Sequential Hiring of Contingent Workers Through Learning-Based Optimization
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 decisions take effect only after a random delay. We formulate this problem as a stochastic multi-play bandit with costly switching and delayed act...
| Authors | Chris Lee, Xiuli Chao, Izak Duenyas |
|---|---|
| Primary category | math.OC |
| Published | 16 June 2026 |
| Last updated at source | 29 June 2026 |
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