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"Information economics": Inleiding en kritiek |
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A DSS for production planning: A case study including simulation and optimization |
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A DSS for production planning: A case study including simulation and optimization |
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A Methodology for Fitting and Validating Metamodels in Simulation |
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A Methodology for Fitting and Validating Metamodels in Simulation |
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A User's Guide to the Brave New World of Designing Simulation Experiments |
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A User's Guide to the Brave New World of Designing Simulation Experiments |
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A scenario for sequential experimentation |
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A scenario for sequential experimentation |
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Adjustable Robust Parameter Design with Unknown Distributions |
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Adjustable Robust Parameter Design with Unknown Distributions |
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An Overview of the Design and Analysis of Simulation Experiments for Sensitivity Analysis |
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An Overview of the Design and Analysis of Simulation Experiments for Sensitivity Analysis |
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An asymptotic test of optimality conditions in multiresponse simulation optimization |
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Analysis and comparison of two strategies for multi-item inventory systems with joint replenishment costs |
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Analysis and comparison of two strategies for multi-item inventory systems with joint replenishment costs |
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Analysis of simulation with common random numbers: A note on Heikes et al. (1976) (Version 2) |
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Analyzing simulation experiments with common random number, part II: Rao's approach |
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Analyzing simulation experiments with common random number, part II: Rao's approach |
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Analyzing simulation experiments with common random numbers (Part I, 2nd rev. and expanded version) |
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Analyzing simulation experiments with common random numbers (Part I, 2nd rev. and expanded version) |
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Application-driven Sequential Designs for Simulation Experiments: Kriging Metamodeling |
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Application-driven Sequential Designs for Simulation Experiments: Kriging Metamodeling |
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Classic Kriging versus Kriging with Bootstrapping or Conditional Simulation: Classic Kriging's Robust Confidence Intervals and Optimization (Revised version of CentER DP 2013-038) |
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Classic Kriging versus Kriging with Bootstrapping or Conditional Simulation: Classic Kriging's Robust Confidence Intervals and Optimization (Revised version of CentER DP 2013-038) |
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Computers and operations research: A survey |
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Conditional simulation for efficient global optimization |
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Configuring a pull production control strategy through a generic model |
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Configuring a pull production control strategy through a generic model |
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Constrained Optimization in Random Simulation: Efficient Global Optimization and Karush-Kuhn-Tucker Conditions |
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Constrained Optimization in Simulation: A Novel Approach |
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Constrained Optimization in Simulation: Efficient Global Optimization and Karush-Kuhn-Tucker Conditions |
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Constrained Optimization in Simulation: Efficient Global Optimization and Karush-Kuhn-Tucker Conditions (revision of 2021-031) |
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Constrained Optimization in Simulation: Efficient Global Optimization and Karush-Kuhn-Tucker Conditions (revision of 2021-031) |
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Constrained optimization in simulation: A novel approach |
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Convex and Monotonic Bootstrapped Kriging |
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Convex and monotonic bootstrapped kriging |
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Cross-validation using the t statistic |
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Cross-validation using the t statistic |
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Customized Pull Systems for Single-Product Flow Lines |
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Customized Pull Systems for Single-Product Flow Lines |
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Customized Sequential Designs for Random Simulation Experiments: Kriging Metamodelling and Bootstrapping |
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Customized Sequential Designs for Random Simulation Experiments: Kriging Metamodelling and Bootstrapping |
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Customized Sequential Designs for Random Simulation Experiments: Kriging Metamodelling and Bootstrapping |
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De rol van simulatie in de algemene econometrie |
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Decision Support Systems (DSS), en de kleren van de keizer |
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Decision Support Systems (DSS), en de kleren van de keizer |
