  This paper is the fruit of a partnership with Renault. Their backward
logistic requires solving 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.
