50th Anniversary Article: Stochastic Simulation Research in Management Science

Published Online:https://doi.org/10.1287/mnsc.1040.0246

References

  • Andradóttir S. Optimization of the transient and steady-state behavior of discrete event systems. Management Sci. (1996) 42:717–737LinkGoogle Scholar
  • Burt J. M., Garman M. B. Conditional Monte Carlo: A simulation technique for stochastic network analysis. Management Sci. (1971) 18:207–217LinkGoogle Scholar
  • Conway R. W. Some tactical problems in digital simulation. Management Sci. (1963) 10:47–61LinkGoogle Scholar
  • Conway R. W., Johnson B. M., Maxwell M. L. Some problems of digital simulation. Management Sci. (1959) 6:92–110LinkGoogle Scholar
  • Fishman G. S.Newsletter of the TIMS College on Simulation and Gaming (1980) 4(3):4–5Google Scholar
  • Fishman G. S., Kiviat P. J. The analysis of simulation-generated time series. Management Sci. (1967) 13:525–557LinkGoogle Scholar
  • Fox B. L., Glynn P. W. Discrete-time conversion for simulating semi-Markov processes. Oper. Res. Lett. (1986) 5:191–196CrossrefGoogle Scholar
  • Fu M. C., Gass S., Harris C. Perturbation analysis. Encyclopedia of Operations Research and Management Science (2001) 2nd ed.(Kluwer Academic Publishers)608–611CrossrefGoogle Scholar
  • Fu M. C. Optimization for simulation: Theory vs. practice. INFORMS J. Comput. (2002) 14:192–215LinkGoogle Scholar
  • Gafarian A. V., Ancker C. J., Morisaku T. Evaluation of commonly used rules for detecting steady-state in computer-simulation. Naval Res. Logist. (1978) 25:511–529CrossrefGoogle Scholar
  • Glasserman P., Heidelberger P., Shahabuddin P. Variance reduction techniques for estimating value-at-risk. Management Sci. (2000) 46:1349–1364LinkGoogle Scholar
  • Glynn P. W., Iglehart D. L. Importance sampling for stochastic simulations. Management Sci. (1989) 35:1367–1392LinkGoogle Scholar
  • Glynn P. W., Iglehart D. L. Conditions for the applicability of the regenerative method. Management Sci. (1993) 39:1108–1111LinkGoogle Scholar
  • Goldsman D., Meketon M., Schruben L. Properties of standardized time series weighted area variance estimators. Management Sci. (1990) 36:602–612LinkGoogle Scholar
  • Goyal A., Shahabuddin P., Heidelberger P., Nicola V. F., Glynn P. W. A unified framework for simulating Markovian models of highly dependable systems. IEEE Trans. Comput. (1992) 41:36–51CrossrefGoogle Scholar
  • Heidelberger P., Cao X., Zazanis M. A., Suri R. Convergence properties of infinitesimal perturbation analysis estimates. Management Sci. (1988) 34:1281–1302LinkGoogle Scholar
  • Henderson S. G., Joines J. A., Barton R. R., Kang K., Fishwick P. A. Mathematics for simulation. Proc. 2000 Winter Simulation Conf. (2000) (IEEE, Piscataway, NJ) 137–146CrossrefGoogle Scholar
  • Hopp W. Fifty years of Management Science.. Management Sci. (2004) 50:1–7LinkGoogle Scholar
  • Hordijk A., Iglehart D. L., Schassberger R. Discrete-time methods for simulating continuous time Markov chains. Adv. Appl. Probab. (1976) 8:772–778CrossrefGoogle Scholar
  • Iglehart D. L., Lewis P. A. W. Regenerative simulation with internal controls. J. ACM (1979) 26:271–282CrossrefGoogle Scholar
  • Kleijnen J. P. C. Analyzing simulation experiments with common random numbers. Management Sci. (1988) 34:65–74LinkGoogle Scholar
  • Lavenberg S. S., Welch P. D. A perspective on the use of control variables to increase the efficiency of Monte Carlo simulations. Management Sci. (1981) 27:322–335LinkGoogle Scholar
