Modeling and Managing an On-Demand Meal Delivery System with Mixed Autonomy
Abstract
This paper investigates the on-demand meal delivery system with mixed autonomy. We have explored how the future implementation of autonomous vehicles (AVs) in the system will affects demand, the labor market of human couriers (HCs), and the service provider in the system. In the system, the service provider determines the fleet size of AVs, the average delivery price for customers, and the average hourly wage for HCs. In response to the operation and pricing strategies, customers decide whether or not to order meals with delivery services, and potential HCs decide whether or not to work for the system. Therefore, a market model is proposed to capture the interactions among the service provider, customers, and HCs. An adaptive particle swarm optimization (APSO) algorithm is adopted to find optimal solutions. The results of numerical experiments show that a lower cost of AVs leads to higher penetration of AVs, lowered delivery price, and improved service quality. As a result, expanded demand is expected. By comparing the market outcomes under a varying number of potential customers, we find that AVs are considered more costefficient in densely populated areas than HCs, and have a higher percentage in the mixed fleet. Hence, customers in those areas are served with improved quality of delivery services.
Anke Ye
PhD Student
Qishen Zhou
PhD Student
