Modelling and simulation: Exploring dynamic system behaviour

Modelling and Simulation: Exploring Dynamic System Behaviour

Volumn , Issue , 2007, Pages 1-454

  Birta, Louis G ^a   Arbez, Gilbert ^a  

^a UNIVERSITY OF OTTAWA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84892228330     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-1-84628-622-3     Document Type: Book

References (112)

1. Bowden, R., (1998), The spectrum of simulation software, IEEE Solutions, 30(May): 44-54.
2. Endoscopy simulators for training and assessment skills, (2006), J. Cohen (Ed.), Gastrointestinal Endoscopy Clinics of North America, 16(3): 389-610 (see also: www.theclinics.com).
3. Modeling and Simulation Resource Directory, (2005), Modeling and Simulation, 4(1) (see also www.scs.org).
4. Nance, R.E. and Sargent, R.G., (2002), Perspectives on the evolution of simulation, Operations Research, 50: 161-172.
5. Nance, R.E., (1993), A history of discrete event simulation programming languages, ACM SIGPLANNotices, 28(3):149-175.
6. Nance, R.E., (1995), Simulation programming languages: An abridged history, in Proceedings of the 1995 Winter Simulation Conference, Arlington, VA, pp. 1307-1313.
7. Oren, T.I. and Zeigler, B.P., (1979), Concepts for advanced simulation methodologies, Simulation, 32: 69-82.
8. Oren, T.I, Elzas, M.S., Smit, I. and Birta, L.G., (2002), A code of professional ethics for simulationists, in Proceedings of the 2002 Summer Computer Simulation Conference, San Diego, pp. 434-1-35.
9. Nikoukaran, J., Hlupic, V., and Paul, R.J., (1999), A hierarchical framework for evaluating simulation software, Simulation Practice and Theory, 7: 219-231.
10. Swain, J.J., (2005), Seventh biennial survey of discrete event simulation software tools, OR/MS Today, 32.
11. Wolffe, G., Yurcik, W., Osborne, H., and Holloday, M., (2002), Teaching computer organization/architecture with limited resources using simulators, in Proceedings of the 33rd Technical Symposium on Computer Science Education, ACM Press, New York.
12. Yurcik, W., (2002), Computer architecture simulators are not as good as the real thing - They are better!, ACM Journal of Educational Resources in Computing, 1(4) (guest editorial, Special Issue on General Computer Architecture Simulators).
13. Zeigler, B.P., Praehofer, H., and Kim, T.G., (2000), Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems (2nd edn.), Academic Press, San Diego.
14. Balci, 0.,(1994), Validation, verification, and testing techniques throughout the life cycle of a simulation study, Annals of Operations Research, 53: 121-173.
15. Balci, O., (2001), A methodology for certification of modeling and simulation applications, ACM Transactions on Modeling and Computer Simulation, 11: 352-377.
16. Birta, L.G. and Ozmizrak, N.F., (1996), A knowledge-based approach for the validation of simulation models: The foundation, ACM Transactions on Modeling and Computer Simulation, 6: 67-98.
17. Boehm, B.W., (1979), Software engineering: R&D trends and defence needs, in: P. Wegner (Ed.), Research Directions in Software Technology, MIT Press, Cambridge, MA.
18. Cellier, F.E., (1986), Combined discrete/continuous system simulation - aplication, techniques and tools, in Proceedings of the 1986 Winter Simulation Conference. Department of Defense (DoD) Recommended Practices Guide (RPG) for Modeling and Simulation VV&A, Millennium Edition (available at http://vva.dmso.mil).
19. General Accounting Office, (1976), Report to the Congress: Ways to improve management of federally funded computerized models, report LCD-75-111, U.S. General Accounting Office, Washington, DC.
20. General Accounting Office, (1979), Guidelines for model evaluation, report PAD-79-17, U.S. General Accounting Office, Washington, DC.
21. General Accounting Office, (1987), DOD simulations: Improved assessment procedures would increase the credibility of results, report GAO/PEMD-88-3, U.S. General Accounting Office, Washington, DC.
22. Oren, T.I., (1981), Concepts and criteria to access acceptability of simulation studies, Communications of the ACM, 24: 180-189.
23. Oren, T.I., (1971), GEST: General system theory implementor, a combined digital simulation language, PhD Dissertation, University of Arizona, Tucson.
24. Neelamkavil, F., (1987), Computer Simulation and Modeling, John Wiley and Sons, Chichester, UK.
25. Pace, D.K., (2003), Verification, validation and accreditation of simulation models, in M.S. Obaidat and G.I. Papadimitriou (Eds.), Applied System Simulation: Methodologies and Applications, Kluwer Academic, Boston.
