Large Neighborhood Search and Bitmask Dynamic Programming for Wireless Mobile Charging Electric Vehicle Routing Problems in Medical Transportation
🤖 AI Summary
This paper addresses the timeliness bottleneck in medical emergency transportation caused by the limited driving range of medical transport electric vehicles (MTEVs) and insufficient fixed charging infrastructure. We formulate the Wireless Mobile Charging Electric Vehicle Routing Problem (WMC-EVRP), wherein mobile charging trucks (MCTs) dynamically recharge MTEVs en route, eliminating charging-induced delays. We propose a novel heterogeneous-vehicle (MTEV/MCT) cooperative decision-making model that jointly optimizes charging sequences and integrates path, spatiotemporal, and capacity constraints—first such formulation in the literature. Our solution framework combines a Bitmask-based Dynamic Programming (BDP) algorithm for efficient charging-sequence optimization with a customized Large Neighborhood Search (LNS) for handling complex constraints. Evaluated on a real-world hospital dataset from Singapore, our approach significantly reduces total operational cost while guaranteeing 100% on-time delivery of critical medical supplies, outperforming conventional solvers.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Optimizing electric vehicle routing for medical transportation using wireless charging.
Developing a model to minimize charging downtime and ensure timely deliveries.
Combining Bitmask Dynamic Programming and Large Neighborhood Search for efficient solutions.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Combines wireless mobile charging with optimization
Uses Bit Mask Dynamic Programming for charging strategies
Applies Large Neighborhood Search for route optimization
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