EURO 2025 Leeds
Abstract Submission

568. Large Neighborhood and Hybrid Genetic Search for Inventory Routing Problems

Invited abstract in session WA-17: Combinatorial optimization in distribution and delivery operations, stream Combinatorial Optimization.

Wednesday, 8:30-10:00
Room: Esther Simpson 2.08

Authors (first author is the speaker)

1. Claudia Archetti
Università degli Studi di Brescia
2. Jingyi Zhao
Shenzhen Research Institute of Big Data,
3. Tuan Anh Pham
Faculty of DS&AI, College of Technology, National Economics University
4. Thibaut Vidal
Mathematics and Industrial Engineering, Polytechnique Montréal

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.

Keywords

Status: accepted

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