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Flexible job-shop scheduling method based on interval grey processing time

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Abstract

With the complexity of industrial products, the processing time of products is affected by many factors, and it is difficult to give a concrete time estimate. Therefore, it is significant to study the flexible job shop scheduling problem (FJSP) with uncertain processing time. This paper defines the uncertain processing time as the interval grey processing time (IGPT). Also, an FJSP model with IGPT (G-FJSP) is formulated to minimize the interval grey maximum completion time, and the mathematical operation rules of IGPT are improved. Based on this, a step-size adaptive discrete particle swarm algorithm with load balancing (LS-DPSO) is put forward to solve the G-FJSP model. The experimental analysis on six classical test cases indicates that LS-DPSO outperforms four algorithms proposed in recent literature in terms of speed and solution quality. Taking IMK05 as an example, the minimum and the average values of LS-DPSO IGPT are 1.8% and 2.2% smaller than the optimal results of other four algorithms. Also, the resulting grey Gantt chart has better processing time flexibility to guide practical production.

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References

  1. Chu Y, You F, Wassick J M et al (2015) Integrated planning and scheduling under production uncertainties: bi-level model formulation and hybrid solution method. Comput Chem Eng 72:255–272. https://doi.org/10.1016/j.compchemeng.2014.02.023

    Article  Google Scholar 

  2. Johnson S M (1954) Optimal two- and three-stage production schedules with setup times included. Nav Res Logist Q 1(1):61–68. https://doi.org/10.1002/nav.3800010110

    Article  MATH  Google Scholar 

  3. Giffler B, Thompson GL (1960) Algorithms for solving production-scheduling problems. Oper Res 8(4):487–503. https://doi.org/10.1287/opre.8.4.487https://doi.org/10.1287/opre.8.4.487

    Article  MathSciNet  MATH  Google Scholar 

  4. Cook SA (1971) The complexity of theorem-proving procedures. In: Proceedings of the third annual ACM symposium on Theory of computing - STOC ’71, ACM Press. https://doi.org/10.1145/800157.805047https://doi.org/10.1145/800157.805047

  5. Aarts EHL, van Laarhoven PJM, Lenstra JK et al (1994) A computational study of local search algorithms for job shop scheduling. ORSA J Comput 6(2):118–125. https://doi.org/10.1287/ijoc.6.2.118https://doi.org/10.1287/ijoc.6.2.118

    Article  MATH  Google Scholar 

  6. Laguna M, Barnes J W, Glover F W (1991) Tabu search methods for a single machine scheduling problem. J Intell Manuf 2(2):63–73. https://doi.org/10.1007/bf01471219

    Article  Google Scholar 

  7. Nakano R, Yamada T (1991) Conventional genetic algorithm for job shop problems. In: Proceedings of the 4th international conference on genetic algorithms (ICGA), pp 474–479

  8. Brucker P, Schlie R (1990) Job-shop scheduling with multi-purpose machines. Computing 45 (4):369–375. https://doi.org/10.1007/bf02238804https://doi.org/10.1007/bf02238804

    Article  MathSciNet  MATH  Google Scholar 

  9. Wang C, Li Y, Li X (2021) Solving flexible job shop scheduling problem by a multi-swarm collaborative genetic algorithm. J Syst Eng Electron 32(2):261–271. https://doi.org/10.23919/jsee.2021.000023https://doi.org/10.23919/jsee.2021.000023

    Article  Google Scholar 

  10. Ding H, Gu X (2020) Improved particle swarm optimization algorithm based novel encoding and decoding schemes for flexible job shop scheduling problem. Comput Oper Res 121:104,951. https://doi.org/10.1016/j.cor.2020.104951

    Article  MathSciNet  MATH  Google Scholar 

  11. Zadeh MS, Katebi Y, Doniavi A (2018) A heuristic model for dynamic flexible job shop scheduling problem considering variable processing times. Int J Prod Res 57(10):3020–3035. https://doi.org/10.1080/00207543.2018.1524165

    Article  Google Scholar 

  12. Wu X, Peng J, Xie Z, et al. (2021) An improved multi-objective optimization algorithm for solving flexible job shop scheduling problem with variable batches. J Syst Eng Electron 32(2):272–285. https://doi.org/10.23919/jsee.2021.000024

    Article  Google Scholar 

  13. Chen XZ (2018) Process planning technology of mechanical machining system for generalized energy efficiency. PhD thesis Chongqing University, Chongqing, China

