Improvement of freight consolidation through a data mining-based methodology Online publication date: Tue, 01-Oct-2024
by Zineb Aboutalib; Bruno Agard
International Journal of Logistics Systems and Management (IJLSM), Vol. 49, No. 2, 2024
Abstract: Freight consolidation is a complex logistics practice supported by a broad spectrum of strategies and methods to improve supply chain cost-effectiveness. It consists of grouping products in a single batch to reduce distribution costs. Literature review revealed that operational research (OR) is typically used for freight consolidation, and their inputs are often aggregated over time. While necessary to accommodate computationally expensive OR algorithms, such data simplifications are responsible for losing valuable data patterns. Our contribution is a novel data mining methodology that uses association rules to leverage data patterns in the context of intermittent demand. Our approach is compared to a typical operational research approach from a literature case study. Simple to implement, our methodology gives good results and can flexibly accommodate and exploit data patterns while being able to scale to a much larger amount of data, making it a more suitable approach for the big data world.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Logistics Systems and Management (IJLSM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook