TY - JOUR
T1 - A data-based opportunity identification engine for collaborative freight logistics based on a trailer capacity graph
AU - Luan, Jianlin
AU - Daina, Nicolò
AU - Reinau, Kristian Hegner
AU - Sivakumar, Aruna
AU - Polak, John W.
N1 - Funding Information:
This work was supported by Innovation Fund Denmark [project number 6156‐00001B]. We would also like to thank the two anonymous large logistics companies for providing the data.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/12/30
Y1 - 2022/12/30
N2 - 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.
AB - 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.
KW - Collaborative freight logistics
KW - Data-based
KW - Large-scale
KW - Real-time
KW - Trailer capacity graph
KW - Collaborative freight logistics
KW - Data-based
KW - Large-scale
KW - Real-time
KW - Trailer capacity graph
UR - http://www.scopus.com/inward/record.url?scp=85136465081&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2022.118494
DO - 10.1016/j.eswa.2022.118494
M3 - Journal article
AN - SCOPUS:85136465081
SN - 0957-4174
VL - 210
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 118494
ER -