Skip to main content
Log in

Dynamic control rules for conveyor intersections in a truck-assembly paint shop

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

Abstract

The truck plant of General Motors of Canada produces 60 trucks/hour, 2 shifts/day, in a highly automated factory employing 3,200 people. Flow in the paint shop is prone to bottlenecks when there is a large ratio of two- to monotone jobs. (Two-tone jobs require a second pass through the paint shop.) The shop has four major control points (two conveyor crossovers and two significant conveyor intersections), where decisions for routing jobs can greatly impact paint-shop performance. A simulation model is developed and refined to determine a set of “best” rules for those control points, dynamic rules reflecting input variables, and current system status. The paint-shop simulation model is employed to study overall throughput and time in the system for various control rules, as functions of product mix and the rates of automotive processing and repair. Conclusions are drawn regarding the influence of those rules on the conveyor crossovers and intersections.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Agnetis A, Pacifi A, Rossi F (1997) Scheduling of flexible flow lines in an automobile assembly plant. Eur J Oper Res 97(2):348–362

    Article  MATH  Google Scholar 

  2. AlDurgham MM, Barghash MA (2008) A generalised framework for simulation-based decision support for manufacturing. Prod Plan & Control 19(5):518–534

    Article  Google Scholar 

  3. Álvarez-Caldas C, Quesada A, Gauchía A, San Román JL (2009) Expert system for simulation of metal sheet stamping. Eng Comput 25(4):405–410

    Article  Google Scholar 

  4. Banks J, Carson II JS, Nelson BL and Nicol DM (2010) Discrete-event system simulation, 5th edn. Prentice-Hall, Upper Saddle River

  5. Bock S, Rosenberg O, van Brackel T (2006) Controlling mixed-model assembly lines in real time by using distributed systems. Eur J Oper Res 168(3):880–904

    Article  MATH  Google Scholar 

  6. Bookbinder JH, Kirk MD (1997) Lane selection in an AGV-based asynchronous parallel assembly line. Comput Ind Eng 32(4):927–938

    Article  Google Scholar 

  7. Boysen N, Fliedner M, Scholl A (2009) Production planning of mixed-model assembly lines: overview and extensions. Prod Plan & Control 20(5):455–471

    Article  Google Scholar 

  8. Boysen N, Scholl A, Wopperer N (2012) Resequencing of mixed-model assembly lines: survey and research agenda. Eur J Oper Res 216(3):594–604

    Article  Google Scholar 

  9. Ching SN, Meerkov SM, Zhang L (2008) Assembly systems with non-exponential machines: throughput and bottlenecks. Nonlinear Anal 69(3):911–917

    Article  MATH  MathSciNet  Google Scholar 

  10. Duplaga EA, Hahn CK, Hur D (1996) Mixed-model assembly line sequencing at Hyundai Motor Company. Production & Inventory Management 37(3):20–25

  11. Fisher ML, Ittner CD (1999) The impact of product variety on automobile assembly operations: empirical evidence and simulation analysis. Manag Sci 45(6):771–786

    Article  Google Scholar 

  12. Gnoni MG, Iavagnilio R, Mossa G, Mummolo G, Dileva A (2003) Production planning of a multi-site manufacturing system by hybrid modeling: a case study from the automotive industry. Int J Prod Econ 85(2):251–262

    Article  Google Scholar 

  13. Han Y-H, Zhou C, Bras B, McGinnis L, Carmichael C, Newcomb PJ (2003) Paint line color change reduction in automobile assembly through simulation. In: Chick S et al. (eds) Proceedings of the 2003 Winter Simulation Conference, pp 1204–1209

  14. Jahangirian M, Eldabi T, Naseer A, Stergioulas LK, Young T (2010) Simulation in manufacturing and business: a review. Eur J Oper Res 203(1):1–13

    Article  Google Scholar 

  15. Jin M, Luo Y, Eksioglu SD (2008) Integration of production sequencing and outbound logistics in the automotive industry. Int J Prod Econ 113(2):766–774

    Article  Google Scholar 

  16. Joly A, Frein Y (2008) Heuristics for an industrial car sequencing problem considering paint and assembly shop objectives. Comput Ind Eng 55(2):295–310

    Article  Google Scholar 

  17. Law AM (2014) Simulation modeling and analysis, 5th edn. McGraw-Hill, New York

  18. Li J (2004) Throughput analysis in automotive paint shops: a case study. IEEE Trans Autom Sci Eng 1(1):90–98

    Article  Google Scholar 

  19. Li J, Blumenfeld DE, Marin SP (2008) Production system design for quality robustness. IIE Trans 40(3):162–176

    Article  Google Scholar 

  20. Lou HH, Huang YL (2003) Hierarchical decision making for proactive quality control: system development for defect reduction in automotive coating operations. Eng Appl Artif Intell 16(3):237–250

    Article  Google Scholar 

  21. Masmoudi W, Chtourou H, Maalej AY (2007) Labor and machine sizing through a simulation-expert-system-based approach. Simul Model Pract Theory 15(1):98–110

    Article  Google Scholar 

  22. Muhl E, Charpentier P, Chaxel F (2003) Optimization of physical flows in an automotive manufacturing plant: some experiments and issues. Eng Appl Artif Intell 16(4):293–305

    Article  Google Scholar 

  23. Ockerman DH, Goldsman D (1999) Student t-tests and compound tests to detect transients in simulated time series. Eur J Oper Res 116(3):681–691

    Article  MATH  Google Scholar 

  24. Park YH, Matson JE and Miller DM (1998) Simulation and analysis of the Mercedes-Benz all activity vehicle (AAV) production facility, Proceedings of the 1998 Winter Simulation Conference, pp 921–926

  25. Resano Lázaro A, Luis Pérez J (2009) Dynamic analysis of an automobile assembly line considering starving and blocking. Robot Comput Integr Manuf 25(2):271–279

    Article  Google Scholar 

  26. Ribeiro CC, Aloise D, Noronha TF, Rocha C, Urrutia S (2008) A hybrid heuristic for a multi-objective real-life car sequencing problem with painting and assembly line constraints. Eur J Oper Res 191(3):981–992

    Article  MATH  MathSciNet  Google Scholar 

  27. Sargent RG (2013) Verification and validation of simulation models. J Simul 7(1):12–24

    Article  MathSciNet  Google Scholar 

  28. van der Zee DJ (2006) Modeling decision making and control in manufacturing simulation. Int J Prod Econ 100(1):155–167

    Article  Google Scholar 

  29. Yalcin A, Namballa RK (2005) An object-oriented simulation framework for real-time control of automated flexible manufacturing systems. Comput Ind Eng 48(1):111–127

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to James H. Bookbinder.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bookbinder, J.H. Dynamic control rules for conveyor intersections in a truck-assembly paint shop. Int J Adv Manuf Technol 76, 1515–1527 (2015). https://doi.org/10.1007/s00170-014-6235-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-014-6235-3

Keywords

Navigation