Dual Resource Constrained Flexible Job Shop Scheduling Based on Improved Quantum Genetic Algorithm

Zhang, Shoujing; Du, Haotian; Borucki, Sebastian; Jin, Shoufeng; Hou, Tiantian; Li, Zhixiong

doi:10.3390/machines9060108

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Dual Resource Constrained Flexible Job Shop Scheduling Based on Improved Quantum Genetic Algorithm

by

Shoujing Zhang

¹,

Haotian Du

¹,

Sebastian Borucki

²

,

Shoufeng Jin

¹,

Tiantian Hou

¹ and

Zhixiong Li

^3,*

¹

Xi’an Key Laboratory of Modern Intelligent Textile Equipment, Department of Industrial Engineering, Xi’an Polytechnic University, Xi’an 710600, China

²

Institute of Electrical Power Engineering and Renewable Energy, Opole University of Technology, 45-758 Opole, Poland

³

Yonsei Frontier Lab, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea

^*

Author to whom correspondence should be addressed.

Machines 2021, 9(6), 108; https://doi.org/10.3390/machines9060108

Submission received: 13 April 2021 / Revised: 10 May 2021 / Accepted: 21 May 2021 / Published: 24 May 2021

(This article belongs to the Section Machines Testing and Maintenance)

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Abstract

Aiming at solving the problem of dual resource constrained flexible job shop scheduling problem (DRCFJSP) with differences in operating time between operators, an artificial intelligence (AI)-based DRCFJSP optimization model is developed in this paper. This model introduces the differences between the loading and unloading operation time of workers before and after the process. Subsequently, the quantum genetic algorithm (QGA) is used as the carrier; the process is coded through quantum coding; and the niche technology is used to initialize the population, adaptive rotation angle, and quantum mutation strategy to improve the efficiency of the QGA and avoid premature convergence. Lastly, through the Kacem standard calculation example and the reliability analysis of the factory workshop processing process example, performance evaluation is conducted to show that the improved QGA has good convergence and does not fall into premature ability, the improved QGA can solve the problem of reasonable deployment of machines and personnel in the workshop, and the proposed method is more effective for the DRCFJSP than some existing methods. The findings can provide a good theoretical basis for actual production and application.

Keywords: dual resource constrained; flexible job shop scheduling; improved quantum genetic algorithm; niching technique; adaptive rotation angle

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MDPI and ACS Style

Zhang, S.; Du, H.; Borucki, S.; Jin, S.; Hou, T.; Li, Z. Dual Resource Constrained Flexible Job Shop Scheduling Based on Improved Quantum Genetic Algorithm. Machines 2021, 9, 108. https://doi.org/10.3390/machines9060108

AMA Style

Zhang S, Du H, Borucki S, Jin S, Hou T, Li Z. Dual Resource Constrained Flexible Job Shop Scheduling Based on Improved Quantum Genetic Algorithm. Machines. 2021; 9(6):108. https://doi.org/10.3390/machines9060108

Chicago/Turabian Style

Zhang, Shoujing, Haotian Du, Sebastian Borucki, Shoufeng Jin, Tiantian Hou, and Zhixiong Li. 2021. "Dual Resource Constrained Flexible Job Shop Scheduling Based on Improved Quantum Genetic Algorithm" Machines 9, no. 6: 108. https://doi.org/10.3390/machines9060108

APA Style

Zhang, S., Du, H., Borucki, S., Jin, S., Hou, T., & Li, Z. (2021). Dual Resource Constrained Flexible Job Shop Scheduling Based on Improved Quantum Genetic Algorithm. Machines, 9(6), 108. https://doi.org/10.3390/machines9060108

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Dual Resource Constrained Flexible Job Shop Scheduling Based on Improved Quantum Genetic Algorithm

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