26. Praehofer, H., (1991), System theoretic formalisms for combined discrete continuous system simulation, International Journal of General Systems, 19: 219-240.
27. Robinson, S., (2006), Issues in conceptual modelling for simulation: Setting a research agenda, in Proceedings of 2006 Operations Research Society Simulation Workshop, March, Lexington, England.
28. Shannon, R.E., (1975), Systems Simulation: The Art and Science, Prentice- Hall, Englewood Cliffs, NJ.
29. Balci O., (1998), Validation, verification and testing, in The Handbook of Simulation, Chapter 10, John Wiley & Sons, New York, 335-393.
30. Banks, J., Carson II, J.S., Nelson, B.L., and Nicol, D.M., (2005), Discrete-Event System Simulation, 4th edn., Pearson Prentice Hall, Upper Saddle River, NJ.
31. Biller, B. and Nelson, B.L., (2002), Answers to the top ten input modeling questions, in Proceedings of the 2002 Winter Simulation Conference.
32. Choi S.C. and Wette R., (1969), Maximum likelihood estimation of the paramters of the gamma distribution and their bias, Technometrics, 11: 683-690.
33. Couture, R. and L'Ecuyer, P. (Eds.), (1998), Special Issue on Random Variate Generation, ACM Transactions on Modeling and Simulation, 8(1).
34. Coveyou, R.R., (1960), Serial correlation in the generation of psuedo-random numbers, Journal of the ACM, 7: 72-74.
35. Harrell, C., Ghosh, B.K., and Bowden, Jr., R.O., (2004), Simulation Using ProModel, 2nd ed., McGraw-Hill, New York.
36. Hull, T.E. and Dobell, A.R., (1962), Random number generators, SIAM Review, 4: 230-254.
37. Kelton, D.W., Sadowski, R.P., and Sturrock, D.T., (2004), Simulation with Arena, 3rd ed., McGraw-Hill, New York.
38. Kleijnen, J.P.C., (1987), Statistical Tools for Simulation Practitioners, Marcel Dekker, New York.
39. Knuth, D.E., (1998), The Art of Computer Programming, Vol. 2: Seminumerical Algorithms, 3rd ed. , Addison-Wesley, Reading, MA.
40. Law A.M. and McComas, M.G., (1997), Expertfit: Total support for simulation input modeling, in Proceedings of the 1997 Winter Simulation Conference, pp. 668-673.
41. Law, A.M. and Kelton, D.W., (2000), Simulation Modeling and Analysis, 3rd ed., McGraw-Hill, New York.
42. Leemis, L.M. and Park, S.K., (2006), Discrete Event Simulation: A First Course, Pearson Prentice Hall, Upper Saddle River, NJ.
43. Lehmer, D.H., (1949, 1951), Mathematical methods in large scale computing units, in Proceedings of the Second Symposium on Large-Scale Digital Calculating Machinery, 1949 and Annals Comput. Lab, 1951, vol. 26, pp. 141-146, Harvard University Press, Cambridge, MA.
44. Ross, S.M., (1990), A Course in Simulation, Macmillan, New York.
45. Rotenberg, A., (1960), A new pseudo-random number generator, Journal of the ACM, 7: 75-77.
46. Gershwin, S.B., (1991) Hierarchical flow control: A framework for scheduling and planning discrete events in manufacturing, in Y.-C. Ho (Ed.), Discrete Event Dynamic Systems, IEEE Press, Piscataway, NJ.
47. Kreutzer, W., (1986), System Simulation: Programming Styles and Languages, Addison-Wesley, Sydney-Wokingham, UK.
48. Martinez, J.C., (2001), EZStrobe: General-purpose simulation system based on activity cycle diagrams, in Proceedings of the 2001 Winter Simulation Conference, pp. 1556-1564.
49. Peterson, J.T., (1981), Petri Net Theory and the Modeling of Systems, Prentice-Hall, Englewood Cliffs, NJ.
50. Shi, J.J., (2000), Object-oriented technology for enhancing activity-based modelling functionality, in J.A. Joines, R.R. Barton, K. Kang, and P.A. Fishwick (Eds.), Proceedings of the 2000 Winter Simulation Conference, Orlando, FL, December 10-13, 2000, pp. 1938-1944.
51. Tadao, M., (1989), Petri nets: Properties, analysis and applications, Proceedings of IEEE, 77: 541-580.
52. Zeigler, B.P., (1976), Theory of Modeling and Simulation, Wiley Interscience, New York.
53. Zeigler, B.P., Praehofer, H., and Kim, T.G., (2000), Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems, Academic Press, San Diego.
