Risk-Aware Skill-Coverage Hybrid Workforce Configuration on Social Networks

TL;DR

This paper introduces a new problem for optimizing hybrid workforce arrangements on social networks, balancing health risks and skill coverage, and proposes an effective multi-stage algorithm with demonstrated superior performance.

Contribution

It formulates the NP-hard RSHWC problem on social networks and develops the GRIA algorithm to effectively balance risks and skills in workforce configuration.

Findings

GRIA outperforms existing methods in experiments

The RSHWC problem is NP-hard

Effective risk and skill balancing achieved

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.