@techreport{Bouvier2022,
abstract = {This paper is the fruit of a partnership with Renault. Their backward logistic requires to solve a continent-scale multi-attribute inventory routing problem (IRP). With an average of {\$}30{\$} commodities, {\$}16{\$} depots, and {\$}600{\$} customers spread across a continent, our instances are orders of magnitude larger than those in the literature. Existing algorithms do not scale. We propose a large neighborhood search (LNS). To make it work, (1) we generalize existing split delivery vehicle routing problem and IRP neighborhoods to this context, (2) we turn a state-of-the art matheuristic for medium-scale IRP into a large neighborhood, and (3) we introduce two novel perturbations: the reinsertion of a customer and that of a commodity into the IRP solution. We also derive a new lower bound based on a flow relaxation. In order to stimulate the research on large-scale IRP, we introduce a library of industrial instances. We benchmark our algorithms on these instances and make our code open-source. Extensive numerical experiments highlight the relevance of each component of our LNS.},
archivePrefix = {arXiv},
arxivId = {2209.00412},
author = {Bouvier, L. and Dalle, G. and Parmentier, A. and Vidal, T.},
eprint = {2209.00412},
file = {:C$\backslash$:/Users/Thibaut/Documents/Mendeley-Articles/Bouvier et al/Bouvier et al. - 2022 - Solving a continent-scale inventory routing problem at Renault.pdf:pdf},
institution = {arXiv:2209.00412},
title = {{Solving a continent-scale inventory routing problem at Renault}},
url = {https://arxiv.org/pdf/2209.00412.pdf},
year = {2022}
}

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