Crowdsourced delivery platforms have made use of two types of couriers: ad-hoc couriers, who are more flexible, and committed couriers, who are more reliable. In this paper we show that by designing a system that intelligently utilizes order partitioning between the two delivery channels (e.g., makes routing and partitioning decisions jointly), the delivery platform can exploit the benefits of each courier base to improve customer service and reduce the total cost when compared to order pooling.

On two-sided matching platforms such as VolunteerMatch (VM), a sizable fraction of website traffic arrives via an external link, bypassing the platform's recommendation algorithm. We study how platforms can account for this, given the goal of maximizing successful matches. We model the problem as a variant of online matching and introduce an algorithm providing near-optimal guarantees in certain parameter regimes. We also show our algorithm’s strong performance in a case study based on VM data.

We propose a dynamic car relocation policy for a car-sharing network with centralized control and uncertain, unbalanced demand. The policy is derived from a reformulation of the fluid model approximation of the dynamic problem. We project the full-dimensional fluid approximation onto the lower-dimensional space of relocations only. Our policy exploits these gradients to make dynamic car relocation decisions. We close the optimality gap on average by 30% in static and time-varying settings.