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Design and Analysis of Monte Carlo Experiments |
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Design and Analysis of Monte Carlo Experiments |
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Design and Analysis of simulation experiments: Tutorial |
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Design and Analysis of simulation experiments: Tutorial |
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Design of Experiments: An Overview |
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Design of Experiments: An Overview |
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Design of simulation experiments |
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Design of simulation experiments |
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Deterministic versus Stochastic Sensitivity Analysis in Investment Problems: An Environmental Case Study |
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Deterministic versus Stochastic Sensitivity Analysis in Investment Problems: An Environmental Case Study |
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Economic framework for information systems |
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Economic framework for information systems |
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Een toepassing van "importance sampling" |
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Efficient Global Optimization for Black-Box Simulation via Sequential Intrinsic Kriging |
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Efficient Global Optimization for Black-Box Simulation via Sequential Intrinsic Kriging |
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Efficient estimation of power functions |
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Efficient estimation of power functions |
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Estimating the Variance of the Predictor in Stochastic Kriging |
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Estimating the Variance of the Predictor in Stochastic Kriging |
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Ethical Issues in Engineering Models: Personal Reflections |
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Ethical Issues in Engineering Models: Personal Reflections |
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Ethical issues in engineering models: An operations researcher's reflections |
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Ethische vragen in Operations Research |
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Expected Improvement in Efficient Global Optimization Through Bootstrapped Kriging - Replaces CentER DP 2010-62 |
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Expected Improvement in Efficient Global Optimization Through Bootstrapped Kriging - Replaces CentER DP 2010-62 |
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Experimental Design for Sensitivity Analysis of Simulation Models |
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Experimental Design for Sensitivity Analysis of Simulation Models |
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Experimental Design for Sensitivity Analysis, Optimization and Validation of Simulation Models |
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Experimental design and regression analysis in simulation: An FMS case study |
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Experimental design and regression analysis in simulation: An FMS case study |
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Factor Screening For Simulation With Multiple Responses: Sequential Bifurcation |
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Factor Screening For Simulation With Multiple Responses: Sequential Bifurcation |
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Factor Screening for Simulation with Multiple Responses: Sequential Bifurcation |
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Finding the Important Factors in Large Discrete-Event Simulation: Sequential Bifurcation and its Applications |
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Finding the Important Factors in Large Discrete-Event Simulation: Sequential Bifurcation and its Applications |
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Generalizations of simulation results: Practicality of statistical methods (Part one) |
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Generalizations of simulation results: Practicality of statistical methods (Part one) |
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Generalized Response Surface Methodology: A New Metaheuristic |
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8 |
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Generalized Response Surface Methodology: A New Metaheuristic |
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Generalizing simulation results through metamodels |
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Global Optimization for Black-box Simulation via Sequential Intrinsic Kriging |
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Global Optimization for Black-box Simulation via Sequential Intrinsic Kriging |
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9 |
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Identifying the important factors in simulation models with many factors |
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Identifying the important factors in simulation models with many factors |
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Identifying the important factors in simulation models with many factors |
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Identifying the important factors in simulation models with many factors |
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Importance sampling in systems simulation: A practical failure? |
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Importance sampling in systems simulation: A practical failure? |
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Improved design of queuing simulation experiments with highly heteroscedastic responses |
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2 |
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10 |

