Solving Flexible Job-Shop Scheduling Problems Based on Quantum Computing

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書目詳細資料
發表在:	Entropy vol. 27, no. 2 (2025), p. 189
主要作者:	Fu, Kaihan
其他作者:	Liu, Jianjun, Chen, Miao, Zhang, Huiying
出版:	MDPI AG
主題:	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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實物特徵
Resumen:	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
Fuente:	Engineering Database