Performance-Balanced Task Allocation in Leader-Member Teams

A Group Multirole Assignment Approach Using E-Cargo

Bibliographic Data

ID22108463
AuthorsJiahui Yu (0000-0002-1215-3851, Nanjing University), Xinlei Zhang (0000-0002-9270-0527, Nanjing University), Lisha Peng (0000-0002-6411-0829, Zhejiang University of Finance and Economics), Haibin Zhu (0000-0001-8169-9703, Nipissing University), Yuxiang Sun (0009-0002-4144-1921, Robotics Research (United States)), Xianzhong Zhou (0000-0003-4321-1441, Nanjing University)
Year2026
Volume13
Issue3
Pages2912-2925
Publication date2026-06-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueIEEE Transactions on Computational Social Systems (JOURNAL)
Journal identifiersISSN: 2329-924X • E-ISSN: 2373-7476
PublisherInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/tcss.2026.3668139
OpenAlexW7138843729
LanguageEN
References cited39

Role-based collaboration (RBC) is a role-centered computational paradigm for solving collaborative problems, where group multirole assignment (GMRA) is an important component. This article focuses on leader–member teams, a common organizational structure in project management, and extends the GMRA framework to address two critical challenges. First, evaluating the qualifications of leaders and members is nontrivial due to their distinct responsibilities. To address this, we propose a capability–requirement matching evaluation (CRME) method that applies differentiated mechanisms to assess leaders and members. Second, existing studies mainly maximize overall performance while neglecting task performance balance, which is vital for synchronized progress. To overcome this limitation, we develop a group multirole assignment with balanced task performance (GMRABP) model that incorporates a penalty-augmented objective to maximize team performance while reducing disparities across tasks. Furthermore, two linearized variants, GMRABP-A and GMRABP-B, are introduced to enhance computational efficiency. Extensive experiments and comparative analyses validate the effectiveness of the proposed methods, offering practical strategies for managing projects where both performance maximization and progress coordination are essential

Assignment problem · Maximization · Task Analysis · Utility maximization · Collaboration in agile enterprises · Mobile Crowdsensing and Crowdsourcing · Team Dynamics and Performance

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