Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Developing a Flexible Manufacturing Control System Considering Mixed Uncertain Predictive Maintenance Model: a Simulation-Based Optimization Approach

  • Original Research
  • Published:
Operations Research Forum Aims and scope Submit manuscript

Abstract

Nowadays, with the development of information technology infrastructure, most systems are moving towards intelligent models, among which the field of production is no exception. One of the important sketch of production models is in term of predictive maintenance control systems such as reliability centered maintenance (RCM). This research focuses on developing a popular flexible manufacturing system called flexible job shop scheduling problem (FJSP) with predictive maintenance terms of RCM that are able to measure the level of production system reliability and determine the required maintenance activities. Moreover, since in production environments, processing time is an approximate parameter due to the activities in which manpower is involved, processing times of the model are defined with fuzzy functions. Meanwhile, to cope with stochastic nature of RCM and fuzzy nature of the process times, Buckley fuzzy numbers are implemented. In fact, this research introduces a mixed uncertain model that considers mentioned natures of probability and possibility at the same time by means developing fuzzy numbers of process time thorough confidence intervals of them. Then, since developed model is NP-Hard and stochastic, two simulation-based optimization (SBO) approaches are introduced based on two meta-heuristic algorithms called genetic algorithm (GA) and imperialist competition algorithm (ICA). Finally, various creative statistical and qualitative outputs are presented to analysis the performance of the introduced SBOs for solving the developed FJSP integrated with RCM control terms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

Availability of Data and Material

Not applicable.

Code Availability

Not applicable.

Abbreviations

t:

Time t

m:

Machine

j :

Job j

\({O}_{ij}\) :

Operation jth of job ith

\({\tilde{C }}_{max}\) :

Uncertain makespan

\({\tilde{s }}_{ijk}\) :

Uncertain start time of \({O}_{ij}\)

\({\tilde{p }}_{ijk}\) :

Uncertain processing time of \({O}_{ij}\)

\({\tilde{c }}_{ij}\) :

Uncertain completion time of \({O}_{ij}\)

\({M}_{ij}\) :

Capable machines

\({v}_{ijk}\) :

\({v}_{ijk}\in \left\{0.1\right\}\) Assigning \({v}_{ij}\) to machine k

\({z}_{ijhgk}\) :

\({z}_{ijhgk}\in \left\{0.1\right\}\) Precedence of \({O}_{ij}\) and \({O}_{hg}\) on machine k

RL { m } :

Reliability of machine m

PMD:

Stochastic PM duration

RLPM:

Stochastic recovery level through PM

CMD:

Stochastic CM duration

RLCM:

Stochastic recovery level through CM

TBS:

Stochastic time between two shocks

References

  1. Sun L, Lin L, Lib H, Genc M (2018) Large scale flexible scheduling optimization by a distributed evolutionary algorithm. Comput Ind Eng 128:894–904. https://doi.org/10.1016/j.cie.2018.09.025

    Article  Google Scholar 

  2. Bagheri A, Zandieh M, Mahdavi I, Yazdani M (2010) An artificial immune algorithm for the flexible job-shop scheduling problem. Futur Gener Comput Syst 26:533–541. https://doi.org/10.1016/j.future.2009.10.004

    Article  Google Scholar 

  3. Rahmati SHA, Ahmadi A, Karimi B (2017) Multi-objective evolutionary simulation based optimization mechanism for a novel stochastic reliability centered maintenance problem. Swarm Evol Comput 40:255–271. https://doi.org/10.1016/j.swevo.2018.02.010

    Article  Google Scholar 

  4. Rahmati SHA, Ahmadi A, Karimi B (2018) Developing simulation based optimization mechanism for a novel stochastic reliability centered maintenance problem. Sci Iran 25:2788–2806. https://doi.org/10.24200/SCI.2017.4461

  5. Moubray J (1997) Reliability-centered Maintenance II. Industrial Press, New York

    Google Scholar 

  6. Nakagawa T (2007) Shock and Damage Models in Reliability Theory. Springer Series in Reliability Engineering. Springer, London