  14. Ishii H, Tada M (1995) Single machine scheduling problem with fuzzy precedence relation. Eur J Oper Res 87(2):284–288. https://doi.org/10.1016/0377-2217(94)00162-6

    Article  MATH  Google Scholar 

  15. Sakawa M, Mori T (1999) An efficient genetic algorithm for job-shop scheduling problems with fuzzy processing time and fuzzy duedate. Comput Ind Eng 36(2):325–341. https://doi.org/10.1016/s0360-8352(99)00135-7https://doi.org/10.1016/s0360-8352(99)00135-7

    Article  Google Scholar 

  16. Gao KZ, Suganthan PN, Pan QK et al (2016) An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time. Expert Syst Appl 65:52–67. https://doi.org/10.1016/j.eswa.2016.07.046

    Article  Google Scholar 

  17. Palacios JJ, Gonzȧlez-rodríguez I, Vela CR et al (2017) Robust multiobjective optimisation for fuzzy job shop problems. Appl Soft Comput 56:604–616. https://doi.org/10.1016/j.asoc.2016.07.004https://doi.org/10.1016/j.asoc.2016.07.004

    Article  Google Scholar 

  18. Gen M, Lin L, Ohwada H (2021) Advances in hybrid evolutionary algorithms for fuzzy flexible job-shop scheduling: state-of-the-art survey. In: Proceedings of the 13th international conference on agents and artificial intelligence. SCITEPRESS - science and technology publications. https://doi.org/10.5220/0010429605620573

  19. Xie N, Chen N (2018) Flexible job shop scheduling problem with interval grey processing time. Appl Soft Comput 70:513–524. https://doi.org/10.1016/j.asoc.2018.06.004

    Article  Google Scholar 

  20. Deng JL (1982) Control problems of grey systems. Syst Control Lett 1 (5):288–294. https://doi.org/10.1016/s0167-6911(82)80025-xhttps://doi.org/10.1016/s0167-6911(82)80025-x

    Article  MathSciNet  MATH  Google Scholar 

  21. Zeng B, Liu S, Xie N (2010) Prediction model of interval grey number based on DGM(1,1). J Syst Eng Electron 21(4):598–603. https://doi.org/10.3969/j.issn.1004-4132.2010.04.011

    Article  Google Scholar 

  22. Garcez TV, Cavalcanti HT, de Almeida AT (2021) A hybrid decision support model using grey relational analysis and the additive-veto model for solving multicriteria decision-making problems: an approach to supplier selection. Ann Oper Res 304(1-2):199–231. https://doi.org/10.1007/s10479-021-04103-2

    Article  MathSciNet  MATH  Google Scholar 

  23. Tirkolaee EB, Torkayesh AE (2022) A cluster-based stratified hybrid decision support model under uncertainty: sustainable healthcare landfill location selection. Appl Intell. https://doi.org/10.1007/s10489-022-03335-4

  24. Li B, Gu X (2006) Grey chance constrained programming for finite intermediate storage flow shop scheduling under uncertainty. In: 2006 6th world congress on intelligent control and automation, IEEE. https://doi.org/10.1109/wcica.2006.1713269

  25. Zhu Z, Zhou X (2020) Flexible job-shop scheduling problem with job precedence constraints and interval grey processing time. Comput Ind Eng 149:106,781. https://doi.org/10.1016/j.cie.2020.106781https://doi.org/10.1016/j.cie.2020.106781

    Article  Google Scholar 

  26. Brandimarte P (1993) Routing and scheduling in a flexible job shop by tabu search. Ann Oper Res 41(3):157–183. https://doi.org/10.1007/bf02023073https://doi.org/10.1007/bf02023073

    Article  MATH  Google Scholar 

  27. Shi DL, Zhang BB, Li Y (2020) A multi-objective flexible job-shop scheduling model based on fuzzy theory and immune genetic algorithm. Int J Simul Model 19(1):123–133. https://doi.org/10.2507/ijsimm19-1-co1

    Article  Google Scholar 

  28. Li J q, Zm Liu, Li C, et al. (2021) Improved artificial immune system algorithm for type-2 fuzzy flexible job shop scheduling problem. IEEE Trans Fuzzy Syst 29(11):3234–3248. https://doi.org/10.1109/tfuzz.2020.3016225

