Quantum annealing-based route optimization for commercial AGV operating systems in large-scale logistics warehouses

Abstract

This study proposes a new approach to optimizing the routing of Automated Guided Vehicles (AGVs) in large-scale logistics warehouses using Quantum Annealing. As logistics operations grow, efficient AGV routing becomes critical for ensuring safety, reliability, and throughput, particularly in high-density environments. To address the complexity of real-world systems, we introduce an enhanced cost function that incorporates a real-time priority factor to improve both routing performance and operation safety. In this paper, we present a secure and computationally efficient candidate route generation method, along with an Optimization Problem Clustering technique that decomposes the large problem into smaller, tractable subproblems to reduce computational complexity. The proposed methods are designed to incorporate real-time operational data to ensure collision avoidance, maintain safe operations, and enhance routing efficiency in realistic large-scale warehouse environments. We validate the proposed methods using a state-of-the-art Quantum Annealing machine, benchmarking against both classical and quantum-inspired solvers. Integration with a commercial AGV Operating System (AOS) is demonstrated, enabling seamless connectivity with classical solvers via a local network and with Quantum Annealing and Quantum-inspired solvers via cloud infrastructure. Simulations involving 1000 AGVs show that the proposed route generation method reduces the number of optimization variables by an average of 96% for small-scale to medium-scale systems and 78% for large-scale systems, while also improving route quality. The clustering method reduces the maximum problem size to under 10,000 variables, enabling scalable and safe control of large AGV fleets. Furthermore, a complexity-based problem formulation reduces sampling time by approximately 28.2% compared to conventional size-based approaches for problems ranging from 1000 to 10,000 variables. These results demonstrate the practical viability of Quantum Annealing for managing large-scale AGV Operating Systems and underscore the potential of the proposed methods for advancing logistics optimization in real-world warehouse environments.