Improved design of queuing simulation experiments with highly heteroscedastic responses |
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Jackknifing estimated weighted least squares |
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Jackknifing estimated weighted least squares |
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Kriging Metamodeling in Simulation: A Review |
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Kriging Metamodeling in Simulation: A Review |
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Kriging for Interpolation in Random Simulation |
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11 |
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36 |

Kriging for Interpolation in Random Simulation |
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Kriging in Multi-response Simulation, including a Monte Carlo Laboratory (Replaced by 2014-012) |
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Kriging in Multi-response Simulation, including a Monte Carlo Laboratory (Replaced by 2014-012) |
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20 |
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82 |

Kriging: Methods and Applications |
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Kriging: Methods and Applications |
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29 |
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Maximizing the simulation output: A competition |
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Measurement scales and resolution IV designs: A note (Version 3) |
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Measurement scales and resolution IV designs: A note (Version 3) |
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4 |
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Measuring the Quality of Publications: New Methodology and Case Study |
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1 |
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Measuring the Quality of Publications: New Methodology and Case Study |
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3 |

Methodology for Determining the Acceptability of Given Designs in Uncertain Environments |
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Methodology for Determining the Acceptability of Given Designs in Uncertain Environments |
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Methodology for determining the acceptability of system designs in uncertain environments |
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Minimizing waiting times using priority classes: A case study in response surface methodology |
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Monotonicity-Preserving Bootstrapped Kriging Metamodels for Expensive Simulations |
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Monotonicity-preserving bootstrapped kriging metamodels for expensive simulations |
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2 |
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Monte Carlo sampling and variance reduction techniques |
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Monte Carlo sampling and variance reduction techniques |
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2 |
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Multivariate Versus Univariate Kriging Metamodels for Multi-Response Simulation Models (Revision of 2012-039) |
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Multivariate Versus Univariate Kriging Metamodels for Multi-Response Simulation Models (Revision of 2012-039) |
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23 |
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52 |

New entrants and the role of information technology: Case study: The Tele-Flower Auction in The Netherlands |
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New species of hybrid pull systems |
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New species of hybrid pull systems |
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On the interpretation of variables |
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On the interpretation of variables |
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Operations research and computers |
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Operations research and computers |
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Optimal design of experiments with simulation models of nearly saturated queues |
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Optimal design of experiments with simulation models of nearly saturated queues |
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7 |

Optimal design of simulation experiments with nearly saturated queues |
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Optimal design of simulation experiments with nearly saturated queues |
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3 |
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Optimization Versus Robustness in Simulation: A Practical Methodology, With a Production-Management Case-Study |
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Optimization Versus Robustness in Simulation: A Practical Methodology, With a Production-Management Case-Study |
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4 |
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1 |
14 |

Optimization and Sensitivity Analysis of Computer Similation Models by the Score Function Method |
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3 |
389 |

Optimization and sensitivity analysis of computer simulation models by the score function method |
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3 |
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Optimization and sensitivity analysis of computer simulation models by the score function method |
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Optimization of Simulated Inventory Systems: OptQuest and Alternatives |
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12 |
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47 |

Optimization of Simulated Inventory Systems: OptQuest and Alternatives |
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Parametric and distribution-free bootstrapping in robust simulation-optimization |
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1 |
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Prediction for Big Data through Kriging: Small Sequential and One-Shot Designs |
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Prediction for Big Data through Kriging: Small Sequential and One-Shot Designs |
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41 |
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41 |

Pseudorandom number generation on supercomputers |
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9 |

Pseudorandom number generation on supercomputers |
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Pseudorandom number generators revisited |
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Pseudorandom number generators revisited |
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Quantile estimation in regenerative simulation: A case study |
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Regression Models and Experimental Designs: A Tutorial for Simulation Analaysts |
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Regression Models and Experimental Designs: A Tutorial for Simulation Analaysts |
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Regression analysis for simulation practitioners |
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Regression analysis for simulation practitioners |
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Regression analysis of factoral designs with sequential replication |
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Regression analysis of factoral designs with sequential replication |
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Regression analysis of simulation experiments: Functional software specification |
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Regression analysis of simulation experiments: Functional software specification |
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Regression analysis: Assumptions, alternatives, applications |
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Regression analysis: Assumptions, alternatives, applications |
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4 |
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Regression and Kriging Metamodels with Their Experimental Designs in Simulation: Review |
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53 |
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Regression and Kriging Metamodels with Their Experimental Designs in Simulation: Review |
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Regression estimators in simulation |
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Regression estimators in simulation |
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Regression metamodel summarization of model behaviour |
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Regression metamodel summarization of model behaviour |
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Regression metamodels for simulation with common random numbers: Comparison of techniques |
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Regression metamodels for simulation with common random numbers: Comparison of techniques |
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Regression sampling in statistical auditing |
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0 |
0 |
0 |
0 |

Regression sampling in statistical auditing |
0 |
0 |
0 |
6 |
0 |
0 |
0 |
32 |

Response Surface Methodology |
0 |
0 |
1 |
2 |
1 |
1 |
6 |
12 |

Response Surface Methodology |
0 |
0 |
0 |
65 |
0 |
0 |
0 |
252 |

Response Surface Methodology's Steepest Ascent and Step Size Revisited |
0 |
0 |
2 |
11 |
1 |
1 |
5 |
131 |

Response Surface Methodology's Steepest Ascent and Step Size Revisited |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
4 |

Robust Optimization in Simulation: Taguchi and Krige Combined |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
4 |

Robust Optimization in Simulation: Taguchi and Krige Combined |
0 |
0 |
1 |
6 |
1 |
1 |
6 |
35 |

Robust Optimization in Simulation: Taguchi and Response Surface Methodology |
0 |
0 |
0 |
4 |
0 |
0 |
2 |
34 |

Robust Optimization in Simulation: Taguchi and Response Surface Methodology |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Robust optimization in simulation: Taguchi and Response Surface Methodology |
0 |
0 |
0 |
10 |
0 |
0 |
1 |
38 |

Sampling for quality inspection and correction: AOQL performance criteria |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
27 |

Sampling for quality inspection and correction: AOQL performance criteria |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |

Sampling for quality inspection and correction: AOQL performance criteria (Version 3) |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Sampling for quality inspection and correction: AOQL performance criteria (Version 3) |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
25 |

Scoring methods, multiple criteria, and utility analysis |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
16 |

Scoring methods, multiple criteria, and utility analysis |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Screening Experiments for Simulation: A Review |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
18 |

Screening Experiments for Simulation: A Review |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Sealed-Bid Auction of Dutch Mussels: Statistical Analysis |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
21 |

Sealed-Bid Auction of Dutch Mussels: Statistical Analysis |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |

Sealed-Bid Auctions: Case Study |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |

Sealed-Bid Auctions: Case Study |
0 |
0 |
1 |
2 |
0 |
0 |
1 |
16 |

Selecting random number seeds in practice |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
6 |

Selecting random number seeds in practice |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Sensitivity Analysis of Censored Output Through Polynomial, Logistic and Tobit Regression Meta-Models: Theory and Case Study |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
17 |

Sensitivity Analysis of Censored Output Through Polynomial, Logistic and Tobit Regression Meta-Models: Theory and Case Study |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
5 |

Sensitivity Analysis of Simulation Models |
0 |
0 |
0 |
12 |
0 |
0 |
0 |
37 |

Sensitivity Analysis of Simulation Models |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
5 |

Sensitivity analysis and optimization of system dynamics models: Regression analysis and statistical design of experiments |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
3 |

Sensitivity analysis and optimization of system dynamics models: Regression analysis and statistical design of experiments |
0 |
0 |
1 |
48 |
0 |
1 |
3 |
152 |

Sensitivity analysis and related analysis: A survey of statistical techniques |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Sensitivity analysis and related analysis: A survey of statistical techniques |
0 |
0 |
0 |
26 |
0 |
0 |
0 |
119 |

Sensitivity analysis of simulation experiments: Regression analysis and statistical design |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |

Sensitivity analysis of simulation experiments: Regression analysis and statistical design |
0 |
0 |
0 |
10 |
0 |
0 |
0 |
41 |

Sensitivity analysis of simulation experiments: Tutorial on regression analysis and statistical design |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
20 |

Sensitivity analysis of simulation experiments: Tutorial on regression analysis and statistical design |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |

Sequential Probability Ration Tests: Conservative and Robust |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
4 |

Short-Term Robustness of Production Management Systems: New Methodology |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
6 |

Short-Term Robustness of Production Management Systems: New Methodology |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |

Short-term robustness of production management systems |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |

Short-term robustness of production management systems |
0 |
0 |
0 |
6 |
0 |
0 |
2 |
24 |

Simulatie |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Simulation Experiments in Practice: Statistical Design and Regression Analysis |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |

Simulation Experiments in Practice: Statistical Design and Regression Analysis |
0 |
0 |
0 |
8 |
0 |
0 |
0 |
24 |

Simulation Experiments in Practice: Statistical Design and Regression Analysis |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Simulation Experiments in Practice: Statistical Design and Regression Analysis |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
16 |

Simulation Optimization through Regression or Kriging Metamodels |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Simulation Optimization through Regression or Kriging Metamodels |
0 |
0 |
0 |
17 |
0 |
0 |
0 |
28 |

Simulation and optimization in production planning: A case study (Version 2) |
0 |
0 |
0 |
5 |
0 |
0 |
0 |
19 |

Simulation and optimization in production planning: A case study (Version 2) |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
5 |

Simulation in the evaluation of management information systems: An overview and evaluation |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Simulation in the evaluation of management information systems: An overview and evaluation |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
22 |

Simulation with too many factors: Review of random and group-screening designs |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
12 |

Simulation with too many factors: Review of random and group-screening designs |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Simulation-Optimization via Kriging and Bootstrapping: A Survey (Revision of CentER DP 2011-064) |
0 |
0 |
0 |
23 |
0 |
0 |
0 |
50 |

Simulation-Optimization via Kriging and Bootstrapping: A Survey (Revision of CentER DP 2011-064) |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
6 |

Small-jobs-first: A combined queuing, simulation, and regression analysis |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
1 |

Small-jobs-first: A combined queuing, simulation, and regression analysis |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
13 |

Statistical Analysis of Random Simulations: Bootstrap Tutorial |
0 |
0 |
0 |
13 |
0 |
0 |
0 |
44 |

Statistical Analysis of Random Simulations: Bootstrap Tutorial |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Statistical Testing of Optimality Conditions in Multiresponse Simulation-Based Optimization (Replaced by Discussion Paper 2007-45) |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
19 |

Statistical Testing of Optimality Conditions in Multiresponse Simulation-Based Optimization (Replaced by Discussion Paper 2007-45) |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |

Statistical Testing of Optimality Conditions in Multiresponse Simulation-based Optimization (Revision of 2005-81) |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Statistical Testing of Optimality Conditions in Multiresponse Simulation-based Optimization (Revision of 2005-81) |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
28 |

Statistical Tests for Cross-Validation of Kriging Models |
0 |
0 |
1 |
33 |
0 |
0 |
2 |
37 |

Statistical Tests for Cross-Validation of Kriging Models |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |

Statistical analysis of steady-state simulations: Survey of recent progress |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Statistical analysis of steady-state simulations: Survey of recent progress |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
16 |

Statistical aspects of simulation: An updated survey |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
5 |

Statistical aspects of simulation: An updated survey |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |

Statistical validation of simulation models: A case study |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
4 |

Statistical validation of simulation models: A case study |
0 |
0 |
0 |
5 |
0 |
0 |
0 |
19 |

Statistics and deterministic simulation models: Why not? |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Statistics and deterministic simulation models: Why not? |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
11 |

Stochastic Intrinsic Kriging for Simulation Metamodelling |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
3 |

Stochastic Intrinsic Kriging for Simulation Metamodelling |
0 |
0 |
0 |
26 |
0 |
0 |
0 |
60 |

Stochastic Intrinsic Kriging for Simulation Metamodelling |
0 |
0 |
0 |
14 |
0 |
0 |
0 |
47 |

Stochastic Intrinsic Kriging for Simulation Metamodelling |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
4 |

Supercomputers, Monte Carlo simulation and regression analysis |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |

Supercomputers, Monte Carlo simulation and regression analysis |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
30 |

Superefficient estimation of power functions in simulation experiments |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |

Superefficient estimation of power functions in simulation experiments |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
4 |

Supply Chain Simulation: A Survey |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
9 |

Supply Chain Simulation: A Survey |
0 |
0 |
0 |
9 |
0 |
0 |
0 |
45 |

Techniques for sensitivity analysis of simulation models: A case study of the CO2 greenhouse effect |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
30 |

