Proceedings of the 5th International Conference on Economic Management and Model Engineering, ICEMME 2023, November 17–19, 2023, Beijing, China

Research Article

Scheduling Modeling and Optimization of 3D Print Task in Cloud Manufacturing Environment Based on Quantum Wolf Pack Algorithm

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  • @INPROCEEDINGS{10.4108/eai.17-11-2023.2342663,
        author={Pianpian  Gao},
        title={Scheduling Modeling and Optimization of 3D Print Task in Cloud Manufacturing Environment Based on Quantum Wolf Pack Algorithm},
        proceedings={Proceedings of the 5th International Conference on Economic Management and Model Engineering, ICEMME 2023, November 17--19, 2023, Beijing, China},
        publisher={EAI},
        proceedings_a={ICEMME},
        year={2024},
        month={2},
        keywords={cloud manufacturing; 3d printing; production scheduling; resource matching; quantum wolf pack algorithm; discrete event simulation},
        doi={10.4108/eai.17-11-2023.2342663}
    }
    
  • Pianpian Gao
    Year: 2024
    Scheduling Modeling and Optimization of 3D Print Task in Cloud Manufacturing Environment Based on Quantum Wolf Pack Algorithm
    ICEMME
    EAI
    DOI: 10.4108/eai.17-11-2023.2342663
Pianpian Gao1,*
  • 1: Shandong Jianzhu University
*Contact email: gaopian@qq.com

Abstract

The cloud manufacturing service model effectively solves the problem of uneven distribution of manufacturing resources in the entire manufacturing industry and realizes the efficient use of manufacturing resources. We fully consider the heterogeneity of user orders and 3D printing, and build a supply and demand matching and task scheduling model for distributed 3D printing tasks and 3D printing equipment resources under cloud manufacturing, aiming at the characteristics of equipment resources and diversified production target requirements in the cloud manufacturing environment. In order to effectively solve the above problems, we propose a hybrid optimization method based on quantum wolf swarm algorithm and discrete event simulation. Among them, the design of local search improves the search process, and adopts Grover quantum algorithm to make the search more efficient. Numerical experiments show that the algorithm has good convergence and can find a satisfactory solution to the problem within a rational quantity of iterations.