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Multi-batch integrated scheduling algorithm based on time-selective

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Abstract

Based on the problem that the integrated scheduling algorithm cannot fully consider the impact of the scheduling process on the subsequent process so that the scheduling results are impacted, this paper presents a multi-batch integrated scheduling algorithm based on time-selective. This algorithm proposes a process sequence sequencing strategy,it divides the whole structure of the process tree into several process sequences and determines the scheduling order according to the path length. The multi-batch time-selective scheduling strategy generates several process combination plans. It presents the process combination selection strategy chooses the combination plan most close to scheduling targets among the different combination plans. The analysis and example show that this algorithm is better in multi-batch integrated scheduling.

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References

  1. Baioletti M, Milani A, Santucci V (2016) A discrete differential evolution algorithm for multi-objective permutation flowshop scheduling. Intel Artif 10(2):81–95

    MATH  Google Scholar 

  2. Li J, Pan Y (2013) A hybrid discrete particle swarm optimization algorithm for solving fuzzy job shop scheduling problem. Int J Adv Manuf Technol 66(1):583–596

    Article  Google Scholar 

  3. Li X, Zhang K (2018) Single batch processing machine scheduling with two-dimensional bin packing constraints. Int J Prod Econ 196:113–121

    Article  Google Scholar 

  4. Lin L, Gen M (2018) Hybrid evolutionary optimisation with learning for production scheduling: state-of-the-art survey on algorithms and applications. Int J Prod Res 56(1–2):193–223

    Article  Google Scholar 

  5. Liu WB (2016) A hybrid differential evolution algorithm based on dynamic variable neighborhood search for permutation Flowshop scheduling problem. Appl Mech Mater 4254(835):847–857

    Article  Google Scholar 

  6. Qiang L, Xiao T (2007) Model extended BOA to solve hybrid assembly scheduling problems. Comput Integr Manuf Syst 02:317–322

    Google Scholar 

  7. Santucci V, Baioletti M, Milani A (2015) Solving permutation flowshop scheduling problems with a discrete differential evolution algorithm. AI Commun 29(2):269–286

    Article  MathSciNet  MATH  Google Scholar 

  8. Shahvari O, Logendran R (2018) A comparison of two stage-based hybrid algorithms for a batch scheduling problem in hybrid flow shop with learning effect. Int J Prod Econ 195:227–248

    Article  Google Scholar 

  9. Singh MR, Singh M, Mahapatra SS et al (2016) Particle swarm optimization algorithm embedded with maximum deviation theory for solving multi-objective flexible job shop scheduling problem. Int J Adv Manuf Technol 85(9):2353–2366

    Article  Google Scholar 

  10. Wang H, Yan H, Wang Z (2014) Adaptive assembly shceudling of aero-engine based on double-layer Q-learning in knowledgeable manufacturing. Comput Integr Manuf Syst 12:3000–3010

    Google Scholar 

  11. Xie Z, Liu S, Qiao P (2003) Dynamic job-shop scheduling algorithm based on ACPM and BFSM. J Comput Res Dev 40(7):977–983

    Google Scholar 

  12. Xie Z, Yang J, Yang G et al (2008) Dynamic machine job-shop scheduling algorithm with dynamic machine set of operation having priority[J]. Chin J Comput 31(3):502–508

    Article  Google Scholar 

  13. Xie Z, Xin Y, Yang J (2011) Integrated scheduling algorithm based on event driven by machines’idle. J Mech Eng 47(11):139–147

    Article  Google Scholar 

  14. Xie Z, Xin Y, Yang J (2011) Machine-driven integrated scheduling algorithm with rollback-preemptive. Automatica Sin 37(11):1332–1343

    Google Scholar 

  15. Z Xie, Y Wang, J Yang (2011) Batch integrated scheduling algorithm with constraint of 2 operations batches processing. J Beijing Univ Technol (10): 1470–1476 +1481

  16. Xie Z, Liu C, Yang J (2012) Batch integrated scheduling algorithm considering posterior operations and with constraint of 2 operations batches processing. J Shanghai Jiaotong Univ 46(11):1746–1758

    MathSciNet  Google Scholar 

  17. Xie Z, Xin Y, Yang J (2013) Multi-batch processing integrated scheduling algorithm based on signal driven. Chin J Comput 36(4):818–828

    Article  Google Scholar 

  18. Zhiqiang X, Jing Y, Yong Z, Dali Z, Guangyu T (2011) Dynamic critical paths multi-product manufacturing scheduling algorithm based on operation set. Chin J Comput 34(2):406–412

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China(No. U1731128, No. 51374035), Foundation of Liaoning Educational committee under the Grant No.2016HZPY09.

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Correspondence to Chi Ma.

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Zhang, X., Ma, C. & Wu, J. Multi-batch integrated scheduling algorithm based on time-selective. Multimed Tools Appl 78, 29989–30010 (2019). https://doi.org/10.1007/s11042-018-6805-8

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  • DOI: https://doi.org/10.1007/s11042-018-6805-8

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