# Two-echelon Electric Vehicle Routing Problem in Parcel Delivery: A Literature Review

arXiv: https://arxiv.org/abs/2412.19395 · HTML: https://mlsanity.com/paper/2412.19395/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [A Survey on Algorithmic Developments in Optimal Transport Problem with Applications](https://mlsanity.com/paper/2501.06247/) (2025)

## Abstract (arXiv)

Multi-echelon parcel delivery systems using electric vehicles (EVs) are crucial for managing urban logistics complexity and promoting sustainability. In multi-echelon systems, particularly within two-stage systems, larger vehicles transport parcels from a central depot to satellite hubs, where smaller EVs pick up the parcels and carry out last-mile deliveries. This system could increase efficiency, reduce emissions, and improve service reliability. The two-echelon electric vehicle routing problem (2E-EVRP), an extension of the traditional two-echelon vehicle routing problem (2E-VRP), addresses EV-specific challenges such as battery constraints and recharging stations to tackle environmental impacts, urban congestion, and e-commerce demands. While effectively reducing costs, energy use, and emissions, the 2E-EVRP faces modeling challenges due to multi-echelon structures, EV limitations, and recharging station selection. This paper systematically reviews 2E-EVRP literature, analyzing key studies. It proposes a classification scheme to categorize the papers based on the problem variants, objectives, constraints, and solution methods. It identifies gaps such as delivery tardiness, environmental trade-offs, multi-objective optimization, multiple depots, split deliveries, and time-dependent travel conditions. Future research directions include aligning models with urban policies, integrating parcel lockers, enabling same-day delivery, and incorporating advanced technologies like autonomous vehicles. Methodological advancements suggest using machine learning, reinforcement learning, and simulation-based approaches to enhance dynamic routing and real-time decision-making. These directions aim to expand the 2E-EVRP applicability, addressing theoretical and practical challenges in sustainable urban logistics for future works.