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A hybrid algorithm for integrated scheduling problem of complex products with tree structure

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Abstract

Due to the poor design of encoding methods or evolutionary operators in previous genetic-algorithm-based integrated scheduling algorithms, this paper proposes an integrated scheduling algorithm based on a hybrid genetic algorithm and tabu search. Firstly, an encoding method based on a dynamic schedulable operation set is proposed. This method cannot only reflect the priority constraints among operations, but also avoid the problems of imposing constraints and missing solution space in previous division encoding method. Secondly, a decoding method based on machine idle time driving is presented to handle the scheduling order of operations on different machines. Then, two different discrete crossover and mutation operators are designed to ensure the legitimacy of the processing sequence of the same machine. Finally, a local search strategy based on tabu search is shown to enhance the search capability for superior solutions. The algorithm is tested by the randomly generated instances, and the experimental results indicate that the proposed algorithm is effective and can achieve satisfactory performance.

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References

  1. Abualigah L, Diabat A (2020) A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Clust Comput https://doi.org/10.1007/s10586-020-03075-5

  2. Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423–435

    Article  Google Scholar 

  3. Abualigah LM, Khader AT, Hanandeh ES (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466

    Article  Google Scholar 

  4. Andrade CE, Silva T, Pessoa LS (2019) Minimizing flowtime in a flowshop scheduling problem with a biased random-key genetic algorithm. Expert Syst Appl 128:67–80

    Article  Google Scholar 

  5. Borisovsky P, Eremeev A, Kallrath J (2020) Multi-product continuous plant scheduling: combination of decomposition, genetic algorithm, and constructive heuristic. Int J Prod Res 58(9):2677–2695

    Article  Google Scholar 

  6. Dios M, Fernandez VV, Framinan JM (2018) Efficient heuristics for the hybrid flow shop scheduling problem with missing operations. Comput Ind Eng 115:88–99

    Article  Google Scholar 

  7. Gao K, Yang F, Zhou M, Pan Q, Suganthan PN (2018) Flexible job-shop rescheduling for new job insertion by using discrete jaya algorithm. IEEE Trans Cybern 49(5):1944–1955

    Article  Google Scholar 

  8. Komaki G, Sheikh S, Malakooti B (2019) Flow shop scheduling problems with assembly operations: a review and new trends. Int J Prod Res 57 (10):2926–2955

    Article  Google Scholar 

  9. Lee DH, Na MW, Song JB, Park CH, Park DI (2019) Assembly process monitoring algorithm using force data and deformation data. Robot Comput Integr Manuf 56:149–156

    Article  Google Scholar 

  10. Lei Q, Guo W, Song Y (2018) Integrated scheduling algorithm based on an operation relationship matrix table for tree-structured products. Int J Prod Res 56(16):5437–5456

    Article  Google Scholar 

  11. Liu L, Liu Y, Zhang J (2018) Learning-based hand motion capture and understanding in assembly process. IEEE Trans Ind Electron

  12. Liu H, Xu B, Lu D, Zhang G (2018) A path planning approach for crowd evacuation in buildings based on improved artificial bee colony algorithm. Appl Soft Comput 68:360–376

    Article  Google Scholar 

  13. Luo J, El Baz D, Xue R, Hu J (2020) Solving the dynamic energy aware job shop scheduling problem with the heterogeneous parallel genetic algorithm. Futur Gener Comput Syst 108:119–134

    Article  Google Scholar 

  14. Mahalingam T, Subramoniam M (2019) A hybrid gray wolf and genetic whale optimization algorithm for efficient moving object analysis. Multimedia Tools and Applications 78(18):26633–26659

    Article  Google Scholar 

  15. Mohammed AM, Duffuaa SO (2020) A tabu search based algorithm for the optimal design of multi-objective multi-product supply chain networks. Expert Syst Appl 140:112808

    Article  Google Scholar 

  16. Rossit DA, Tohmé F, Frutos M (2018) The non-permutation flow-shop scheduling problem: a literature review. Omega 77:143–153

    Article  Google Scholar 

  17. Shi F, Zhao S (2017) Product comprehensive scheduling problems solved by genetic algorithm based on operation constraint chain coding. China Mech Eng 28(20):2483–2492

