamic Scheduling in Cloud Manufacturing Environment

作　　者：李晓辉[1] 王雪茹 赵毅 李沛帆 冉保健 LI Xiao-Hui;WANG Xue-Ru;ZHAO Yi;LI Pei-Fan;RAN Bao-Jian(School of Electronics and Control Engineering,Chang’an Universicty,Xi’an 710064,China)

机构地区：[1]长安大学电子与控制工程学院,西安710064

出　　处：《计算机系统应用》2021年第7期225-231,共7页Computer Systems & Applications

基　　金：工信部国家物联网重点研发项目(2019ZDLGY03-01);陕西省重点产业链项目(201805045YD23CG29)。

摘　　要：在云制造环境下,制造资源和制造能力以服务的形式封装起来,不同的任务通过云端汇集到云平台并通过合适的调度给每个任务分配相应的服务.由于任务在执行的过程中的不确定性,会在某个时刻遇到突发状况从而导致对余下任务的重调度问题.因此,针对该问题,考虑到云制造环境下任务的复杂性和多样性会导致在合理的时间段内很难找到最优解,以所有任务的最大完成时间为优化目标,提出了一种以改进的遗传算法与邻域搜索技术相结合的元启发式算法,旨在解决云制造环境下由于任务和资源服务等的不确定性导致的重调度问题.实验结果表明,本文所提出的算法能够很好地解决动态调度过程中的重调度问题,并可以快速地获取最优解.In the cloud manufacturing environment,manufacturing resources and capabilities are encapsulated in the form of services.Different tasks are collected to the cloud platform via the cloud and corresponding services are assigned to each task through appropriate scheduling.Due to the uncertainty in task execution,emergency can take place,forcing the remaining tasks to be rescheduled.In addition,the complexity and diversity of tasks in the cloud manufacturing environment will lead to difficulty in finding the optimal solution within a reasonable time period.With the maximum completion time of all the tasks as the optimization goal,a metaheuristic algorithm that combines an improved genetic algorithm and the neighborhood search technique is proposed to tackle the rescheduling caused by the uncertainty of tasks and resource services in the cloud manufacturing environment.The experimental results show that the proposed algorithm can deal with the rescheduling during dynamic scheduling and obtain the optimal solution quickly.

关 键 词：遗传算法 动态调度 云制造调度 邻域搜索 重调度 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP393.09[自动化与计算机技术—控制科学与工程]