  • Lavenberg S. S., Moeller T. L., Sauer C. H. Concomitant control variables applied to the regenerative simulation of queueing systems. Oper. Res. (1979) 27:134–160LinkGoogle Scholar
  • Lavenberg S. S., Moeller T. L., Welch P. D. Statistical results on control variables with application to queueing network simulation. Oper. Res. (1982) 30:182–202LinkGoogle Scholar
  • Law A. M., Kelton W. D. Confidence interval procedures for steady-state simulations, II: A survey of sequential procedures. Management Sci. (1982) 28:550–562LinkGoogle Scholar
  • L'Ecuyer P. A unified view of the IPA SF, and LR gradient estimation techniques. Management Sci. (1990) 36:1364–1383LinkGoogle Scholar
  • Meketon M. S., Heidelberger P. A renewal theoretic approach to bias reduction in regenerative simulations. Management Sci. (1982) 28:173–181LinkGoogle Scholar
  • Nance R. E., Sargent R. G. Perspectives on the evolution of simulation. Oper. Res. (2002) 50:161–172LinkGoogle Scholar
  • Naylor T. H., Finger J. M. Verification of computer simulation models. Management Sci. (1967) 14:B92–B101LinkGoogle Scholar
  • Nelson B. L., Matejcik F. J. Using common random numbers for indifference-zone selection and multiple comparisons in simulation. Management Sci. (1995) 41:1935–1945LinkGoogle Scholar
  • Newsletter of the TIMS College on Simulation and Gaming (1980) 4(3):3–4Google Scholar
  • Newsletter of the TIMS College on Simulation and Gaming (1985) 9(2):3–4Google Scholar
  • Sargent R. G. Event graph modelling for simulation with an application to flexible manufacturing systems. Management Sci. (1988) 34:1231–1251LinkGoogle Scholar
  • Schmeiser B. Batch size effects in the analysis of simulation output. Oper. Res. (1982) 30:556–568LinkGoogle Scholar
  • Schmeiser B., Yeh Y. On choosing a single criterion for confidence-interval procedures. Proc. 2002 Winter Simulation Conf. (2002) (IEEE, Piscataway, NJ) 345–352CrossrefGoogle Scholar
  • Schruben L. W. A coverage function for interval estimators of simulation response. Management Sci. (1980) 26:18–27LinkGoogle Scholar
  • Shahabuddin P. Importance sampling for the simulation of highly reliable Markovian systems. Management Sci. (1994) 40:333–352LinkGoogle Scholar
  • Steiger N. M., Wilson J. R. An improved batch means procedure for simulation output analysis. Management Sci. (2002) 48:1569–1586LinkGoogle Scholar
  • Suri R., Zazanis M. A. Perturbation analysis gives strongly consistent sensitivity estimates for the M/G/1 queue. Management Sci. (1988) 34:39–64LinkGoogle Scholar
  • Van Horn R. L. Validation of simulation results. Management Sci. (1971) 17:247–258LinkGoogle Scholar
  • Venkatraman S., Wilson J. R. The efficiency of control variates in multiresponse simulation. Oper. Res. Lett. (1986) 5:37–42CrossrefGoogle Scholar
  • Whitt W. Planning queueing simulations. Management Sci. (1989) 35:1341–1366LinkGoogle Scholar
  • Wilson J. R., Pritsker A. A. B. Variance reduction in queueing simulation using generalized concomitant variables. J. Statist. Comput. Simulation (1984a) 19:129–153CrossrefGoogle Scholar
  • Wilson J. R., Pritsker A. A. B. Experimental evaluation of variance reduction techniques for queueing simulation using generalized concomitant variables. Management Sci. (1984b) 30:1459–1472LinkGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.