A Real-Time Dispatching Strategy for Shared Automated Electric Vehicles with Performance Guarantees

TL;DR

This paper introduces a real-time dispatching strategy for shared automated electric vehicles that guarantees performance and stability without prior demand knowledge, outperforming existing algorithms in simulations.

Contribution

The paper presents an online MDPP approach for SAEV dispatching that ensures stability, control over costs, and real-time computational efficiency, addressing challenges posed by electrification.

Findings

MDPP reduces customer waiting times compared to other algorithms.

MDPP controls dispatching costs effectively.

The approach is validated under real-world demand scenarios.

Abstract

Real-time vehicle dispatching operations in traditional car-sharing systems is an already computationally challenging scheduling problem. Electrification only exacerbates the computational difficulties as charge level constraints come into play. To overcome this complexity, we employ an online minimum drift plus penalty (MDPP) approach for SAEV systems that (i) does not require a priori knowledge of customer arrival rates to the different parts of the system (i.e. it is practical from a real-world deployment perspective), (ii) ensures the stability of customer waiting times, (iii) ensures that the deviation of dispatch costs from a desirable dispatch cost can be controlled, and (iv) has a computational time-complexity that allows for real-time implementation. Using an agent-based simulator developed for SAEV systems, we test the MDPP approach under two scenarios with real-world calibrated demand and charger distributions: 1) a low-demand scenario with long trips, and 2) a high-demand scenario with short trips. The comparisons with other algorithms under both scenarios show that the proposed online MDPP outperforms all other algorithms in terms of both reduced customer waiting times and vehicle dispatching costs.