    Google Scholar 

  7. Ben-Daya M, Duffuaa S, Raouf A (2000) Maintenance, modeling and optimization. Kluwer Academic, London

    Book  Google Scholar 

  8. Caballé NC, Castro IT, Pérez CJ, Lanza-Gutiérrez JM (2015) A condition-based maintenance of a dependent degradation threshold-shock model in a system with multiple degradation processes. Reliab Eng Syst Saf 134:98–109. https://doi.org/10.1016/j.ress.2014.09.024

    Article  Google Scholar 

  9. Graves GH, Lee CY (1999) Scheduling maintenance and semiresumable jobs on a single machine. Nav Res Logist 46:845–863

    Article  Google Scholar 

  10. Cassady CR, Kutanoglu E (2005) Integrating preventive maintenance planning and production scheduling for a single machine. IEEE Trans Reliab 54:304–309. https://doi.org/10.1109/TR.2005.845967

    Article  Google Scholar 

  11. Sortrakul N, Nachtmann HL, Cassady CR (2005) Genetic algorithms for integrated preventive maintenance planning and production scheduling for a single machine. Comput Ind 56:161–168. https://doi.org/10.1016/j.compind.2004.06.005

    Article  Google Scholar 

  12. Mauguiere PH, Billaut JC, Bouquard JL (2005) New single machine and job-shop scheduling problems with availability constraints. J Sched 8(3):211–231. https://doi.org/10.1007/s10951-005-6812-2

    Article  Google Scholar 

  13. Chen JS (2008) Scheduling of nonresumable jobs and flexible maintenance activities on a single machine to minimize makespan. Eur J Oper Res 190:90–102. https://doi.org/10.1016/j.ejor.2007.06.029

    Article  Google Scholar 

  14. Chen WJ (2009) Minimizing number of tardy jobs on a single machine subject to periodic maintenance. Omega 37:592–599. https://doi.org/10.1016/j.omega.2008.01.001

    Article  Google Scholar 

  15. Pan E, Liao W, Xi L (2010) Single machine-based production scheduling model integrated preventive maintenance planning. The International Journal of Advanced Manufacturing Technology 50:365–375. https://doi.org/10.1007/s00170-009-2514-9

    Article  Google Scholar 

  16. Low C, Ji M, Hsu CJ, Su CT (2010) Minimizing the makespan in a single machine scheduling problems with flexible and periodic maintenance. Appl Math Model 34:334–342. https://doi.org/10.1016/j.apm.2009.04.014

    Article  Google Scholar 

  17. Ozturkoglu Y, Bulfin RL (2011) A unique integer mathematical model for scheduling deteriorating jobs with rate modifying-activities on a single machine. The International Journal of Advanced Manufacturing Technology 57:753–762. https://doi.org/10.1007/s00170-011-3303-9

    Article  Google Scholar 

  18. Kim BS, Ozturkoglu Y (2013) Scheduling a single machine with multiple preventive maintenance activities and position-based deteriorations using genetic algorithms. The International Journal of Advanced Manufacturing Technology 67:1127–1137. https://doi.org/10.1007/s00170-012-4553-x

    Article  Google Scholar 

  19. Ying KC, Lu CC, Chen JC (2016) Exact algorithms for single-machine scheduling problems with a variable maintenance. Comput Ind Eng 98:427–433. https://doi.org/10.1016/j.cie.2016.05.037

    Article  Google Scholar 

  20. Cui WW, Lu Z (2017) Minimizing the makespan on a single machine with flexible maintenances and jobs’ release dates. Comput Oper Res 80:11–12. https://doi.org/10.1016/j.cor.2016.11.008

    Article  Google Scholar 

  21. Shen J, Zhu K (2018) An uncertain single machine scheduling problem with periodic maintenance. Knowl-Based Syst 144:32–41. https://doi.org/10.1016/j.knosys.2017.12.021