    Google Scholar 

  18. Sun L, Lin L, Gen M, Li H (2019) A hybrid cooperative coevolution algorithm for fuzzy flexible job shop scheduling. IEEE Trans Fuzzy Syst 27(5):1008–1022

    Article  Google Scholar 

  19. Wang F, Zhao G, Jia Z, Lu X, Wang L (2010) Assembly job shop scheduling based on feasible solution space genetic algorithm. Comput Integr Manuf Syst 16(1):115–120

    Google Scholar 

  20. Xie Z, Hao S, Ye G, Tan G (2009) A new algorithm for complex product flexible scheduling with constraint between jobs. Comput Ind Eng 57(3):766–772

    Article  Google Scholar 

  21. Xie Z, Guo H, Su W, Xin Y, Yang J (2018) Reversal sequence integrated scheduling algorithm of multiple workshop with multi-procedures ended together. J Jilin Univ (Engineering and Technology Edition) 048 (2):578–587

    Google Scholar 

  22. Xie Z, Li Z, Xue J, Xin Y (2017) Machine-driven integrated scheduling algorithm with equipment’ idle adjustment. Transactions of Beijing Institute of Technology 37(5):532–536

    MATH  Google Scholar 

  23. Xie Z, Wang Y, Xin Y, Shao X (2016) Integrated scheduling algorithm of device-driven mechanism between two workshops using migration time. Journal of Shanghai Jiaotong University 50(6):929–936

    MathSciNet  MATH  Google Scholar 

  24. Xie Z, Zhang X, Gao Y, Xin Y (2018) Time-selective integrated scheduling algorithm considering the compactness of serial processes. J Mech Eng 054 (006):191–202

    Article  Google Scholar 

  25. Xie Z, Zhang X, Xin Y, Yang J (2018) Time-selective integrated scheduling algorithm considering posterior processes. Acta Automatica Sinica 44 (2):344–362

    MATH  Google Scholar 

  26. Xie Z, Zheng F, Xia Y (2017) An algorithm of asymmetric three workshops integrated scheduling with batch equalization processing. Transactions of Beijing Institute of Technology 37(3):274–280

    MathSciNet  MATH  Google Scholar 

  27. Yu K, Wang X, Wang Z (2016) An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems. J Intell Manuf 27(4):831–843

    Article  Google Scholar 

  28. Zhang J, Ding G, Zou Y, Qin S, Fu J (2019) Review of job shop scheduling research and its new perspectives under industry 4.0. J Intell Manuf 30(4):1809–1830

    Article  Google Scholar 

  29. Zhang S, Li X, Zhang B, Wang S (2020) Multi-objective optimisation in flexible assembly job shop scheduling using a distributed ant colony system. Eur J Oper Res 283(2):441–460

    Article  MathSciNet  MATH  Google Scholar 

  30. Zhang X, Ma C, Wu J (2019) Multi-batch integrated scheduling algorithm based on time-selective. Multimedia Tools and Applications 78(21):29989–30010

    Article  Google Scholar 

  31. Zhang X, Xie Z, Xin Y, Yang J (2017) Integrated scheduling algorithm of two workshops based on optimal time. Comput Integr Manuf Syst 9:1938–1949

    Google Scholar 

  32. Zhang X, Xie Z, Xin Y, Yang J (2019) Time-selective integrated scheduling algorithm with backtracking adaptation strategy. Expert Syst 36(3):e12305

    Article  Google Scholar 

  33. Zhao S, Han Q, Wang G (2015) Product comprehensive scheduling algorithm based on virtual component level division coding. Comput Integr Manuf Syst 21(9):2435–2445

    Google Scholar 

Download references

Acknowledgements

We acknowledge the support of the National Natural Science Foundation of China [grant numbers 61772160, 61370086]; Heilongjiang Province Postdoctoral Science Foundation of China [grant number LBHQ13092], National University of Computer Education Research Association of china [grant number ER2014018], the Heilongjiang Postdoctoral Science Foundation of china [grant number LBH-Z15096], Postdoctoral Science Foundation of China [grant number 2016M591541].

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Correspondence to Zhiqiang Xie.

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Gao, Y., Xie, Z. & Yu, X. A hybrid algorithm for integrated scheduling problem of complex products with tree structure. Multimed Tools Appl 79, 32285–32304 (2020). https://doi.org/10.1007/s11042-020-09477-2

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