Service and capacity planning in crowd-sourced delivery

Abstract

The success of on-demand service platforms, e.g., Uber and Lyft to obtain a ride and Grubhub and Eat24 to get a meal, which rely on crowd-sourced transportation capacity, has radically changed the view on the potential and benefits of crowd-sourced transportation and delivery. Many retail stores, for example, are examining the pros and cons of introducing crowd-sourced delivery in their omni-channel strategies. However, few models exist to support the analysis of service area, service quality, and delivery capacity planning, and their interaction, in such environments. We introduce a model that seeks to do exactly that and can answer many fundamental questions arising in these settings. Using on-demand meal delivery platforms as an example, we investigate, among others, the relation between service area and profit and delivery offer acceptance probability and profit, and the benefits of integrating delivery service of multiple restaurants, and generate many valuable insights.