Hybrid quantum-classical computation for automatic guided vehicles scheduling

TL;DR

This paper explores the application of hybrid quantum-classical solvers, specifically D-Wave's, to optimize AGV scheduling in industrial settings, demonstrating practical efficiency for problems involving up to 21 AGVs.

Contribution

It demonstrates the practical use of hybrid quantum-classical solvers for AGV scheduling, highlighting their efficiency and potential advantages over classical methods in real-world scenarios.

Findings

Hybrid solvers perform comparably to classical solvers.

Effective scheduling of up to 21 AGVs within seconds.

AGV scheduling benefits more from quantum approaches than railway scheduling.

Abstract

Motivated by recent efforts to develop quantum computing for practical, industrial-scale challenges, we demonstrate the effectiveness of state-of-the-art hybrid (not necessarily quantum) solvers in addressing the business-centric optimization problem of scheduling Automatic Guided Vehicles (AGVs). Some solvers can already leverage noisy intermediate-scale quantum (NISQ) devices. In our study, we utilize D-Wave hybrid solvers that implement classical heuristics with potential assistance from a quantum processing unit. This hybrid methodology performs comparably to existing classical solvers. However, due to the proprietary nature of the software, the precise contribution of quantum computation remains unclear. Our analysis focuses on a practical, business-oriented scenario: scheduling AGVs within a factory constrained by limited space, simulating a realistic production setting. Our approach maps a realistic AGVs problem onto one reminiscent of railway scheduling and demonstrates that the AGVs problem is better suited to quantum computing than its railway counterpart, the latter being denser in terms of the average number of constraints per variable. The main idea here is to highlight the potential usefulness of a hybrid approach for handling AGVs scheduling problems of practical sizes. We show that a scenario involving up to 21 AGVs, significant due to possible deadlocks, can be efficiently addressed by a hybrid solver in seconds.