A data-based opportunity identification engine for collaborative freight logistics based on a trailer capacity graph

Jianlin Luan*, Nicolò Daina, Kristian Hegner Reinau, Aruna Sivakumar, John W. Polak

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Abstract

Logistics operators participating in horizontal collaboration can gain economic benefits and being better placed to meet environmental goals. Data-based approaches provide a viable, albeit suboptimal, solution that can enable real-time collaborative order sharing. Conventional data-based approaches for identifying collaboration (order sharing) opportunities are typically based on origin-destination (OD) matching between trips and shipments from different collaborating companies. This, however, prevents the exploitation of en-route collaboration opportunities. Hence, we propose a practical data-based engine for identifying collaboration opportunities during shipment planning stages that enables shipments to be matched according to both the OD and trailer trip routes. The engine is based on a multigraph approach, called the trailer capacity graph (TCG) approach. We further enhance the engine to improve its computational performance for real-time operations. Numerical experiments based on real-world data from two logistics companies show that the TCG approach identifies a significantly larger number of opportunities, and provides a higher total distance saving than conventional OD-based matching. The experiments also demonstrate that with trailer route approximation and route shape simplification, this engine allows trade-offs between the computational performance and the effectiveness of opportunity identification, which implies that the engine can be flexibly tailored according to user preferences.

OriginalsprogEngelsk
Artikelnummer118494
TidsskriftExpert Systems with Applications
Vol/bind210
ISSN0957-4174
DOI
StatusUdgivet - 30 dec. 2022

Bibliografisk note

Publisher Copyright:
© 2022 Elsevier Ltd

Emneord

  • Collaborative freight logistics
  • Data-based
  • Large-scale
  • Real-time
  • Trailer capacity graph

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