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With the rapid growth of service computing and collaborative computing, customers increasingly demand Quality of Service (QoS), and the assignment of service composition suppliers has become critical to realizing high-quality delivery. Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) has gained much attention as a conceptual model for Role-Based Collaboration (RBC). Extending E-CARGO-based Group Multi-Role Assignment (GMRA), we propose a similarity-constrained Team Multi-Role Assignment (TMRASC) model applicable to the service computing domain, which bridges the core of RBC with QoS to form a QoS-driven supplier assignment. The model integrates subservice similarity and common team goals to maximize group performance and synergy. The TMRASC is formally modeled and a Gurobi solution based on Mixed-Integer Linear Programming (MILP) is designed. The simulation experiments demonstrated its effectiveness and robustness across large-scale and complex constraint scenarios, intending to provide new ideas for solutions in the area.

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Research on Service Composition Supplier Assignment Strategy Based on E-CARGO with QoS Driving

  • 会议论文

  • 发表日期：

    2025年

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  • Wenyi Mao,
  • Ruiling Gao,
  • Wen’an Tan,
  • Dongsheng Qi,
  • Jin Zhang,
  • Jieying Chen,
  • Guangming Chen

摘要

With the rapid growth of service computing and collaborative computing, customers increasingly demand Quality of Service (QoS), and the assignment of service composition suppliers has become critical to realizing high-quality delivery. Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) has gained much attention as a conceptual model for Role-Based Collaboration (RBC). Extending E-CARGO-based Group Multi-Role Assignment (GMRA), we propose a similarity-constrained Team Multi-Role Assignment (TMRASC) model applicable to the service computing domain, which bridges the core of RBC with QoS to form a QoS-driven supplier assignment. The model integrates subservice similarity and common team goals to maximize group performance and synergy. The TMRASC is formally modeled and a Gurobi solution based on Mixed-Integer Linear Programming (MILP) is designed. The simulation experiments demonstrated its effectiveness and robustness across large-scale and complex constraint scenarios, intending to provide new ideas for solutions in the area.