Abstract
Simulation has been frequently used in manufacturing because it allows alternative designs and control policies to be tried out on the model during the preparatory phase of the physical plant. It helps to reduce cost and risk of large scale errors. Simulation approaches are also used during the operational phase of the manufacturing plants to find better ways to operate, and these studies may be one point in time exercises or may be part of a periodic check on the running of the system [1].
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pidd M, (2003) Tools for thinking. Modelling in management science. 2nd. Edition. Chichester. England: Wiley.
Ignizio JP, (2004) Optimal maintenance headcount allocation: An application of Chebyshev goal programming. International Journal of Production Research, 42(1): 201–210.
Crespo Márquez A, Sánchez Herguedas A, (2002) Models for maintenance optimisation: A study for repairable systems and finite time periods. Reliability Engineering and System Safety, 75(3). 367–377.
Gerstbakh IB, (1976) Sufficient optimality conditions for control-limit policy in a semi-Markov model. Journal Of Applied Probability, 13: 400–406.
Gerstbakh IB, (1977) Models of preventive maintenance. New York: North-Holland.
Gerstbakh IB, (2000) Reliability theory. With applications to preventive maintenance. Berlin: Springer-Verlag.
Papazoglou IA, (2000) Semi-Markovian reliability models for systems with testable components and general test/outage times. Reliability Engineering and System Safety, 47: 175–185.
Becker G, Camarinopoulos L and Zioutas G, (1999) A Semi-Markovian model allowing for inhomogenities with respect to process time. Reliability Engineering and System Safety, 70(1): 41–48.
Abboud NE, (2001) A Discrete-time Markov production-inventory model with machine breakdowns. Computers and Industrial Engineering, 39: 95–107.
Campbell JD, Jardine AKS, (2001) Maintenance excellence: Optimizing equipment life-cycle decisions. New York: Marcel Dekker.
Dekker R and Groenendijk W, (1995) Availability Assessment Methods and their application in Practice. Microelectron Reliability, 35(9–10): 1257–1274.
Scarf PA, (1997) On the application of mathematical models in maintenance. European Journal of Operational Research, 99: 493–506.
Bellman R, (1957) Dynamic Programming. Princeton. New Jersey: Princeton University Press.
Howard RA, (1960) Markov processes and dynamic programming. New York: Technology Press and Wiley Press.
Yao X, Fernandez-Gaucherand E, Fu MC and Marcus SI, (2004) Optimal preventive maintenance scheduling in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 17 (3): 345–356.
Hoyland A, Rausand M, (2004) System reliability theory. Models, statistical methods and applications. Wiley Series in probability and Statistics. New Jersey. Hoboken: John Wiley & Sons Inc.
Marseguerra M, Zio E, (2000) Optimizing maintenance and repair policies via combination of genetic algorithms and Monte Carlo Simulation. Reliability Engineering and System Safety, 68: 69–83.
Hopp WJ, Spearman ML, (1996) Factory physics. Foundations of manufacturing management. Chicago: IRWIN.
Hopp WJ, Pati N, Jones PC, (1989) Optimal inventory control in a production flow system with failures. International Journal of Production Research, 27: 1367–1384.
Hsu LF, (1999) Simultaneous determination of preventive maintenance and replacement policies in a queue-like production system with minimal repair. Reliability Engineering and System Safety, 63(2): 161–167.
Liu B, Cao J, (1999) Analysis of a production-inventory system with machine breakdowns and shutdowns. Computers and Operations Research, 26: 73–91.
Simon JT, Hopp WJ, (1995) Throughput and average inventory in discrete balanced assembly systems. IIE Transactions, 267: 368–373.
Van Der Duyn Schouten FA, Vanneste SG, (1995) Maintenance optimisation of a production system with buffer capacity. European Journal of Operations Research, 82: 323–338.
Crespo Marquez A, Gupta JND, Sánchez Herguedas A (2003) Maintenance policies for a production system with constrained production rate and buffer capacity. International Journal of Production Research, 41 (9): 1909–1926.
Andijani A, Duffuaa S, (2002). Critical evaluation of simulation studies in maintenance systems. Production Planning and Control, 13(4): 336–341.
Marseguerra M, Zio E (2002) Basics of the Monte Carlo method with applications to system reliability. Hagen. Germany: LiLoLe-Verlag GmbH.
Vensim, Version 5.4. (2004) Ventana Systems inc. Harvard. Massachusetts.
Rights and permissions
Copyright information
© 2010 Springer-Verlag London Limited
About this chapter
Cite this chapter
(2010). Modelling Back-end Issues in Manufacturing. In: Dynamic Modelling for Supply Chain Management. Springer, London. https://doi.org/10.1007/978-1-84882-681-6_10
Download citation
DOI: https://doi.org/10.1007/978-1-84882-681-6_10
Publisher Name: Springer, London
Print ISBN: 978-1-84882-680-9
Online ISBN: 978-1-84882-681-6
eBook Packages: EngineeringEngineering (R0)