arXiv:2606.18438v1 Announce Type: cross Abstract: In this paper, we study a sequential workforce management problem in a contingent labor setting with uncertainty in both worker production and labor supply. A firm seeks to maximize cumulative profit by maintaining an active team of fixed size while learning worker productivity over time. We emphasize two critical operational frictions in this problem: replacing workers is costly, and workers may not be available immediately for hiring because of, for example, prior job commitments, scheduling constraints, or onboarding procedures. Thus, hiring
The increasing complexity and uncertainty in labor markets, especially with contingent workers, necessitates more sophisticated algorithmic approaches for workforce management to optimize cost and productivity.
This research provides a framework for firms to strategically manage flexible workforces, improving efficiency and profitability in dynamic economic conditions and offering a competitive advantage in talent acquisition.
Firms can shift from reactive to proactive and data-driven hiring strategies for contingent workers, moving away from intuition-based decisions towards optimized learning-based models.
- · Businesses with high contingent worker reliance
- · AI/ML solution providers for HR
- · Gig economy platforms
- · Traditional HR recruitment agencies
- · Firms slow to adopt algorithmic hiring
- · Less efficient contingent workforce models
Companies begin adopting learning-based optimization for contingent hiring, leading to more efficient staffing and reduced operational costs.
Increased demand for specialized AI models that can integrate various labor market data points, creating a new niche in HR technology.
The role of human HR managers evolves towards overseeing AI systems and focusing on strategic human capital development, rather than routine hiring decisions.
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