Notification Timing for On-Demand Personnel Scheduling

Abstract

Modern business models have enabled service systems to leverage a large pool of casual employees with flexible hours, paid based on piece rates, to fulfill on-demand work. These systems have been successfully implemented in sectors such as ride-sharing, delivery services, and microtasks. However, because casual employees engage infrequently and may lack experience, maintaining service quality remains a key challenge. We introduce a novel scheduling system designed to provide experienced casual employees to service companies, optimizing their operations through a dynamic, data-driven approach. Similar to traditional on-call systems, it contacts casual personnel in order of seniority to inform them about available work. However, our system offers greater flexibility, allowing employees to take time to decide and freely select from available shifts. Senior employees can also replace (bump) junior employees from the schedule if no other preferred shift is available, subject to certain conditions. While permitted, these replacements create disruptions and dissatisfaction among employees. The management aims to efficiently assign all shifts while minimizing bumps. However, uncertainty arises regarding when an employee will select a shift. The key challenge is determining the optimal timing to notify employees to reduce disruptions. We first establish that this problem is $\mathcal{NP}$-complete even with perfect information. To address this, we propose a two-stage stochastic formulation for the dynamic problem and develop a heuristic algorithm that approximates the optimal policy using a threshold-based structure. These policies are fine-tuned using offline solutions with pre-known uncertainty, allowing for optimization. Testing on real-world data demonstrates that our approach outperforms the current strategy used by our industry partner.