Title: Improvement of freight consolidation through a data mining-based methodology

Authors: Zineb Aboutalib; Bruno Agard

Addresses: Laboratoire en Intelligence des Données, Département de Mathématiques et Génie Industriel, École Polytechnique de Montréal, Montréal (QC), H3T 1J4, Canada; CIRRELT, Université de Montréal, Pavillon André Aisenstadt, Bureau 3520 – 2920, Chemin de la Tour, Montréal (QC) H3T 1J4, Canada ' Laboratoire en Intelligence des Données, Département de Mathématiques et Génie Industriel, École Polytechnique de Montréal, Montréal (QC), H3T 1J4, Canada; CIRRELT, Université de Montréal, Pavillon André Aisenstadt, Bureau 3520 – 2920, Chemin de la Tour, Montréal (QC) H3T 1J4, Canada

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.

Keywords: transportation; association rules; cost reduction; data mining; freight consolidation; intermittent data; decision making; logistics; big data; pattern extraction.

DOI: 10.1504/IJLSM.2024.141701

International Journal of Logistics Systems and Management, 2024 Vol.49 No.2, pp.255 - 273

Received: 07 Feb 2022
Accepted: 05 Mar 2022
Published online: 01 Oct 2024 *

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