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Simulating breakdowns: a taxonomy for modelling

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Journal of Simulation

Abstract

This article provides a classification system, that is, taxonomy for modelling breakdowns for discrete-event simulation. The taxonomy has four parameters; the mode (single or multimodal), delay time (whether the breakdown is instantaneous or delayed), load condition (what happens to the current unit of production), and the basis for time to failure (elapsed time, usage time, or cycle count). Four examples are given to show how the taxonomy is applied. As a possible fifth parameter, starvation, blocking, and jamming are described. Although the context is manufacturing, other types of systems could follow the taxonomy.

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References

  • Abogrean EM (2012). Stochastic simulation of machine breakdown. Journal of Public Administration & Governance 2 (1): 95–105.

    Article  Google Scholar 

  • Banks J and Manivannan S (1992). Design of a knowledge based on-line simulation system to control a manufacturing shop floor. IIE Transactions on Design and Manufacturing 1 (1): 72–83.

    Google Scholar 

  • Banks J, Carson II JS, Nelson BL and Nicol DM (2010). Discrete-event System Simulation. 5th edn. Prentice-Hall: Upper Saddle River, NJ, p 233.

    Google Scholar 

  • Brennan R and O W (2000). A simulation test-bed to evaluate multi-agent control of manufacturing system. In: Joines JA, Barton RR, Kang K and Fishiwick PA (eds). Proceedings of the 2000 Winter Simulation Conference. Orlando, FL, pp. 1747–1756, 10–13 December. Institute of Electrical and Electronics Engineers: Piscataway, New Jersey.

  • Chiu SW, Yang JC and Wu MF (2009). Optimal replenishment policy for manufacturing systems with failure in rework, backlogging and random breakdown. Mathematical and Computer Modelling of Dynamical Systems 15 (3): 255–274.

    Article  Google Scholar 

  • Chong CS, Sivakumar AI and Gay R (2003). Simulation-based scheduling for dynamic discrete manufacturing. In: Chick S, Sanches P J, Ferrin D, and Morrice D J (eds). Proceedings of the 2003 Winter Simulation Conference. New Orleans, LA, pp. 1465–1473, 7–10 December. Institute of Electrical and Electronics Engineers: Piscataway, New Jersey.

  • Dersin P and Valenzuela RC (2012). Application of non-Markovian stochastic petri nets to the modeling of rail system maintenance and availability. In: Laroque C, Himmelspach J, Pasupathy R, Rose O, and Uhrmacher A M (eds). Proceedings of the 2012 Winter Simulation Conference. Berlin, Germany, 8–12 December. Institute of Electrical and Electronics Engineers: Piscataway, New Jersey.

  • Dunnar F and Ladet P (2004). Consideration of machine breakdowns in the utilization of flexible production systems. International Journal of Computer Integrated Manufacturing 17 (1): 69–82.

    Article  Google Scholar 

  • Gören S and Sabuncuoglu I (2009). Optimization of schedule robustness and stability under random machine breakdowns and processing time variability. IIE Transactions 42(3): 203–220 Copyright C ‘IIE’.

  • Gupta SM (1995). The effect of machine breakdown on the performance of a JIT system. Proceedings of the 26th Annual Meeting of Decision Sciences Institute. Northeastern University: Boston, MA, pp 1135–1137.

  • Hasgül S and Büyüksünetçi AS (2005). Simulation modeling and analysis, of new mixed model production lines. In: Kuhl ME, Steiger NM, Armstrong FB, and Joines JA (eds). Proceedings of the 2005 Winter Simulation Conference. Orlando. FL, pp. 1408–1412, 4–7 December. Institute of Electrical and Electronics Engineers: Piscataway, New Jersey.

  • Higgins LR and Mobley RK (2001). Maintenance Engineering Handbook. 6th edn. McGraw-Hill: New York, p 1152.

    Google Scholar 

  • Jadhav PD and Smith JS (2005). Analyzing printed circuit board assembly lines using a PCB assembly template. In: Kuhl ME, Steiger NM, Armstrong FB, and Joines JA (eds). Proceedings of the 2005 Winter Simulation Conference. Orlando. FL, pp. 1335–1342, 4–7 December. Institute of Electrical and Electronics Engineers: Piscataway, New Jersey.

  • Johansson M et al (2007). A test implementation of the core manufacturing simulation data specification. In: Tew J, Barton R, Henderson S, Biller B, Hsieh M-H, Shortle J (eds). Proceedings of the 2007 Winter Simulation Conference. Washington DC, pp. 1673–1681, 9–12 December. Institute of Electrical and Electronics Engineers: Piscataway, New Jersey.

  • Kendall DG (1953). Stochastic processes occurring in the theory of queues and their analysis by the method of imbedded Markov chains. Annals of Mathematical Statistics 24 (3): 338–354.

