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Optimizing Performance While Considering Equity and Preference in Time-Constrained Group Multirole Assignment
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AI
DOI：10.1109/TSMC.2025.3648031.png)
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In collaborative systems such as factory operations and physician rostering, it is crucial to assign agents to multiple roles over time while balancing performance, equity, and individual preferences. Traditional group multirole assignment (GMRA) models prioritize performance optimization but often neglect workload fairness and individual preferences, leading to agent demotivation and suboptimal team outcomes. To address this gap, we propose a time-constrained GMRA (TGMRA) framework that extends the classical GMRA into a 3-D (roles, agents, time) model, explicitly integrating temporal constraints and heterogeneous agent requirements. Based on this framework, we further develop two extended models: TGMRA_E, which ensures the equitable distribution of work periods and task quantities, and TGMRA_EP, which integrates individual preferences via a weighted multiobjective optimization strategy. Extensive simulations across various group sizes confirm the effectiveness and robustness of these models. Compared with the baseline GMRA, TGMRA_E significantly reduces workload disparities, while TGMRA_EP improves preference satisfaction by up to 123% with less than 4% performance loss. Our results provide scalable scheduling strategies that balance team performance and individual needs and offer practical guidance on parameter selection for diverse real-world scenarios.
Keyword:
Environments—classes
agents
roles
groups
and objects (E-CARGO)
equity
preference
time-constrained group multirole assignment (TGMRA)
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论文数:
240
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