    Article  Google Scholar 

  29. Pan Z, Lei D, Wang L (2022) A bi-population evolutionary algorithm with feedback for energy-efficient fuzzy flexible job shop scheduling. IEEE Trans Syst Man Cybern Syst 52(8):5295–5307. https://doi.org/10.1109/tsmc.2021.3120702

    Article  Google Scholar 

  30. Li R, Gong W, Lu C (2022) A reinforcement learning based RMOEA/D for bi-objective fuzzy flexible job shop scheduling. Expert Syst Appl 203:117,380. https://doi.org/10.1016/j.eswa.2022.117380https://doi.org/10.1016/j.eswa.2022.117380

    Article  Google Scholar 

  31. Zhu Z, Zhou X (2021) A multi-objective multi-micro-swarm leadership hierarchy-based optimizer for uncertain flexible job shop scheduling problem with job precedence constraints. Expert Syst Appl 182:115,214. https://doi.org/10.1016/j.eswa.2021.115214

    Article  Google Scholar 

  32. Jamrus T, Chien C F, Gen M et al (2018) Hybrid particle swarm optimization combined with genetic operators for flexible job-shop scheduling under uncertain processing time for semiconductor manufacturing. IEEE Trans Semicond Manuf 31(1):32–41. https://doi.org/10.1109/tsm.2017.2758380

    Article  Google Scholar 

  33. Yao L, Liu Y, Zhao H et al (2019) An improved UKPK-PSO algorithm inspired from block chain technology for flexible job shop scheduling problem. In: 2019 Chinese control conference (CCC). https://doi.org/10.23919/chicc.2019.8866111https://doi.org/10.23919/chicc.2019.8866111. IEEE, China, pp 2260–2265

  34. Zhang Y, Zhu H, Tang D (2020) An improved hybrid particle swarm optimization for multi-objective flexible job-shop scheduling problem. Kybernetes 49(12):2873–2892. https://doi.org/10.1108/k-06-2019-0430https://doi.org/10.1108/k-06-2019-0430

    Article  Google Scholar 

  35. Zhang GH (2009) Research on methods for flexible job shop scheduling problems. PhD thesis. Huazhong University of Science and Technology, China

    Google Scholar 

  36. Xu WX, Wang Q, Bian WB et al (2017) Improved GA and global random machine selection based on key operation to solve FJSP. CIESC Journal 68(3):1073–1080. https://doi.org/10.11949/j.issn.0438-1157.20161625https://doi.org/10.11949/j.issn.0438-1157.20161625

    Google Scholar 

  37. Pan QK, Wang WH, Zhu JY et al (2007) Hybrid heuristics based on particle swarm optimization and variable neighborhood search for job shop scheduling. Comput Integr Manuf 2:323–328. https://doi.org/10.13196/j.cims.2007.02.117.panqk.019

    Google Scholar 

  38. Sudholt D, Witt C (2010) Runtime analysis of a binary particle swarm optimizer. Theor Comput Sci 411(21):2084–2100. https://doi.org/10.1016/j.tcs.2010.03.002

    Article  MathSciNet  MATH  Google Scholar 

  39. Cui W, Li X, Zhou S et al (2007) Investigation on process parameters of electrospinning system through orthogonal experimental design. J Appl Polym Sci 103(5):3105–3112. https://doi.org/10.1002/app.25464https://doi.org/10.1002/app.25464

    Article  Google Scholar 

  40. Cheng BW, Chang CL (2007) A study on flowshop scheduling problem combining taguchi experimental design and genetic algorithm. Expert Syst Appl 32(2):415–421. https://doi.org/10.1016/j.eswa.2005.12.002https://doi.org/10.1016/j.eswa.2005.12.002

    Article  Google Scholar 

  41. Gao KZ, Suganthan PN, Pan QK et al (2014) Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives. J Intell Manuf 27(2):363–374. https://doi.org/10.1007/s10845-014-0869-8

    Article  Google Scholar 

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Acknowledgements

This study was supported by the Scientific Research Program of Beijing Municipal Commission of Education-Natural Science Foundation of Beijing (KZ202210017024) and the Interdisciplinary Research Exploration Program of Beijing Institute of Petrochemical Technology (BIPTCSF-008); the General Project of Scientific Research and Technology Program of Beijing Municipal Education Commission (KM201810017006).

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Correspondence to Zhimei Lei.

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Xu, W., Wu, W., Wang, Y. et al. Flexible job-shop scheduling method based on interval grey processing time. Appl Intell 53, 14876–14891 (2023). https://doi.org/10.1007/s10489-022-04213-9

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