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先序约束下的群组角色指派及其优化 被引量:2

Group Role Assignment and its Optimization with Preorder Constraints
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摘要 在协同工作中如团队分工明确具体,协作将轻松易行,然而复杂的数据耦合、时空冲突等约束关系往往制约了任务的分工和指派.先序约束是最重要而又难于处理的约束之一,其体现了任务分发的先决条件关联.为此本文于指派模型中引入角色,使用角色对任务分工进行抽象与建模,并对先序约束下的指派作表达与计算.相关问题的穷举处理时间复杂度为Σ2P级,为优化加速,论文提出了能快速收敛的多对多线性指派规划算法,并用IMB ILOG CPLEX软件包进行了模拟仿真.经比较,相关方法的优化率可达80%~100%,均值为94%,能满足有限时间内对问题处理规模与团队性能保持的要求,为团队协作与生产管理提供了有效支撑. If everyone or a unit in a team is assigned to specific work,the cooperation between teammates will be much easier than that without specific assignments.Nonetheless,due to the complexity of data coupling and spacetime,the assignments with conflict constraints are a big challenge.As one of the most important but intractable constraints,the preorder constraint determines the prerequisites of assignments.Therefore,roles are introduced to abstract and model the assignment problem and express the assignment with the preorder constraints.Tested by the exhaustive method,the complexity of the proposed problem is ofΣ2P.In order to optimize the solution of the problem and accelerate the processing speed,a multiple objective linear programming approach is proposed with the application of IMB ILOG CPLEX.To verify the proposed approach,simulation experiments are conducted.The optimization rate of the proposed approach could reach80%to100%,average94%,which can meet the requirements of solving a certain number of problems within limited time as well as guarantee an excellent team performance and hence help support collaboration and management effectively.
作者 刘冬宁 卢明俊 黄宝莹 梁路 Liu Dong-ning;Lu Ming-jun;Huang Bao-ying;Liang Lu(School of Computers, Guangdong University of Technology, Guangzhou 510006, China)
出处 《广东工业大学学报》 CAS 2017年第3期21-29,共9页 Journal of Guangdong University of Technology
基金 国家自然科学基金资助项目(61402118 61673123) 广东省科技计划项目(2015B090901016 2016B010108007) 广东省教育厅项目(粤教高函2015[133]号 粤教高函[2014]97号) 广州市科技计划项目(201604020145 2016201604030034 201508010067)
关键词 角色协同 群组角色指派 先序约束 线性指派规划 大数据 role-based collaboration group role assignment preorder constraint linear assignment problem big data
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