    Article  Google Scholar 

  22. Liu Q, Dong M, Chen FF, Lv W, Ye C (2019) Single-machine-based joint optimization of predictive maintenance planning and production scheduling. Robotics and Computer-Integrated Manufacturing 55:173–182. https://doi.org/10.1016/j.rcim.2018.01.002

    Article  Google Scholar 

  23. Lee CY, Chen ZL (2000) Scheduling of jobs and maintenance activities on parallel machines. Nav Res Logist 47(2):145–165. https://doi.org/10.1002/(SICI)1520-6750(200003)47:2%3c145::AID-NAV5%3e3.0.CO;2-3

    Article  Google Scholar 

  24. Lin CH, Liao CJ (2007) Makespan minimization for two parallel machines with an unavailable period on each machine. The International Journal of Advanced Manufacturing Technology 33:1024–1030. https://doi.org/10.1007/s00170-006-0524-4

    Article  Google Scholar 

  25. Liao LW, Sheen GJ (2008) Parallel machine scheduling with machine availability and eligibility constraints. Eur J Oper Res 184:458–467. https://doi.org/10.1016/j.ejor.2006.11.027

    Article  Google Scholar 

  26. Berrichi A, Amodeo L, Yalaoui F, Châtelet E, Mezghiche M (2009) Bi-objective optimization algorithms for joint production and maintenance scheduling: application to the parallel machine problem. J Intell Manuf 20:389. https://doi.org/10.1007/s10845-008-0113-5

    Article  Google Scholar 

  27. Mellouli R, Sadfi C, Chu C, Kacem I (2009) Identical parallel machine scheduling under availability constraints to minimize the sum of completion times. Eur J Oper Res 197:1150–1165. https://doi.org/10.1016/j.ejor.2008.03.043

    Article  Google Scholar 

  28. Wang CH, Tsai SW (2014) Optimizing bi-objective imperfect preventive maintenance model for series-parallel system using established hybrid genetic algorithm. J Intell Manuf 25:603–616. https://doi.org/10.1007/s10845-012-0708-8

    Article  Google Scholar 

  29. Ebrahimipour V, Najjarbashi A, Sheikhalishahi M (2015) Multi-objective modeling for preventive maintenance scheduling in a multiple production line. J Intell Manuf 26:111–122. https://doi.org/10.1007/s10845-013-0766-6

    Article  Google Scholar 

  30. Yoo J, Lee IS (2016) Parallel machine scheduling with maintenance activities. Comput Ind Eng 101:361–371. https://doi.org/10.1016/j.cie.2016.09.020

    Article  Google Scholar 

  31. Shen J, Zhu Y (2019) A parallel-machine scheduling problem with periodic maintenance under uncertainty. J Ambient Intell Humaniz Comput 10:3171–3179. https://doi.org/10.1007/s12652-018-1032-8

    Article  Google Scholar 

  32. Lee CY (1999) Two-machine flow shop scheduling with availability constraints. Eur J Oper Res 114:420–429. https://doi.org/10.1016/S0377-2217(97)00452-9

    Article  Google Scholar 

  33. Espinouse M, Formanowlcz P, Penz B (2011) Complexity results and approximation algorithms for the two-machine no-wait flow-shop with limited machine availability. J Oper Res Soc 52:116–121. https://doi.org/10.1057/palgrave.jors.2601025

    Article  Google Scholar 

  34. Cheng T, Liu Z (2003) Approximability of two-machine no-wait ow shop scheduling with availability constraints. Oper Res Lett 31:319–322. https://doi.org/10.1016/S0167-6377(02)00230-4

    Article  Google Scholar 

  35. Aggoune R (2004) Minimizing the makespan for the flow shop scheduling problem with availability constraints. Eur J Oper Res 153:534–543. https://doi.org/10.1016/S0377-2217(03)00261-3

    Article  Google Scholar 

  36. Allaoui H, Artiba A (2004) Integrating simulation and optimization to schedule a hybrid ow shop with maintenance constraints. Eur J Oper Res 47:431–450. https://doi.org/10.1016/j.cie.2004.09.002

