ArticleScientific reports2024
Quantum computing for several AGV scheduling models.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Quantum Computing for Optimal Dispatch of Virtual Power Plants Under Wind and Solar Uncertainty.Entropy (Basel, Switzerland) · 2026Article
- Quantum Computing for Transport Network Optimization.Entropy (Basel, Switzerland) · 2025Article
- Real time task planning for order picking in intelligent logistics warehousing.Scientific reports · 2025Article
- Quantum Computing in Community Detection for Anti-Fraud Applications.Entropy (Basel, Switzerland) · 2024Article
- Hybrid quantum-classical computation for automatic guided vehicles scheduling.Scientific reports · 2024Article
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Authors and funding
5 authors.
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Abstract
Due to the high degree of automation, automated guided vehicles (AGVs) have been widely used in many scenarios for transportation, and traditional computing power is stretched in large-scale AGV scheduling. In recent years, quantum computing has shown incomparable performance advantages in solving specific problems, especially Combinatorial optimization problem. In this paper, quantum computing technology is introduced into the study of the AGV scheduling problem. Additionally two types of quadratic unconstrained binary optimisation (QUBO) models suitable for different scheduling objectives are constructed, and the scheduling scheme is coded into the ground state of Hamiltonian operator, and the problem is solved by using optical coherent Ising machine (CIM). The experimental results show that compared with the traditional calculation method, the optical quantum computer can save 92% computation time on average. It has great application potential.
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