Imperial College London
Alternate

DrArunaSivakumar

Faculty of Engineering, Department of Civil and Environmental Engineering

Reader in Consumer Demand Modelling And Urban Systems
 
 
 
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Contact

 

+44 (0)20 7594 6036a.sivakumar Website

 
 
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Location

 

604Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Luan:2022:10.1016/j.eswa.2022.118494,
author = {Luan, J and Daina, N and Reinau, KH and Sivakumar, A and Polak, JW},
doi = {10.1016/j.eswa.2022.118494},
journal = {Expert Systems with Applications},
pages = {1--17},
title = {A data-based opportunity identification engine for collaborative freight logistics based on a trailer capacity graph},
url = {http://dx.doi.org/10.1016/j.eswa.2022.118494},
volume = {210},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
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.
AU  - Luan,J
AU  - Daina,N
AU  - Reinau,KH
AU  - Sivakumar,A
AU  - Polak,JW
DO  - 10.1016/j.eswa.2022.118494
EP  - 17
PY  - 2022///
SN  - 0957-4174
SP  - 1
TI  - A data-based opportunity identification engine for collaborative freight logistics based on a trailer capacity graph
T2  - Expert Systems with Applications
UR  - http://dx.doi.org/10.1016/j.eswa.2022.118494
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000877393100001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.sciencedirect.com/science/article/pii/S0957417422015780
UR  - http://hdl.handle.net/10044/1/106719
VL  - 210
ER  -
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