Large Neighborhood Search and Bitmask Dynamic Programming for Wireless Mobile Charging Electric Vehicle Routing Problems in Medical Transportation
Jingyi Zhao, Haoxiang Yang, Yang Liu
TL;DR
The paper tackles time-sensitive medical transportation under electric vehicle constraints by proposing the Wireless Mobile Charging EV Routing Problem (WMC-EVRP), where MTEVs can be charged en route by mobile charging trucks via wireless links, coupling two vehicle types through synchronization. A novel LNS-LS-BDP framework combines Large Neighborhood Search with a Bitmask Dynamic Programming subroutine to optimally decide charging edges on fixed routes and then iteratively improve routes, achieving high-quality solutions on medium-to-large instances. The authors provide a MILP formulation, demonstrate that LNS-BDP matches or outperforms the state-of-the-art solver on small instances and outperforms it on larger ones, and validate the approach with real-world Singapore hospital data, showing practical applicability and energy-efficiency gains. This work offers a scalable, practical framework for deploying on-road wireless charging in healthcare logistics and lays groundwork for extensions to time windows, stochastic demand, and real-time routing adaptations.
Abstract
The transition to electric vehicles (EVs) is critical to achieving sustainable transportation, but challenges such as limited driving range and insufficient charging infrastructure have hindered the widespread adoption of EVs, especially in time-sensitive logistics such as medical transportation. This paper presents a new model to break through this barrier by combining wireless mobile charging technology with optimization. We propose the Wireless Mobile Charging Electric Vehicle Routing Problem (WMC-EVRP), which enables Medical Transportation Electric Vehicles (MTEVs) to be charged while traveling via Mobile Charging Carts (MCTs). This eliminates the time wastage of stopping for charging and ensures uninterrupted operation of MTEVs for such time-sensitive transportation problems. However, in this problem, the decisions of these two types of heterogeneous vehicles are coupled with each other, which greatly increases the difficulty of vehicle routing optimizations. To address this complex problem, we develop a mathematical model and a tailored meta-heuristic algorithm that combines Bit Mask Dynamic Programming (BDP) and Large Neighborhood Search (LNS). The BDP approach efficiently optimizes charging strategies, while the LNS framework utilizes custom operators to optimize the MTEV routes under capacity and synchronization constraints. Our approach outperforms traditional solvers in providing solutions for medium and large instances. Using actual hospital locations in Singapore as data, we validated the practical applicability of the model through extensive experiments and provided important insights into minimizing costs and ensuring the timely delivery of healthcare services.