54. Banks, J., Carson II, J.S., Nelson, B.L., and Nicol, D.M., (2005), Discrete-Event System Simulation, 4th edn., Prentice-Hall, Upper Saddle River, NJ.
55. Horatmann, C., (2003), Computing Concepts with Java Essentials, 3rd edn., John Wiley and Sons, New York.
56. Liang, Y.D., (2007), Introduction to Java Programming: Fundamentals First, 6th edn., Prentice-Hall, Upper Saddle River, NJ.
57. CERN, The Colt Project, (2004), Version 1.2.0, http://dsd.lbl.gov/ ~hoschek/-colt/.
58. Sun Microsystems, Java 2 Platform Standard Edition 5.0, TUhttp://java.sun. com/j2se/1.5.0/docs/index.htmlUT.
59. Banks J., Carson II, J.S., Nelson, B.L., and Nicol, D.M., (2005), Discrete-Event System Simulation, 4th edn., Pearson Prentice Hall, Upper Saddle River, NJ.
60. Goldsman, D. and Nelson, B.L., (1998), Comparing Systems Via Simulation, in J. Banks (Ed.), Handbook of Simulation, John Wiley, New York, pp. 273-306.
61. Goldsman, D. and Nelson, B.L., (2001), Statistical selection of the best system, in B.A. Peters, J.S. Smith, D.J. Medeiros, and M.W. Rohrer, (Eds.), Proceedings of the 2001 Winter Simulation Conference IEEE Press, Piscataway, NJ, pp. 139-146.
62. Goldsman, D., Schruben, L.W., and Swain J.J., (1994), Test for transient means in simulation time series, Naval Research Logistics Quarterly, 41: 171-187.
63. Law, A.M. and Kelton, D.W., (2000), Simulation Modeling and Analysis, 3rd edn., McGraw-Hill, New York.
64. Robinson S., (2002), A statistical process control approach for estimating the warm-up period, in Proceedings of the 2002 Winter Simulation Conference, IEEE Piscataway, NJ, pp. 439-446
65. Roth, E., (1994), The relaxation time heuristic for the initial transient problem in M/M/K queuing systems, European Journal of Operational Research, 72: 376-386.
66. Welch, P., (1983), The statistical analysis of simulation results, in S. Lavenberg (Ed.), The Computer Performance Modeling Handbook, Academic Press, New York, pp. 268-328.
67. Brauer, F. and Soudack, A.C., (1979), Stability regions and transition phenomena for harvested predator-prey systems, Journal of Mathematical Biology, 8: 55-71.
68. Hall, C.A.S. and Day, J.W., (1977). Ecosystem Modelling in Theory and Practice, John Wiley, New York.
69. Iserles, A., (1996), A First Course in the Numerical Analysis of Differential Equations, Cambridge University Press, Cambridge, UK.
70. Lambert, J.D., (1991), Numerical Methods for Ordinary Differential Equations, Wiley, London.
71. Birta, L.G., Oren, T.I., and Kettenis, D.L., (1985), A robust procedure for discontinuity handling in continuous system simulation, Transactions of the Society for Computer Simulation, 2: 189-205.
72. Cellier, F.E. and Kofman, E., (2006), Continuous System Simulation, Springer-Verlag, New York.
73. Ellison, D., (1981), Efficient automatic integration of ordinary differential equations with discontinuities, Mathematics and Computation in Simulation, 23: 12-20.
74. Elmquist, H., (2004), Dymola - Dynamic modeling language, user's manual, Version 5.3, DynaSim AB, Research Park Ideon, Lund, Sweden.
75. Gear, C.W., (1971), Numerical Initial Value Problems in Ordinary Differential Equations, Prentice-Hall, Englewood Cliffs, NJ.
76. Gear, C.W. and Osterby, O., (1984), Solving ordinary differential equations with discontinuities, ACM Transactions on Mathematical Software, 10: 23-44.
77. Hairer, E. and Wanner, G., (1996), Solving Ordinary Differential Equations II: Stiff and Differential-Algebraic Problems, 2nd edn., Springer-Verlag, Berlin.
78. Iserles, A., (1996), A First Course in the Numerical Analysis of Differential Equations, Cambridge University Press, Cambridge, UK.
79. Kincaid, D. and Cheng, W., (2002), Numerical Analysis: Mathematics of Scientific Computing, 3rd edn., Brooks/Cole, Pacific Grove, CA.
80. Kofman, E. and Junco, S., (2001), Quantized state systems: A DEVS approach for continuous system simulation, Transactions of the SCS, 18(3): 123-132.
