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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.11308 |
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Table of Contents:
- This paper proposes the framework of an efficient gig-work management system. A gig-work management system recommends one-off tasks with information about task hours and wages to gig-workers. To enable effective management, this paper develops a model of gig-workers' decision-making. Then, based on the model, we formulate an optimization problem to determine the optimal task hours and wages. The formulated problem belongs to the class of chance-constrained model predictive control (CC-MPC) problems. To efficiently solve the CC-MPC problem, we develop an approximate solution algorithm with guaranteed confidence levels. Finally, we develop gig-worker models based on data collected through crowdsourcing.