    Article  Google Scholar 

  37. Ruiz R, Garcia-Diaz JC, Maroto C (2007) Considering scheduling and preventive maintenance in the flow shop sequencing problem. Comput Oper Res 34(11):3314–3330. https://doi.org/10.1016/j.cor.2005.12.007

    Article  Google Scholar 

  38. Naderi B, Zandieh M, Fatemi Ghomi SMT (2009) Scheduling sequence-dependent setup time job shops with preventive maintenance. The International Journal of Advanced Manufacturing Technology 43:170–181. https://doi.org/10.1007/s00170-008-1693-0

    Article  Google Scholar 

  39. Safari E, Sadjadi SJ, Shahanaghi K (2010) Scheduling flow shops with condition-based maintenance constraint to minimize expected makespan. The International Journal of Advanced Manufacturing Technology 46:757–767. https://doi.org/10.1007/s00170-009-2151-3

    Article  Google Scholar 

  40. Safari E, Sadjadi SJ (2011) A hybrid method for flow shops scheduling with condition based maintenance constraint and machines breakdown. Expert Syst Appl 38(3):2020–2029. https://doi.org/10.1016/j.eswa.2010.07.138

    Article  Google Scholar 

  41. Huang RH, Yu SC (2016) Two-stage multiprocessor flow shop scheduling with deteriorating maintenance in cleaner production. J Clean Prod 135:276–283. https://doi.org/10.1016/j.jclepro.2016.06.109

    Article  Google Scholar 

  42. Khatami M, Zegordi SH (2017) Coordinative production and maintenance scheduling problem with flexible maintenance time intervals. J Intell Manuf 28:857–867. https://doi.org/10.1007/s10845-014-1001-9

    Article  Google Scholar 

  43. Ye H, Wang X, Liu K (2020) Adaptive preventive maintenance for flow shop scheduling with resumable processing. IEEE Trans Autom Sci Eng 18(1):106–113. https://doi.org/10.1109/TASE.2020.2978890

    Article  Google Scholar 

  44. Zribi N, Kamel E, Borne P (2008) Minimizing the makespan for the MPM job-shop with availability constraints. Int J Prod Econ 112:151–160. https://doi.org/10.1016/j.ijpe.2007.01.014

    Article  Google Scholar 

  45. Mati Y (2010) Minimizing the makespan in the non-preemptive job-shop scheduling with limited machine availability. Eur J Oper Res 59:537–543. https://doi.org/10.1016/j.cie.2010.06.010

    Article  Google Scholar 

  46. Ben Ali M, Sassi M, Gossa M, Harrath Y (2011) Simultaneous scheduling of production and maintenance tasks in the job shop. Int J Prod Res 49(13):3891–3918. https://doi.org/10.1080/00207543.2010.492405

    Article  Google Scholar 

  47. Zhou X, Lu Z, Xi L (2012) Preventive maintenance optimization for multi-component system under changing job shop schedule. Reliab Eng Syst Saf 101:14–20. https://doi.org/10.1016/j.ress.2012.01.005

    Article  Google Scholar 

  48. Zribi N, Borne P (2005) Hybrid genetic algorithm for the flexible job shop problem under maintenance constraints. Adv Natural Comput 3612:259–268

    Article  Google Scholar 

  49. Gao J, Gen M, Sun L (2006) Scheduling jobs and maintenance in flexible job shop with a hybrid genetic algorithm. J Intell Manuf 17:493–507. https://doi.org/10.1007/s10845-005-0021-x

    Article  Google Scholar 

  50. Wang S, Yu J (2010) An effective heuristic for flexible job-shop scheduling problem with maintenance activities. Comput Ind Eng 59(3):436–447. https://doi.org/10.1016/j.cie.2010.05.016

    Article  Google Scholar 

  51. Moradi E, Fatemi Ghomi SMT, Zandieh M (2011) Bi-objective optimization research on integrated fixed time interval preventive maintenance and production for scheduling flexible job-shop problem. Expert Syst Appl 38:7169–7178. https://doi.org/10.1016/j.eswa.2010.12.043