    Article  Google Scholar 

  • Khazraei K and Desue J (2011). A strategic standpoint on maintenance taxonomy. Journal of Facilities Management 9 (2): 96–113.

    Article  Google Scholar 

  • Kibira D and Mclean C (2002). Virtual reality simulation of a mechanical assembly production line. In: Yücesan E, Chen C-H, Snowdon JL, and Charnes JM (eds). Proceedings of the 2002 Winter Simulation Conference. San Diego, CA, pp.1130–1137, 8–11 December. Institute of Electrical and Electronics Engineers: Piscataway, New Jersey.

  • Kim YD, Shim SO, Choi B and Hwang H (2003). Simplification methods for accelerating simulation-based real-time scheduling in a Semiconductor Wafer Fabrication Facility. IEEE Transactions on Semiconductor Manufacturing 16 (2): 290–298.

    Article  Google Scholar 

  • Ladbrook J (1998). Modeling breakdowns: An inquest. M Phil Thesis, University of Birmingham. Birmingham, UK.

  • Ladbrook J and Januszczak A (2001). Ford’s power train operations—Changing the simulation environment. In: Rohrer M, Medeiros D, Peters BA, and Smith J (eds). Proceedings of the 2001 Winter Simulation Conference. Arlington, VA: pp. 863–869, 9–12 December. Institute of Electrical and Electronics Engineers: Piscataway, New Jersey.

  • Law A (2006). ExpertFit Version 7 © User’s Guide, http://www.simprocess.net/docs/SP45/ExpertFitUsersGuide.pdf, accessed on 19 February 2013.

  • Liao CJ and Chen WJ (2004). Scheduling under machine breakdown in a continuous process industry. Computers & OR 31 (4): 415–428.

    Article  Google Scholar 

  • Lin CG and Kroll DE (2006). Economic lot sizing for an imperfect production system subject to random breakdowns. Engineering Optimization 38 (1): 73–92.

    Article  Google Scholar 

  • Moubray J (2000). Reliabity-Centred Maintenance. Aladon: Lutterworth, p 426.

    Google Scholar 

  • Nakajima S (1989). TPM Development Program: Implementing Total Productive Maintenance. Productivity: Cambridge, UK, p 403.

    Google Scholar 

  • Ohno T (1990). Toyota Production System beyond Large-Scale Production. Productivity Press: New York, p 143.

    Google Scholar 

  • Robinson S (2004). Simulation: The practice of Model Development and Use. John Wiley & Sons: Chichester, UK.

    Google Scholar 

  • Scholl W, Gan BP and Preuss P (2012). Multi-stage discrete event simulation approach for scheduling of maintenance activities in a semiconductor manufacturing line. In: Laroque C, Himmelspach J, Pasupathy R, Rose O, and Uhrmacher AM (eds). Proceedings of the 2012 Winter Simulation Conference. Berlin, Germany, 8–12 December. Institute of Electrical and Electronics Engineers: Piscataway, New Jersey.

  • Shin F, Ram B, Gupta A, Yu Z and Menassa R (2004). A decision tool for assembly line breakdown action. In: RG Ingalls, MD Rossetti, JS Smith, and BA Peters (eds). Proceedings of the 2004 Winter Simulation Conference. Washington DC, pp. 1122–1127, 5–8 December. Institute of Electrical and Electronics Engineers: Piscataway, New Jersey.

  • Sivakumar AI (1999). Optimization of cycle time and utilization in semiconductor test manufacturing using simulation-based, on-line, near-real-time scheduling system. In: Sturrock DT, Evans GW, Farrington PA, and Nembhard HB (eds). Proceedings of the 1999 Winter Simulation Conference. Phoenix, AZ, pp. 727–735, 5–8 December. Institute of Electrical and Electronics Engineers: Piscataway, New Jersey.

  • Turkcan A, Akturk MS and Storer RH (2009). Predictive/reactive scheduling with controllable processing, times and earliness-tardiness penalties. IIE Transactions 41(12): 1080–1095. Copyright C ‘IIE’.

  • Widyadan GA and Wee HM (2011). Optimal deteriorating items production inventory models with random machine breakdown and stochastic repair time. Applied Math. Modeling 35 (7): 4395–3508.

    Google Scholar 

Download references

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Authors and Affiliations

  1. Escola de Engenharia Maua, SP, Brazil

    L Chwif

  2. Georgia Tech, Georgia, USA

    J Banks

  3. Université du Québec à Trois-Rivières, Québec, Canada

    D R Vieira

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  1. L Chwif
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  2. J Banks
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  3. D R Vieira
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Correspondence to L Chwif.

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Chwif, L., Banks, J. & Vieira, D. Simulating breakdowns: a taxonomy for modelling. J Simulation 9, 43–53 (2015). https://doi.org/10.1057/jos.2014.18

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  • DOI: https://doi.org/10.1057/jos.2014.18

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