Solving a Continent-Scale Inventory Routing Problem at Renault

TL;DR

This paper presents a scalable large neighborhood search algorithm for a continent-scale inventory routing problem at Renault, introducing novel neighborhoods, perturbations, and a flow relaxation lower bound, supported by an open-source instance library.

Contribution

It develops a scalable LNS approach for large IRPs, generalizes neighborhoods, introduces new perturbations, and provides a new lower bound, addressing a gap in existing literature.

Findings

The proposed LNS outperforms existing methods on large-scale instances.

New perturbations improve solution quality and convergence.

Open-source library facilitates further research on large IRPs.

Abstract

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.