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Volume 7 Issue 6
Oct.  2020

IEEE/CAA Journal of Automatica Sinica

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Haibin Zhu, "Group Multi-Role Assignment With Conflicting Roles and Agents," IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1498-1510, Nov. 2020. doi: 10.1109/JAS.2020.1003354
Citation: Haibin Zhu, "Group Multi-Role Assignment With Conflicting Roles and Agents," IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1498-1510, Nov. 2020. doi: 10.1109/JAS.2020.1003354

Group Multi-Role Assignment With Conflicting Roles and Agents

doi: 10.1109/JAS.2020.1003354
Funds:  This work was supported in part by Natural Sciences and Engineering Research Council, Canada (NSERC) (RGPIN-2018-04818) and the funding from the Innovation for Defence Excellence and Security (IDEaS) Program from the Canadian Department of National Defence (DND)
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  • Group role assignment (GRA) is originally a complex problem in role-based collaboration (RBC). The solution to GRA provides modelling techniques for more complex problems. GRA with constraints (GRA+) is categorized as a class of complex assignment problems. At present, there are few generally efficient solutions to this category of problems. Each special problem case requires a specific solution. Group multi-role assignment (GMRA) and GRA with conflicting agents on roles (GRACAR) are two problem cases in GRA+. The contributions of this paper include: 1) The formalization of a new problem of GRA+, called group multi-role assignment with conflicting roles and agents (GMAC), which is an extension to the combination of GMRA and GRACAR; 2) A practical solution based on an optimization platform; 3) A sufficient condition, used in planning, for solving GMAC problems; and 4) A clear presentation of the benefits in avoiding conflicts when dealing with GMAC. The proposed methods are verified by experiments, simulations, proofs and analysis.

     

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    Highlights

    • A new problem of Group Role Assignment with Constraints (GRA+), called Group Multi-role Assignment (GMRA) with Conflicting roles and agents (GMAC) is proposed and formalized.
    • A practical solution based on an optimization platform, i.e., IBM ILOG CPLEX Optimization Package is provided.
    • A sufficient condition, used in planning, for solving GMAC problems is proved.
    • The benefits of avoiding conflicts when dealing with GMAC are presented.

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