基于最大依赖度及最小冗余度的员工协作优化策略  

Staff collective optimization strategy based on maximal dependency and minimal redundancy

作　　者：许荣斌[1,2,3] 鲍广华[4] 杨培全[2,3] 汪欣梅 谢莹[1,3,4] 

机构地区：[1]安徽大学计算智能与信号处理教育部重点实验室,安徽合肥230601 [2]安徽大学计算机科学与技术学院,安徽合肥230601 [3]安徽大学信息保障技术协同创新中心,安徽合肥230601 [4]安徽大学大学计算机教学部,安徽合肥230601

出　　处：《计算机集成制造系统》2017年第5期1014-1019,共6页Computer Integrated Manufacturing Systems

基　　金：国家自然科学基金资助项目(61602005);教育部人文社会科学青年基金资助项目(14YJCZH169);安徽省自然科学面上基金资助项目(1608085MF130);安徽高校人文社会科学重点资助项目(SK2016A007);安徽大学博士科研启动经费资助项目~~

摘　　要：为了使业务过程管理工作中的员工分配工作自动地进行、提高人员工作的效率,提出一种新算法。新算法考虑员工的认知特征,基于商业过程模型中的流程链接关系以及员工间的协作特性,研究员工协作认知特征的依赖度和冗余度属性,采用增量搜索策略自动分配员工。将新算法应用于制造企业相关数据集,结果表明,该算法能够在全体员工集合中寻找具备最大依赖度和最小冗余度特性的子员工集合、提高员工协作的效率。To complete staff assignment automatically in business processes management, a novel algorithm which could greatly improve the efficiency of staff working and reduce the cost for enterprises was proposed,. By considering the cognitive character, the dependency and redundancy of staff collective behavior based on business processes model were researched, and the increment search strategy was adopted to assign staff automatically. The proposed method was applied on related manufacture datasets to find a staff subset that had maximal dependency and minimal redundancy in the whole staff set. The plentiful experiment results showed high efficiency of staff collective behavior.

关 键 词：员工分配 资源管理 协作行为 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]