Collaborative electric vehicle routing with meet points

Zhou, Fangting; Arvidsson, Ala; Wu, Jiaming; Kulcsár, Balázs · 2024 · DOAJ

DOI: 10.1016/j.commtr.2024.100135

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This paper addresses the challenge of optimizing urban logistics through horizontal collaboration among competing delivery companies, specifically focusing on electric vehicles (EVs). Motivated by rising freight demand, traffic congestion, and the need for sustainable transportation, the authors propose a mechanism to incentivize collaboration by sharing profits and exchanging goods en route. Existing literature often overlooks individual company benefits or assumes shared depots, which are restrictive in practice. To bridge this gap, the study introduces the Collaborative Electric Vehicle Routing Problem with Meet Points (CoEVRPMP). This model allows two logistics companies to exchange parcels at designated "meet points" rather than relying on depot-based transfers, thereby reducing total costs and environmental impact while ensuring each company achieves a profit threshold higher than non-collaborative operations. The methodology formulates the CoEVRPMP as a mixed-integer nonlinear programming (MINLP) model. The problem considers practical constraints including customer time windows, vehicle capacity, opportunity charging at customer locations and meet points, and synchronization of vehicle arrivals at meet points. A key feature is the integration of a profit-sharing mechanism that splits revenue from shared customers based on service contributions, ensuring individual profitability. To solve this complex optimization problem, the authors developed two solution approaches: an exact method using branching for theoretical analysis and a matheuristic combining adaptive large neighborhood search with linear programming for practical implementation. The model assumes deterministic travel times and distances, with EVs capable of partial charging. The study validates the proposed methods through numerical case studies, including a real-world scenario and large-scale experiments involving up to 500 customers. The results demonstrate that the collaborative approach is viable and scalable. Specifically, the findings indicate that horizontal collaboration significantly reduces total operational costs and travel distances compared to non-collaborative routing. By exchanging goods at meet points, companies can serve customers that would otherwise be cost-prohibitive to reach individually. The experiments confirm that the proposed mechanism successfully balances system-wide efficiency with individual company gains, meeting the predefined profit thresholds for each participant. The significance of this work lies in its practical contribution to sustainable urban logistics. By integrating route optimization with a fair profit-sharing scheme, the paper provides a framework that encourages competitors to collaborate without sacrificing individual interests. The reduction in travel distance directly lowers the environmental footprint of delivery fleets, supporting sustainability goals. Furthermore, the introduction of meet points as dynamic exchange locations offers a more flexible alternative to traditional transshipment models. The study concludes that such collaborative strategies are essential for managing the growing complexity of urban freight distribution while transitioning to electric vehicle fleets.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• last mile delivery