81. Korn, G.A., (1998), Interactive Dynamic-system Simulation Under Windows 95 and NT, Gordon Breach, London.
82. Lambert, J.D., (1991), Numerical Methods for Ordinary Differential Equations, Wiley, London.
83. Shampine, L.F., Gladwell, I., and Brankin, R.W., (1991), Reliable solutions of special event location problems for ODEs, ACM Transactions on Mathematical Software, 17: 11-25.
84. Watts, H.A., (1984), Step-size control in ordinary differential equation solvers, Transactions of the Society for Computer Simulation, 1: 15-25.
85. Beale, E.M.L., (1972), A derivation of conjugate gradients, in F.A. Lottsma (Ed.), Numerical Methods for Non-Linear Optimization, Academic Press, London, pp. 39-13.
86. Al-Baali, (1985), Descent property and global convergence of the Fletcher-Reeves method with inexact line search, IMA Journal on Numerical Analysis, 5: 121-124.
87. Bertsekas, D.P., (1996), Constrained Optimization and Lagrange Multiplier Methods, Athena Scientific, Nashua, NH.
88. Bhatnager, S. and Kowshik, H.J., (2005), A discrete parameter stochastic approximation algorithm for simulation optimization, Simulation, 81(11).
89. Bonnans, J.F., Gilbert, J.C., Lemarechal, C., and Sagastizabal, C.A., (2003), Numerical Optimization: Theoretical and Practical Aspects, Springer-Verlag, Berlin.
90. Cormen, T.H., Leisserson, C.E., and Rivest, R.L., (1990), Introduction to Algorithms, MIT Press, Cambridge, MA.
91. Fletcher, R. and Reeves, C.M., (1964), Function minimization by conjugate gradients, Computer Journal, 7: 149-154.
92. Fletcher, R., (1987), Practical Methods of Optimization, 2nd edn., John Wiley, New York.
93. Gilbert, J. and Nocedal, J., (1992), Global convergence properties of conjugate gradient methods for optimization, SIAM Journal on Optimization, 2: 21-12.
94. Heath, M.T., (2000), Scientific Computing, An Introductory Survey, 2nd edn., McGraw-Hill, New York.
95. Lagarias, J.C., Reeds, J.A., Wright, M.H., and Wright, P.E., (1998), Convergence properties of the Nelder-Mead simplex method in low dimensions, SIAM Journal of Optimization, 9: 112-147.
96. Law, A.M. and Kelton, D.W., (2000), Simulation Modeling and Analysis, (3rd edn.), McGraw-Hill, New York.
97. Lewis, F.L. and Syrmos, V.L. (1995), Optimal Control, 2nd edn., John Wiley and Sons, New York.
98. Nelder, J. and Mead, R., (1965), A simplex method for function minimization, Computer Journal,, 7: 308-313.
99. Nocedal, J. and Wright, S.J., (1999), Numerical Optimization, Springer-Verlag, New York.
100. Olafason, S. and Kim, J. (2002), Simulation optimization, in Proceedings of the 2002 Winter Simulation Conference, pp. 79-84.
101. Oretega, J.M. and Rheinboldt, W.C., (1970), Iterative Solution of Nonlinear Equations in Several Variables, Academic Press, New York.
102. Pedregal, P., (2004), Introduction to Optimization, Springer-Verlag, New York.
103. Pinter, Janos D., Linder, David and Chin, Paulina (2006), Global Optimization toolbox for maple: an introduction with illustrative applications, Optimization Methods and Software., vol 21(4), pp. 565-582.
104. Polack, E. and Ribiere, G., (1969), Note sur la Convergence de Methodes de Directions Conjuguees, Revue Frangaise d'Informatique et de Recherche Operationnelle, 16: 35-43.
105. Powell, M.J.D., (1978), Restart procedures for the conjugate gradient method, Mathematical Progress, 12: 241-254.
106. Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P., (1999), Numerical Recipes in C, The Art of Scientific Computing, 2nd edn., Cambridge University Press, Cambridge, UK.
107. Rubinstein, R. and Shapiro, A., (1993), Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method, John Wiley, New York.
108. Rykov, A., (1983), Simplex algorithms for unconstrained optimization, Problems of Control and Information Theory, 12: 195-208.
109. Sorenson, H.W., (1969), Comparison of Some Conjugate Directions Procedures for Function Minimization, J Franklin Institute, vol. 288, pp. 421-441.
110. Seierstad, A. and Sydstaeter, K., (1987), Optimal Control Theory with Economic Applications, North Holland, Amsterdam.
111. Wolfe, P., (1969), Convergence conditions for ascent methods, SIAM Review, 11: 226-235.
112. Zabinsky, Z.B., (2003), Stochastic Adaptive Search for Global Optimization, Kluwer Academic, Dordrecht.