Solving Flexible Job-Shop Scheduling Problems Based on Quantum Computing

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書誌詳細
出版年:	Entropy vol. 27, no. 2 (2025), p. 189
第一著者:	Fu, Kaihan
その他の著者:	Liu, Jianjun, Chen, Miao, Zhang, Huiying
出版事項:	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
オンライン･アクセス:	Citation/Abstract Full Text + Graphics Full Text - PDF
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その他の書誌記述
抄録:	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
ソース:	Engineering Database