    Article  Google Scholar 

  52. Mokhtari H, Dadgar M (2015) Scheduling optimization of a stochastic flexible job-shop system with time-varying machine failure rate. Computers & Operation Research 61:31–45. https://doi.org/10.1016/j.cor.2015.02.014

    Article  Google Scholar 

  53. Palacios JJ, González MA, Vela CR, Rodríguez IG, Puente P (2015) Genetic tabu search for the fuzzy flexible job shop problem. Comput Oper Res 54:74–89. https://doi.org/10.1016/j.cor.2014.08.023

    Article  Google Scholar 

  54. Ahmadi E, Zandieh M, Farrokh M, Emami SM (2016) A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms. Comput Oper Res 73:56–66. https://doi.org/10.1016/j.cor.2016.03.009

    Article  Google Scholar 

  55. Rebai M, Kacem I, Adjallah KH (2013) Scheduling jobs and maintenance activities on parallel machines. Oper Res Int Journal 13:363–383. https://doi.org/10.1007/s12351-012-0130-1

    Article  Google Scholar 

  56. Dalfard VM, Mohammadi G (2012) Two meta-heuristic algorithms for solving multi-objective flexible job-shop scheduling with parallel machine and maintenance constraints. Comput Math Appl 64:2111–2117. https://doi.org/10.1016/j.camwa.2012.04.007

    Article  Google Scholar 

  57. Li JQ, Pan QK (2012) Chemical-reaction optimization for flexible job-shop scheduling problems with maintenance activity. Appl Soft Comput 12:2896–2912. https://doi.org/10.1016/j.asoc.2012.04.012

    Article  Google Scholar 

  58. Li JQ, Pan QK, Tasgetiren MF (2014) A discrete artificial bee colony algorithm for the multi-objective flexible job shop scheduling problem with maintenance activities. Appl Math Model 38:1111–1132. https://doi.org/10.1016/j.apm.2013.07.038

    Article  Google Scholar 

  59. Demir Y, Işleyen SK (2013) Evaluation of mathematical models for flexible job-shop scheduling problems. Appl Math Model 37:977–988. https://doi.org/10.1016/j.apm.2012.03.020

    Article  Google Scholar 

  60. Khoukhi FE, Boukachour J, Alaoui AEH (2017) The “Dual-Ants Colony”: a novel hybrid approach for the flexible job shop scheduling problem with preventive maintenance. Comput Ind Eng 106:236–255. https://doi.org/10.1016/j.cie.2016.10.019

    Article  Google Scholar 

  61. Buckley J (2005) Fuzzy statistics: hypothesis testing. soft. Computing 9:512–518. https://doi.org/10.1007/s00500-004-0368-5

    Article  Google Scholar 

  62. Mitchell M (1998) An introduction to genetic algorithms. The MIT press, London

    Book  Google Scholar 

  63. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition IEEE Congress Evol Comput 4661–4667. https://doi.org/10.1109/CEC.2007.4425083

  64. Rahmati SHA, Zandieh M (2012) A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem. The International Journal of Advanced Manufacturing Technology 58:1115–1129. https://doi.org/10.1007/s00170-011-3437-9

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Behrooz Shahbazi & Seyed Habib A. Rahmati

Authors
  1. Behrooz Shahbazi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seyed Habib A. Rahmati
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Not applicable.

Corresponding author

Correspondence to Seyed Habib A. Rahmati.

Ethics declarations

Ethics Approval

Not applicable.

Consent to Participate

We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.

Consent for Publication

Not applicable.

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shahbazi, B., Rahmati, S.H.A. Developing a Flexible Manufacturing Control System Considering Mixed Uncertain Predictive Maintenance Model: a Simulation-Based Optimization Approach. Oper. Res. Forum 2, 51 (2021). https://doi.org/10.1007/s43069-021-00098-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s43069-021-00098-5

Keyword

Search

Navigation