Literature review bibliography of simulation optimitation

Management Scientists and systems analysts often wish to find the values of input variables which optimize (maximize or minimize) some function of system performance. If the system can be described analytically, mathematical programming is used to find the optimum. When systems are too complicated to be described analytically, simulation is the appropriate tool for modelling systems. Therefore, methods of optimization through simulation are quite important to the management scientist. This paper consists of a discussion and a bibliography of the optimization of simulated systems.

[1]  G. S. Fishman,et al.  Concepts and Methods in Discrete Event Digital Simulation , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  Michael A. Crane,et al.  Simulating Stable Stochastic Systems: III. Regenerative Processes and Discrete-Event Simulations , 1975, Oper. Res..

[3]  Carl W. Nelson,et al.  A Search Procedure for Policy Oriented Simulations: Applications to Urban Dynamics , 1974 .

[4]  Michael A. Crane,et al.  Simulating Stable Stochastic Systems, II: Markov Chains , 1974, JACM.

[5]  William Ernest Biles,et al.  A gradient—regression search procedure for simulation experimentation , 1974, WSC '74.

[6]  David L. Eldredge.  Experimental optimization of statistical simulation , 1974, WSC '74.

[7]  Michael A. Crane,et al.  Simulating Stable Stochastic Systems, I: General Multiserver Queues , 1974, JACM.

[8]  H. Hartley,et al.  Confidence Regions for Global Optima in Nonlinear Programming. , 1973 .

[9]  Dennis E. Smith,et al.  Research on an OPTIMIZER Computer Program for Use in Simulation Studies. , 1973 .

[10]  T. H. I. Jaakola,et al.  Optimization by direct search and systematic reduction of the size of search region , 1973 .

[11]  James R. Rowland.  Optimal digital simulations for random linear systems with integration constraints , 1973 .

[12]  James K. Hartman.  A new method for global optimization , 1973 .

[13]  Dennis E. Smith.  An Empirical Investigation of Optimum-Seeking in the Computer Simulation Situation , 1973, Oper. Res..

[14]  Joel Weisman,et al.  Introduction to optimization theory , 1973 .

[15]  S. R. Clark,et al.  On the optimization of performance of time-sharing systems by simulation , 1972, CACM.

[16]  Edward Ignall.  Note--On Experimental Designs for Computer Simulation Experiments , 1972 .

[17]  Jack P. C. Kleijnen,et al.  The Use of Multiple Ranking Procedures to Analyze Simulations of Management Systems: A Tutorial , 1972 .

[18]  John M. Burt,et al.  Conditional Monte Carlo: A Simulation Technique for Stochastic Network Analysis , 1971 .

[19]  Thomas J. Schriber,et al.  Use of an external optimizing algorithm with a GPSS model , 1971, WSC '71.

[20]  Dennis E Smith.  AN OPTIMIZER FOR USE IN COMPUTER SIMULATION: BACKGROUND AND DESIGN CONCEPTS , 1970 .

[21]  Leon S. Lasdon,et al.  Optimization Theory of Large Systems , 1970 .

[22]  Thomas H. Naylor,et al.  Experimental Designs for Computer Simulation Experiments , 1970 .

[23]  E. M. L. Beale,et al.  Nonlinear Programming: A Unified Approach. , 1970 .

[24]  James R. Emshoff,et al.  Design and use of computer simulation models , 1970 .

[25]  Harold L. Pazer,et al.  Simulation in Business and Economics , 1969 .

[26]  A. R. Manson,et al.  Minimum Bias Estimation and Experimental Design for Response Surfaces , 1969 .

[27]  John Mandel,et al.  A Method for Fitting Empirical Surfaces To Physical or Chemical Data , 1969 .

[28]  Thomas H. Naylor,et al.  The design of computer simulation experiments , 1969 .

[29]  Olvi L. Mangasarian,et al.  Nonlinear Programming , 1969 .

[30]  G. Leininger,et al.  The determination of digital simulation models for continuous systems by direct-search minimization , 1968, ACM National Conference.

[31]  George S. Fishman,et al.  Problems in the statistical analysis of simulation experiments: the comparision of means and the length of sample records , 1967, CACM.

[32]  William J. Hill,et al.  A Review of Response Surface Methodology: A Literature Survey* , 1966 .

[33]  Harry M. Markowitz,et al.  SIMOPTIMIZATION RESEARCH. PHASE III. , 1966 .

[34]  M. J. D. Powell,et al.  An efficient method for finding the minimum of a function of several variables without calculating derivatives , 1964, Comput. J..

[35]  Norman R. Draper,et al.  "RIDGE ANALYSIS" OF RESPONSE SURFACES , 1963 .

[36]  G. R. Hext,et al.  Sequential Application of Simplex Designs in Optimisation and Evolutionary Operation , 1962 .

[37]  Raymond A. Mugele,et al.  A nonlinear digital optimizing program for process control systems , 1962, AIEE-IRE '62 (Spring).

[38]  Robert Hooke,et al.  `` Direct Search'' Solution of Numerical and Statistical Problems , 1961, JACM.

[39]  H. H. Rosenbrock,et al.  An Automatic Method for Finding the Greatest or Least Value of a Function , 1960, Comput. J..

[40]  Samuel H. Brooks.  A Comparison of Maximum-Seeking Methods , 1959 .

[41]  Thomas A. Budne.  The Application of Random Balance Designs , 1959 .

[42]  F. Anscombe.  Quick Analysis Methods for Random Balance, Screening Experiments , 1959 .

[43]  F. E. Satterthwaite.  Random Balance Experimentation , 1959 .

[44]  J. S. Hunter,et al.  Discussion of the Papers of Messrs. Satterthwaite and Budne , 1959 .

[45]  R. Baun,et al.  Response Surface Designs for Three Factors at Three Levels , 1959 .

[46]  K. A. Brownlee,et al.  Fractional Factorial Experiment Designs for Factors at Two Levels. , 1958 .

[47]  Fractional Factorial Experiment Designs for Factors at Two Levels. , 1958 .

[48]  O. L. Davies,et al.  Design and analysis of industrial experiments , 1954 .

[49]  C. Dunnett,et al.  A TOW-SAMPLE MULTIPLE DECISION PROCEDURE FOR RANKING MEANS OF NORMAL POPULATIONS WITH A COMMON UNKNOWN VARIANCE , 1954 .

[50]  G. Box.  MULTI-FACTOR DESIGNS OF FIRST ORDER , 1952 .