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Application research of improved genetic algorithm based on machine learning in production scheduling

  • Deep Learning & Neural Computing for Intelligent Sensing and Control
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Abstract

Job shop scheduling problem is a well-known NP problem. It is limited by various conditions. As the scale of the problem increases, the difficulty of finding the optimal solution will increase. It is a difficult combination problem. Limited by the constraints of the actual production environment, how to effectively arrange the processing order of each part will directly affect the production efficiency, the appropriate production scheduling algorithm can correctly and effectively plan the enterprise resources and rationally arrange the processing order and processing time of the workpiece. Proper use of existing resources, by optimizing production scheduling instructions, to meet the basic requirements of production scheduling, in order to obtain the optimization of total production time, has important theoretical significance for the actual production of enterprises. In this paper, the mathematical model is abstracted on the basis of the production scheduling problem. According to the different parts of the same machine and the different processes of the same part, the corresponding processing time and waiting time are obtained. At the same time, the genetic algorithm is improved by genetic algorithm. A dynamic genetic operator based on the number of iterations is proposed, which further enhances the convergence performance and search ability of the genetic algorithm. Through the simulation of MATLAB simulation program, combined with the scheduling standard example, the performance analysis of different algorithms is carried out, the search efficiency of genetic algorithm is improved, the convergence performance of the algorithm is improved, and different optimization choices are obtained for different time weights. The operation results of the system meet the requirements of production scheduling, which proves the feasibility and practicability of the improved genetic algorithm.

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References

  1. Su N, Yi M, Zhang M (2017) Genetic programming for production scheduling: a survey with a unified framework. Complex & Intelligent Systems 3(1):41–66

    Article  Google Scholar 

  2. Lei X, Song S, Chen X et al (2016) Joint optimization of production scheduling and machine group preventive maintenance. Reliability Engineering & System Safety 146:68–78

    Article  Google Scholar 

  3. Rodammer FA, White KP (2015) A recent survey of production scheduling. IEEE Transactions on Systems Man & Cybernetics 18(6):841–851

    Article  MathSciNet  Google Scholar 

  4. Branke J, Nguyen S, Pickardt CW et al (2016) Automated design of production scheduling heuristics: a review. IEEE Transactions on Evolutionary Computation 20(1):110–124

    Article  Google Scholar 

  5. Gonzalez J, Reeves G (2017) Master production scheduling: a multiple-objective linear programming approach. International Journal of Production Research 21(4):553–562

    Article  Google Scholar 

  6. Shang Jianren, Tian Yunnan, Liu Yi, Liu Runlong (2018) Production scheduling optimization method based on hybrid particle swarm optimization algorithm. Journal of Intelligent and Fuzzy Systems 34(2):955–964

    Article  Google Scholar 

  7. Shrouf F, Ordieres-Meré J, García-Sánchez A et al (2014) Optimizing the production scheduling of a single machine to minimize total energy consumption costs. Journal of Cleaner Production 67(6):197–207

    Article  Google Scholar 

  8. Li Ming-Wei, Hong Wei-Chiang, Geng Jing, Wang Jianlun (2017) Berth and quay crane coordinated scheduling using multi-objective chaos cloud particle swarm optimization algorithm. Neural Computing and Applications 28(11):3163–3182

    Article  Google Scholar 

  9. Lamghari A, Dimitrakopoulos R, Ferland JA (2014) A variable neighbourhood descent algorithm for the open-pit mine production scheduling problem with metal uncertainty. Journal of the Operational Research Society 65(9):1305–1314

    Article  Google Scholar 

  10. Koo J, Kim BI (2016) Some comments on “Optimization of production scheduling with time-dependent and machine-dependent electricity cost for industrial energy efficiency”. International Journal of Advanced Manufacturing Technology 86(9):1–4

    Google Scholar 

  11. Gharaei Ali, Jolai Fariborz (2018) A multi-agent approach to the integrated production scheduling and distribution problem in multi-factory supply chain. Applied Soft Computing 65:577–589