Techniques for sensitivity analysis of simulation models: A case study of the CO2 greenhouse effect |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Testing the Assumptions of Sequential Bifurcation for Factor Screening (revision of CentER DP 2015-034) |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |

Testing the Assumptions of Sequential Bifurcation for Factor Screening (revision of CentER DP 2015-034) |
0 |
0 |
0 |
19 |
0 |
0 |
1 |
41 |

Testing the mean of an asymmetric population: Johnson's modified t test revisited |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
1 |

Testing the mean of an asymmetric population: Johnson's modified t test revisited |
0 |
1 |
1 |
16 |
0 |
1 |
2 |
66 |

The Correct Kriging Variance Estimated by Bootstrapping |
0 |
0 |
1 |
1 |
0 |
0 |
3 |
4 |

The Correct Kriging Variance Estimated by Bootstrapping |
0 |
0 |
0 |
17 |
0 |
0 |
0 |
65 |

The power of weighted and ordinary least squares with estimated unequal variances in experimental design |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
6 |

The power of weighted and ordinary least squares with estimated unequal variances in experimental design |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
2 |

The role of statistical methodology in simulation |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |

The role of statistical methodology in simulation |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
11 |

Timeliness of information: A basic model |
0 |
0 |
1 |
5 |
0 |
0 |
1 |
24 |

Timeliness of information: A basic model |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |

Two-stage versus sequential sample-size determination in regression analysis of simulation experiments |
0 |
0 |
0 |
1 |
0 |
1 |
1 |
14 |

Two-stage versus sequential sample-size determination in regression analysis of simulation experiments |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |

Two-stage versus sequential sample-size determination in regression analysis of simulation experiments |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Two-stage versus sequential sample-size determination in regression analysis of simulation experiments |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
10 |

Validating the Assumptions of Sequential Bifurcation in Factor Screening |
0 |
0 |
0 |
19 |
0 |
0 |
0 |
33 |

Validating the Assumptions of Sequential Bifurcation in Factor Screening |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |

Validation of Models: Statistical Techniques and Data Availability |
0 |
0 |
0 |
5 |
0 |
0 |
1 |
28 |

Validation of Models: Statistical Techniques and Data Availability |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |

Validation of Simulation Models: Regression Analysis Revisited |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |

Validation of Simulation, With and Without Real Data |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |

Validation of Simulation, With and Without Real Data |
0 |
0 |
0 |
8 |
1 |
1 |
1 |
20 |

Validation of simulation models: Mine-hunting case-study |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
14 |

Validation of simulation models: Mine-hunting case-study |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |

Variance Reduction Techniques in Monte Carlo Methods |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |

Variance Reduction Techniques in Monte Carlo Methods |
0 |
0 |
1 |
9 |
0 |
1 |
2 |
31 |

Variance heterogeneity in experimental design |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |

Variance heterogeneity in experimental design |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
14 |

Vector computers, Monte Carlo simulation, and regression analysis: An introduction (Version 2) |
0 |
0 |
0 |
1 |
1 |
1 |
1 |
17 |

Vector computers, Monte Carlo simulation, and regression analysis: An introduction (Version 2) |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |

Verification and validation of models |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
4 |

Verification and validation of models |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
10 |

Verification and validation of simulation models |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
6 |

Verification and validation of simulation models |
0 |
0 |
1 |
3 |
0 |
1 |
2 |
27 |

When is the design of a manufacturing system acceptable in the presence of uncertainty? |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
7 |

White Noise Assumptions Revisited: Regression Models and Statistical Designs for Simulation Practice |
0 |
0 |
0 |
6 |
0 |
0 |
0 |
38 |

White Noise Assumptions Revisited: Regression Models and Statistical Designs for Simulation Practice |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
2 |

Total Working Papers |
2 |
6 |
31 |
1,087 |
19 |
41 |
173 |
5,409 |