Multiple response surface methods in computer simulation

This paper reviews the application of multiple re sponse surfaces to multiple-variable optimization problems and describes how these techniques may be used in analyzing computer simulation experiments. An example with four response surfaces illustrates the method. In it a simulation of a tank duel is analyzed to determine the values of two independent (input) variables that will optimize four dependent (output) variables simultaneously. The problem examined is that of training battle-tank crews, and the optimization procedure used is based on the Geoffrion-Dyer interactive vector maximal algorithm.

[1]  A. Umland,et al.  The Use of LaGrange Multipliers with Response Surfaces , 1959 .

[2]  Laurence J. Moore,et al.  Optimizing Transportation Problems with Multiple Objectives , 1973 .

[3]  James Rumrill Miller.  Professional decision-making : a procedure for evaluating complex alternatives , 1970 .

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

[5]  W. Biles.  A Response Surface Method for Experimental Optimization of Multi-Response Processes , 1975 .

[6]  A. Charnes,et al.  Management Models and Industrial Applications of Linear Programming , 1961 .

[7]  G. E. Staats,et al.  Response Surface Optimization when Experimental Factors are Subject to Costs and Constraints , 1973 .

[8]  James S. Dyer,et al.  A Time-Sharing Computer Program for the Solution of the Multiple Criteria Problem , 1973 .

[9]  Lucien Duckstein,et al.  Interactive multiobjective programing in water resources: A case study , 1973 .

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

[11]  Kailash C. Kapur,et al.  An Algorithm for Solving Multicriterion Linear Programming Problems with Examples , 1973 .

[12]  Dennis E. Smith.  Requirements of an “optimizer” for computer simulations , 1973 .

[13]  Robert C. Meier.  The Application of Optimum-Seeking Techniques to Simulation Studies: A Preliminary Evaluation , 1967 .

[14]  J. Dyer.  Interactive Goal Programming , 1972 .

[15]  W. Chow.  A Note on the Calculation of Certain Constrained Maxima , 1962 .

[16]  P. J. Pengilly,et al.  Maximum Yield for Specified Cost , 1963 .

[17]  R. H. Myers,et al.  Response Surface Techniques for Dual Response Systems , 1973 .

[18]  Charles W. Carroll.  The Created Response Surface Technique for Optimizing Nonlinear, Restrained Systems , 1961 .

[19]  Douglas C. Montgomery,et al.  Second-order response surface designs in computer simulation , 1975 .

[20]  Douglas C. Montgomery,et al.  A simulation-search technique: An example application for left-turn phasing , 1970 .

[21]  Douglas C. Montgomery,et al.  A response surface approach to improving traffic signal settings in a street network , 1972 .

[22]  B. K. Lambert,et al.  Application of Response Surface Methodology to the Selection of Machining Variables , 1972 .

[23]  Philip Wolfe,et al.  An algorithm for quadratic programming , 1956 .

[24]  Arthur M. Geoffrion,et al.  An Interactive Approach for Multi-Criterion Optimization, with an Application to the Operation of an Academic Department , 1972 .

[25]  John H. Seinfeld,et al.  Optimization with Multiple Performance Criteria. Application to Minimization of Parameter Sensitivlties in a Refinery Model , 1970 .

[26]  H. Smith,et al.  SUBJECTIVE RESPONSES IN PROCESS INVESTIGATION , 1963 .

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

[28]  Sang M. Lee,et al.  Goal programming for decision analysis , 1972 .