Task assignment, pricing, and capacity planning for a hybrid fleet of centralized and decentralized couriers

Crowdsourced delivery platforms operate as an intermediary between consumers who require delivery tasks and couriers who make these deliveries; both of which are uncertain. The main challenge of a crowdsourced delivery platform is to meet a service level for their customers (e.g., 95% on-time delivery) by serving dynamically arriving delivery tasks with time windows. The two critical courier management decisions for a platform are how to schedule couriers and how to assign delivery tasks to couriers. These two decisions can be centralized (i.e., decided by the platform) or decentralized (i.e., decided by the couriers). Centralizing these decisions produces a more reliable workforce while decentralizing them may come with cost savings to the platform and allows more freedom to couriers in deciding when and where to work. Crowdsourced delivery platforms have begun to utilize both courier types simultaneously (i.e., a hybrid system) with the hope of reaping the advantages of each. In this paper, we address the challenge of capacity planning for a crowdsourced delivery platform that utilizes both centralized (committed) and decentralized (ad-hoc) couriers. We present fluid models for delivery systems using either type of courier, and a hybrid formulation. Our theoretical, numerical, and simulation results establish the superiority of a hybrid system over each pure system in a majority of real-world scenarios.