随着协同计算的不断进步,环境-类、Agents、角色、组和对象(environments-classes,Agents,roles,groups and objects,E-CARGO)模型作为一种基于角色协同(role-based collaboration,RBC)技术受到了广泛关注.通过对基于E-CARGO的组多角色分配(group multi-role assignment,GMRA)模型开展研究,提出了一个普适度更高的角色依赖的团队多角色分配(team multi-role assignment with role dependency,TMRARD)模型,涉及到引入角色依赖关系和团队共同目标后,如何将协作单元(Agents)有效分配给角色,以最大化群体表现与协同效应.将TMRARD形式化建模,针对角色依赖约束的复杂性,对依赖输入规模进行合理性分析,设计出基于混合整数线性规划(mixed-integer linear programming,MILP)的Gurobi求解策略,并用仿真实验验证了该方法的有效性与鲁棒性,在大规模和复杂约束情形下性能显著,以期为协同计算的决策支持提供新的思路.
E-CARGO-Based Team Multi-Role Assignment Problem with Role Dependency
With the continuous progress of collaborative computing,the environments-classes,Agents,roles,groups and objects(E-CARGO)model has received much attention as a role-based collaboration(RBC)technique.By carrying out research on the E-CARGO-based group multi-role assignment(GMRA)model,a team multi-role assignment with role dependency(TMRARD)model with higher pervasiveness is proposed,which involves how to efficiently assign collaborative units(Agents)to roles to maximize group performance and synergistic effects after the introduction of role-dependency relationships and team common goals.The TMRARD is formally modeled,the scale of dependent inputs is rationally analyzed with respect to the complexity of role-dependent constraints,and the Gurobi solution strategy based on mixed-integer linear programming(MILP)is designed,and simulation experiments are conducted to verify the validity and robustness of the method,which has a significant performance under large-scale and complex constraints,with a view to providing a new way of thinking for the decision-making support of collaborative computing.
collaborative computingrole dependencyteam multi-role assignmentmixed-integer linear programmingGurobi solver
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