Skip to main content

Advertisement

SpringerLink
Log in

Modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

In manufacturing systems, there are environments where the elaboration of a product requires a series of sequential operations, involving the configuration of machines by stages, intermediate buffer capacities, definition of assembly lines, and routing of parts. The objective of this research is to develop a modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments. The methodological approach starts with the structural modeling, then the measurement of the complexity in the systems is developed, the hypotheses are proposed, and finally an experimental and factorial statistical analysis is developed. The results obtained corroborate the hypotheses proposed, where statistically the structural design factors and the variation of production time per stage have a significant influence on the response variable associated to the total complexity. Similarly, there is evidence of correlation between the performance indicators and the variable studied, in which the incidence with production costs stands out.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Quirk M (1999) Manufacturing, teams, and improvement: the human art of manufacturing. Prentice-Hall

    Google Scholar 

  2. Sánchez GV (2006) Introducción a la teoría económica un enfoque latinoamericano. Pearson educación

  3. Gaio L, Gino F, Zaninotto E (2002) I sistemi di produzione: manuale per la gestione operativa dell’impresa. Carocci

  4. Frizelle G, Woodcock E (1995) Measuring complexity as an aid to developing operational strategy. Int J Oper Prod Manag. https://doi.org/10.1108/01443579510083640

    Article  Google Scholar 

  5. Scholl A (1999) Balancing and sequencing of assembly lines (No. 10881). Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL)

  6. Vidal GH, Hernández JRC (2021) Complexity in manufacturing systems: a literature review. Production Engineering, 1–13

  7. Deshmukh AV, Talavage JJ, Barash MM (1998) Complexity in manufacturing systems, Part 1: Analysis of static complexity. IIE Trans 30(7):645–655

    Google Scholar 

  8. Manuj I, Sahin F (2011) A model of supply chain and supply chain decision-making complexity. International Journal of Physical Distribution & Logistics Management

  9. Papakostas N, Papachatzakis P, Xanthakis V, Mourtzis D, Chryssolouris G (2010) An approach to operational aircraft maintenance planning. Decis Support Syst 48(4):604–612

    Article  Google Scholar 

  10. Herbert S (1962) The architecture of complexity. Proc Am Philos Soc 106(6):467–482

    Google Scholar 

  11. Flynn BB, Flynn EJ (1999) Information-processing alternatives for coping with manufacturing environment complexity. Decis Sci 30(4):1021–1052

    Article  Google Scholar 

  12. Garbie IH, Shikdar A (2011) Analysis and estimation of complexity level in industrial firms. Int J Ind Syst Eng 8(2):175–197

    Google Scholar 

  13. Calinescu A, Efstathiou J, Bermejo J, Schirn J (1997) Modelling and simulation of a real complex process-based manufacturing system. In Proceedings of the Thirty-Second International Matador Conference (pp. 137–142). Palgrave, London

  14. Calinescu A, Efstathiou J, Bermejo J, Schirn J (1997) Assessing decision-making and process complexity in a manufacturer through simulation. IFAC Proceedings Volumes 30(24):149–152

    Article  Google Scholar 

  15. Almasarwah N, Süer G (2019) Flexible flowshop design in cellular manufacturing systems. Procedia Manufacturing 39:991–1001

    Article  Google Scholar 

  16. Kurz ME, Askin RG (2003) Comparing scheduling rules for flexible flow lines. Int J Prod Econ 85(3):371–388

    Article  Google Scholar 

  17. Quadt D, Kuhn H (2007) A taxonomy of flexible flow line scheduling procedures. Eur J Oper Res 178(3):686–698

    Article  MathSciNet  Google Scholar 

  18. Bouras A, Masmoudi M, Saadani NEH, Bahroun Z (2017) A three-stage appointment scheduling for an outpatient chemotherapy unit using integer programming. In 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT) (pp. 0916–0921). IEEE

  19. Guinet AGP, Solomon M (1996) Scheduling hybrid flowshops to minimize maximum tardiness or maximum completion time. Int J Prod Res 34(6):1643–1654

    Article  Google Scholar 

  20. Ho MH, Hnaien F, Dugardin F (2021) Electricity cost minimisation for optimal makespan solution in flow shop scheduling under time-of-use tariffs. Int J Prod Res 59(4):1041–1067

    Article  Google Scholar 

  21. Yan J, Li L, Zhao F, Zhang F, Zhao Q (2016) A multi-level optimization approach for energy-efficient flexible flow shop scheduling. J Clean Prod 137:1543–1552

    Article  Google Scholar 

  22. Dai M, Tang D, Giret A, Salido MA, Li WD (2013) Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer-Integrated Manufacturing 29(5):418–429

    Article  Google Scholar 

  23. Marichelvam MK, Geetha M, Tosun Ö (2020) An improved particle swarm optimization algorithm to solve hybrid flowshop scheduling problems with the effect of human factors-a case study. Comput Oper Res 114:104812

  24. Agnetis A, Pacifici A, Rossi F, Lucertini M, Nicoletti S, Nicolo F, Oriolo G, Pacciarelli D, Pesaro E (1997) Scheduling of flexible flow lines in an automobile assembly plant. Eur J Oper Res 97:348–362

    Article  Google Scholar 

  25. Tsubone H, Ohba M, Takamuki H, Miyake Y (1993) A production scheduling system for a hybrid flow shop-a case study. Omega 21(2):205–214

    Article  Google Scholar 

  26. Alisantoso D, Khoo LP, Jiang PY (2003) An immune algorithm approach to the scheduling of a flexible PCB flow shop. Int J Advanced Manufacturing Technol 22(11):819–827

