OCD: Online Crowdsourced Delivery for On-Demand Food

Online-to-offline (O2O) commerce connecting service providers and individuals to address daily human needs is quickly expanding. In particular, on-demand food, whereby food orders are placed online by customers and delivered by couriers, is becoming popular. This novel urban food application requires highly efficient and scalable real-time delivery services. However, it is difficult to recruit enough couriers and route them to facilitate such food ordering systems. This paper presents an online crowdsourced delivery (OCD) approach for on-demand food. Facilitated by Internet-of-Things and 3G/4G/5G technologies, public riders can be attracted to act as crowdsourced workers delivering food by means of shared bicycles or electric motorbikes. An online dynamic optimization framework comprising order collection, solution generation, and sequential delivery processes is presented. A hybrid metaheuristic solution process integrating the adaptive large neighborhood search and tabu search approaches is developed to assign food delivery tasks and generate high-quality delivery routes in a real-time manner. The crowdsourced riders are dynamically shared among different food providers. Simulated small-scale and real-world large-scale on-demand food delivery instances are used to evaluate the performance of the proposed approach. The results indicate that the presented crowdsourced food delivery approach outperforms traditional urban logistics. The developed hybrid optimization mechanism is able to produce high-quality crowdsourced delivery routes in less than 120 s. The results demonstrate that the presented OCD approach can facilitate city-scale on-demand food delivery.