Large Neighborhood and Hybrid Genetic Search for Inventory Routing Problems

2025-05-28

AI Summary

The Inventory Routing Problem (IRP) jointly optimizes inventory and delivery decisions from a supplier to multiple retailers across multiple periods, exhibiting strong coupling and high combinatorial complexity. To address this, we propose a customized Large Neighborhood Search (LNS) framework for IRP, introducing the novel “single-retailer full-visit removal and reinsertion” operator—capable of simultaneously optimizing routing and inventory costs. Our approach integrates an efficient dynamic programming procedure (featuring preprocessing and pruning strategies) with a Hybrid Genetic Search (HGS) mechanism. Evaluated on large-scale standard benchmark instances, the method achieves state-of-the-art solution quality, significantly outperforming existing heuristics. It establishes the first scalable paradigm for IRP that balances both computational efficiency and solution accuracy.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Optimizing inventory and routing decisions jointly in IRP
• Developing advanced large neighborhood search techniques for IRP
• Integrating dynamic programming with Hybrid Genetic Search (HGS)

Innovation

• Large neighborhood search operator for IRP
• Dynamic programming with preprocessing strategies
• Hybrid Genetic Search integration