Online Allocation with Heterogeneous Supply and Minimum Workload Guarantees

Keeping Gig Workers Working How should platforms allocate jobs when workers need a minimum amount of work to stay engaged? In their accepted paper, “Online Allocation with Heterogeneous Supply and Minimum Workload Guarantees,” Garud Iyengar and Raghav Singal study this question in online labor marketplaces, where workers differ in quality, capacity, and desired workload. The authors show that intuitive allocation rules can perform poorly. A first-come-first-served policy can favor lower-quality workers, whereas a quality-based greedy policy can cause high-capacity workers to leave when their workload requirements are not met. They develop Tracker, an online policy that combines admission control with a simple target-tracking rule: Each worker is assigned a target workload, and arriving jobs are directed toward workers who are most under their targets. Despite the stochastic nature of demand, the authors establish that Tracker is optimal. Computational experiments calibrated to a real labor marketplace show substantial improvements in allocation quality relative to standard policies, while maintaining workers’ minimum workload guarantees.