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Solving Flexible Job-Shop Scheduling Problems Based on Quantum Computing

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Published in:Entropy vol. 27, no. 2 (2025), p. 189
Main Author: Fu, Kaihan
Other Authors: Liu, Jianjun, Chen, Miao, Zhang, Huiying
Published:
MDPI AG
Subjects:
Scheduling
Quantum computing
Integer programming
Computers
Mathematical models
Combinatorial analysis
Optimization
Flexibility
Variables
Manufacturers
Ising model
Linear programming
Manufacturing
Feedback
Energy consumption
Job shop scheduling
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Abstract:Flexible job-shop scheduling problems (FJSPs) represent one of the most complex combinatorial optimization challenges. Modern production systems and control processes demand rapid decision-making in scheduling. To address this challenge, we propose a quantum computing approach for solving FJSPs. We propose a quadratic unconstrained binary optimization (QUBO) model to minimize the makespan of FJSPs, with the scheduling scheme encoded in the ground state of the Hamiltonian operator. The model is solved using a coherent Ising machine (CIM). Numerical experiments are conducted to evaluate and validate the performance and effectiveness of the CIM. The results demonstrate that quantum computing holds significant potential for solving FJSPs more efficiently than traditional computational methods.
ISSN:1099-4300
DOI:10.3390/e27020189
Source:Engineering Database