    Article  Google Scholar 

  12. Chergui Akram, Hadj-Hamou Khaled, Vignat Frédéric (2018) Production scheduling and nesting in additive manufacturing. Computer and Industrial Engineering 126:292–301

    Article  Google Scholar 

  13. Low C, Chang CM, Li RK et al (2014) Coordination of production scheduling and delivery problems with heterogeneous fleet. International Journal of Production Economics 153(7):139–148

    Article  Google Scholar 

  14. Cui WW, Lu Z, Pan E (2014) Integrated production scheduling and maintenance policy for robustness in a single machine. Computers & Operations Research 47(7):81–91

    Article  MathSciNet  MATH  Google Scholar 

  15. Bakar MRA, Abbas IT, Kalal MA et al (2017) Solution for multi-objective optimisation master production scheduling problems based on swarm intelligence algorithms. Journal of Computational and Theoretical Nanoscience 14(11):5184–5194

    Article  Google Scholar 

  16. Lamghari A, Dimitrakopoulos R (2016) Network-flow based algorithms for scheduling production in multi-processor open-pit mines accounting for metal uncertainty. European Journal of Operational Research 250(1):273–290

    Article  MathSciNet  MATH  Google Scholar 

  17. Zeng L, Liang X, Yan OU et al (2016) Energy integrated scheduling based on multiobjective-constrained optimization evolutionary algorithm. Computer Integrated Manufacturing Systems 76(2):230–267

    Google Scholar 

  18. Jiang SL, Liu M, Lin JH et al (2016) A prediction-based online soft scheduling algorithm for the real-world steelmaking-continuous casting production. Knowledge-Based Systems 111:159–172

    Article  Google Scholar 

  19. Bierwirth C, Mattfeld DC (2014) Production scheduling and rescheduling with genetic algorithms. Evolutionary Computation 7(1):1–17

    Article  Google Scholar 

  20. Wen HJ, Hou SW (2014) Application of dimensional information sharing-based particle swarm optimization algorithm for production scheduling. Industrial Engineering Journal 44(8):2769–2771

    Google Scholar 

  21. Jiang T, Deng G (2018) Optimizing the low-carbon flexible job shop scheduling problem considering energy consumption. IEEE Access 6:46346–46355

    Article  Google Scholar 

  22. Yan HS, Wan XQ, Xiong FL (2015) Integrated production planning and scheduling for a mixed batch job-shop based on alternant iterative genetic algorithm. Journal of the Operational Research Society 66(8):1250–1258

    Article  Google Scholar 

  23. Zhang YX, Xu G (2015) A research of collateral production scheduling based on genetic algorithms. In: International conference on computer modeling and simulation

  24. Hai-Jun W, Shi-Wang H (2014) Application of dimensional information sharing-based particle swarm optimization algorithm for production scheduling. Ind Eng J 17(1):30–35

    Google Scholar 

  25. Soares MM, Vieira GE (2014) A new multi-objective optimization method for master production scheduling problems based on genetic algorithm. International Journal of Advanced Manufacturing Technology 41(5–6):549–567

    Google Scholar 

Download references

Acknowledgements

This work was supported by Henan Province Soft Science Research Project (172400410013); Henan Provincial Department of Education Science and Technology Research Key Project (17A630016); Henan Province Philosophy and Social Affairs Office Planning Project (2016G013); Innovation Method Project of the Ministry of Science and Technology (2017IM060100).

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Authors and Affiliations

  1. School of Management, Henan University of Science and Technology, Luoyang, 471003, China

    Kai Guo, Mei Yang & Hai Zhu

  2. Henan Collaborative Innovation Center of Nonferrous Metals, Luoyang, 471003, China

    Kai Guo

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  1. Kai Guo
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  2. Mei Yang
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Correspondence to Kai Guo.

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Guo, K., Yang, M. & Zhu, H. Application research of improved genetic algorithm based on machine learning in production scheduling. Neural Comput & Applic 32, 1857–1868 (2020). https://doi.org/10.1007/s00521-019-04571-5

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