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

Hui-Ju Hung, Guang-Siang Lee, Chia-Hsun Lu, Chih-Ya Shen, De-Nian Yang

TL;DR

It is proved that RSHWC is NP-hard and the Guided Risk-aware Iterative Assembling (GRIA) algorithm is proposed, a multi-stage algorithm that combines risk-aware workforce construction, skill-preserving workforce refinement, and risk-reducing member replacement.

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.

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

TL;DR

It is proved that RSHWC is NP-hard and the Guided Risk-aware Iterative Assembling (GRIA) algorithm is proposed, a multi-stage algorithm that combines risk-aware workforce construction, skill-preserving workforce refinement, and risk-reducing member replacement.

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.
Paper Structure (7 sections, 4 theorems, 9 equations, 4 figures, 1 table, 1 algorithm)

This paper contains 7 sections, 4 theorems, 9 equations, 4 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. The RSHWC Problem
  4. The Proposed GRIA Algorithm
  5. Correctness Proof of Skill-preserving Workforce Refinement
  6. Experiments
  7. Conclusion

Key Result

theorem 1

The RSHWC problem is NP-hard.

Figures (4)

  • Figure 1: Illustrative example of a two-layer social network
  • Figure 2: The average collaboration (ave. collab.) while varying $|R|$
  • Figure 3: The computation time while varying $|R|$
  • Figure 4: The average collaboration while varying the remote/onsite effectiveness ratio

Theorems & Definitions (9)

  • definition 1: The RSHWC Problem
  • theorem 1
  • proof
  • lemma 1
  • proof
  • lemma 2
  • proof
  • theorem 2: Skill-preserving workforce refinement
  • proof