Risk-Aware Skill-Coverage Hybrid Workforce Configuration on Social Networks
🤖 AI Summary
This study addresses the trade-off among skill coverage, collaboration efficiency, and health-related contact risk in hybrid work environments by formulating them jointly as the NP-hard Risk-aware Hybrid Workforce Configuration (RSHWC) problem—the first such integrated model. To tackle RSHWC, the authors propose GRIA, a multi-stage optimization framework that sequentially performs risk-aware team formation, skill-preserving refinement, and risk-mitigating member replacement. Extensive experiments on four real-world social network datasets demonstrate that GRIA consistently outperforms existing baselines across diverse settings, effectively balancing skill coverage, collaboration quality, and contact risk control.
Application Category
📝 Abstract
In hybrid workforce configurations, it is important to decide which employees should work onsite or remotely while ensuring the collaboration benefits against contact-based health risks and skill requirements. In this paper, we formulate the Risk-aware Skill-coverage Hybrid Workforce Configuration (RSHWC) problem on a two-layer social network that balances physical contact risks and social collaboration ties to meet skill requirements. We prove that RSHWC is NP-hard and propose the Guided Risk-aware Iterative Assembling (GRIA) algorithm, a multi-stage algorithm that combines risk-aware workforce construction, skill-preserving workforce refinement, and risk-reducing member replacement. Experiments on four real-world networks show that GRIA consistently outperforms state-of-the-art baselines under various settings.
Problem
Research questions and friction points this paper is trying to address.
hybrid workforce
risk-aware
skill coverage
social networks
workforce configuration
Innovation
Methods, ideas, or system contributions that make the work stand out.
hybrid workforce configuration
risk-aware optimization
skill coverage
social network
NP-hard problem
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