Large Neighborhood and Hybrid Genetic Search for Inventory Routing Problems

TL;DR

This paper introduces a novel large neighborhood search operator integrated into a hybrid genetic algorithm to effectively solve large-scale inventory routing problems, significantly improving solution quality.

Contribution

It develops a new large neighborhood search operator with a dynamic programming algorithm tailored for IRP, enhancing existing heuristic methods.

Findings

Achieves state-of-the-art solutions on benchmark instances

Effectively handles large-scale IRP instances

Improves solution quality over previous methods

Abstract

The inventory routing problem (IRP) focuses on jointly optimizing inventory and distribution operations from a supplier to retailers over multiple days. Compared to other problems from the vehicle routing family, the interrelations between inventory and routing decisions render IRP optimization more challenging and call for advanced solution techniques. A few studies have focused on developing large neighborhood search approaches for this class of problems, but this remains a research area with vast possibilities due to the challenges related to the integration of inventory and routing decisions. In this study, we advance this research area by developing a new large neighborhood search operator tailored for the IRP. Specifically, the operator optimally removes and reinserts all visits to a specific retailer while minimizing routing and inventory costs. We propose an efficient tailored dynamic programming algorithm that exploits preprocessing and acceleration strategies. The operator is used to build an effective local search routine, and included in a state-of-the-art routing algorithm, i.e., Hybrid Genetic Search (HGS). Through extensive computational experiments, we demonstrate that the resulting heuristic algorithm leads to solutions of unmatched quality up to this date, especially on large-scale benchmark instances.