基于E-CARGO的扁平团队或组织的绩效量化预测  

PREDICTION OF QUANTIFIYING PERFORMANCE FOR THE FLAT TEAM AND ORGANIZATION BASED ON E-CARGO MODEL

作　　者：陈振 汤志成 高海波[2] Chen Zhen;Tang Zhicheng;Gao Haibo(School of Electronic Information,Hunan University of Information Technology,Changsha 410148,Hunan,China;School of Information and Mechanical and Electrical Engineering,Hunan International Economics University,Changsha 410205,Hunan,China)

机构地区：[1]湖南信息学院电子信息学院,湖南长沙410148 [2]湖南涉外经济学院信息与机电工程学院,湖南长沙410205

出　　处：《计算机应用与软件》2021年第9期299-306,共8页Computer Applications and Software

摘　　要：预测与量化团队或组织的绩效对于团队管理至关重要。采用E-CARGO建模,提出常规多任务组绩效预测算法(PGPMTRAP)。算法把常规多任务组的质量评估矩阵转置为组角色分配问题(GRAP)矩阵,再利用GRAP算法来完成多任务分配获取常规多任务角色组的最大性能分配矩阵的性能值,以实际分配矩阵性能值与最大分配矩阵性能值的比值作为常规多任务组的量化预测绩效,且对算法进行实验验证。利用GRAP算法与PGPMTRAP算法实现扁平团队的绩效量化预测,对该算法与GRAP算法分别进行实例验证。To predict and quantify the performance of team and organization is critical to team management.Using the environments-class,agents,roles,groups and objects(E-CARGO)model,this paper proposes a general multi-task group performance prediction algorithm(PGPMTRAP).This algorithm converted the general multi-task group quality evaluation matrix into the group role assignment problem(GRAP)matrix and obtained the maximum performance value of the assignment matrix of the general multi-task role group by GRAP algorithm.The algorithm used the rate ratios between the performance value of the actual allocation matrix and the maximum performance allocation matrix for the quantifying performance of the general multitask group,which was verified by experiments.GRAP and PGPMTRAP algorithm were used to achieve the prediction of quantifying performance for the flat team,and were respectively verified by examples.

关 键 词：RBC 扁平团队 预测与量化绩效 组角色分配 E-CARGO模型 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]