    Article  Google Scholar 

  27. Piramuthu S, Raman N, Shaw MJ (1994) Learning-based scheduling in a flexible manufacturing flow line. IEEE Trans Eng Manage 41(2):172–182

    Article  Google Scholar 

  28. Wang S, Wang X, Chu F, Yu J (2020) An energy-efficient two-stage hybrid flow shop scheduling problem in a glass production. Int J Prod Res 58(8):2283–2314

    Article  Google Scholar 

  29. Liu M, Yang X, Zhang J, Chu C (2017) Scheduling a tempered glass manufacturing system: a three-stage hybrid flow shop model. Int J Prod Res 55(20):6084–6107

    Article  Google Scholar 

  30. Leon VJ, Ramamoorthy B (1997) An adaptive problemspace-based search method for flexible flow line scheduling. IIE Trans 29:115–125

    Google Scholar 

  31. Rahmani D, Ramezanian R (2016) A stable reactive approach in dynamic flexible flow shop scheduling with unexpected disruptions: a case study. Comput Ind Eng 98:360–372

    Article  Google Scholar 

  32. Riane F (1998) Scheduling hybrid flowshops: algorithms and applications. Ph.D. Thesis, Faculte’s Universitaires Catholiques de Mons

  33. Salvador MS (1973) A solution to a special class of flow shop scheduling problems. In: Elmaghraby SE (ed) Symposium on the Theory of Scheduling and Its Applications. Springer, Berlin, pp 83–91

    Chapter  Google Scholar 

  34. Quadt D, Kuhn H (2005) A conceptual framework for lotsizing and scheduling of flexible flow lines. Int J Prod Res 43(11):2291–2308

    Article  Google Scholar 

  35. Wittrock RJ (1988) An adaptive scheduling algorithm for flexible flow lines. Oper Res 36(4):445–453

    Article  Google Scholar 

  36. Shannon CE (1948) A mathematical theory of communication. Bell syst technic J 27(3):379–423

    Article  MathSciNet  Google Scholar 

  37. Calinescu A (2000) Complexity in manufacturing: an information theoretic approach. In Conference on complexity and complex systems in industry, 19–20 Sept 2000 (pp. 19–20). University of Warwick

  38. Chedid JA, Vidal GH (2012) Análisis del Problema de Planificación de la Producción en Cadenas de Suministro Colaborativas: Una Revisión de la Literatura en el Enfoque de Teoría de Juegos.

  39. Bozarth CC, Warsing DP, Flynn BB, Flynn EJ (2009) The impact of supply chain complexity on manufacturing plant performance. J Oper Manag 27(1):78–93

    Article  Google Scholar 

  40. MacDuffie JP, Sethuraman K, Fisher ML (1996) Product variety and manufacturing performance: evidence from the international automotive assembly plant study. Manage Sci 42(3):350–369

  41. Wu Y, Frizelle G, Efstathiou J (2007) A study on the cost of operational complexity in customer–supplier systems. Int J Prod Econ 106(1):217–229

    Article  Google Scholar 

  42. Sivadasan S, Efstathiou J, Calinescu A, Huatuco LH (2006) Advances on measuring the operational complexity of supplier–customer systems. Eur J Oper Res 171(1):208–226

  43. Orfi N, Terpenny J, Sahin-Sariisik A (2011) “Harnessing product complexity: Step 1 - establishing product complexity dimensions and indicators”. Eng Econ:9–79

  44. Coronado Hernández JR (2016) Análisis del efecto de algunos factores de complejidad e incertidumbre en el rendimiento de las Cadenas de Suministro. Propuesta de una herramienta de valoración basada en simulación (Doctoral dissertation)

  45. Efthymiou K, Pagoropoulos A, Papakostas N, Mourtzis D, Chryssolouris G (2014) Manufacturing systems complexity: An assessment of manufacturing performance indicators unpredictability. CIRP J Manuf Sci Technol 7(4):324–334

Download references

Acknowledgements

Thank you to the Fundación Universitaria Tecnológico Comfenalco (FUTC), Research Group Ciptec, Universidad de la Costa (CUC), Colombia, and to the Universidad Nacional Lomas de Zamora (UNLZ), Argentina, for the support of their academic and scientific group.

Author information

Authors and Affiliations

  1. Industrial Engineering Department, Fundacion Universitaria Tecnológico Comfenalco, Grupo de Investigación Ciptec, Cartagena, Colombia

    Germán Herrera Vidal

  2. Universidad Nacional Lomas de Zamora, Lomas de Zamora, Argentina

    Germán Herrera Vidal

  3. Industrial Engineering Department, Universidad de La Costa, Barraquilla, Colombia

    Jairo R. Coronado Hernández

  4. Faculty of Engineering, Universidad Nacional Lomas de Zamora, Lomas de Zamora, Argentina

    Claudia Minnaard

Authors
  1. Germán Herrera Vidal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jairo R. Coronado Hernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Claudia Minnaard
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Germán Herrera Vidal.

Ethics declarations

Ethics approval

The authors hereby state that the present work is in compliance with the ethical standards.

Consent to participate

Not applicable.

Consent for publication

The manuscript has not been published before and is not being considered for publication elsewhere.

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

Vidal, G.H., Hernández, J.R.C. & Minnaard, C. Modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments. Int J Adv Manuf Technol 118, 3049–3058 (2022). https://doi.org/10.1007/s00170-021-08028-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-021-08028-9

Keywords

Search

Navigation