| Working Paper |
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Abstract Views |
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3 months |
12 months |
Total |
Last month |
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12 months |
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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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11 |
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35 |
| A Methodology for Fitting and Validating Metamodels in Simulation |
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14 |
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69 |
| 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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38 |
| 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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6 |
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40 |
| 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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17 |
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57 |
| An asymptotic test of optimality conditions in multiresponse simulation optimization |
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4 |
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27 |
| 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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9 |
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35 |
| 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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8 |
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38 |
| Application-driven Sequential Designs for Simulation Experiments: Kriging Metamodeling |
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2 |
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10 |
| 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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17 |
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65 |
| 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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4 |
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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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6 |
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25 |
| 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 |
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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 Random Simulation: Efficient Global Optimization and Karush-Kuhn-Tucker Conditions (Revision of CentER DP 2022-022) |
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| Constrained optimization in Random Simulation: Efficient Global Optimization and Karush-Kuhn-Tucker Conditions (Revision of CentER DP 2022-022) |
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5 |
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| Constrained optimization in simulation: A novel approach |
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13 |
| Convex and Monotonic Bootstrapped Kriging |
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14 |
| Convex and Monotonic Bootstrapped Kriging |
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3 |
| Convex and monotonic bootstrapped kriging |
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1 |
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10 |
| Cross-validation using the t statistic |
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| Cross-validation using the t statistic |
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7 |
| Customized Pull Systems for Single-Product Flow Lines |
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9 |
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28 |
| 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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10 |
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60 |
| Customized Sequential Designs for Random Simulation Experiments: Kriging Metamodelling and Bootstrapping |
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3 |
| Customized Sequential Designs for Random Simulation Experiments: Kriging Metamodelling and Bootstrapping |
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2 |
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14 |
| 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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3 |
| Decision Support Systems (DSS), en de kleren van de keizer |
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1 |
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9 |
| 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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65 |
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49 |
| Design of Experiments: An Overview |
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19 |
| Design of Experiments: An Overview |
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2 |
| Design of simulation experiments |
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| Design of simulation experiments |
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8 |
| Deterministic versus Stochastic Sensitivity Analysis in Investment Problems: An Environmental Case Study |
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2 |
| Deterministic versus Stochastic Sensitivity Analysis in Investment Problems: An Environmental Case Study |
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12 |
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54 |
| Economic framework for information systems |
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1 |
| Economic framework for information systems |
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12 |
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34 |
| Een toepassing van "importance sampling" |
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| Efficient Global Optimization for Black-Box Simulation via Sequential Intrinsic Kriging |
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3 |
| Efficient Global Optimization for Black-Box Simulation via Sequential Intrinsic Kriging |
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12 |
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66 |
| Efficient estimation of power functions |
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10 |
| Efficient estimation of power functions |
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| Efficient global optimization and the zero-gradient condition, in expensive simulation |
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| Estimating the Variance of the Predictor in Stochastic Kriging |
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10 |
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45 |
| Estimating the Variance of the Predictor in Stochastic Kriging |
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2 |
| Ethical Issues in Engineering Models: Personal Reflections |
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3 |
| Ethical Issues in Engineering Models: Personal Reflections |
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2 |
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27 |
| Ethical issues in engineering models: An operations researcher's reflections |
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1 |
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14 |
| Ethische vragen in Operations Research |
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2 |
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6 |
| Expected Improvement in Efficient Global Optimization Through Bootstrapped Kriging - Replaces CentER DP 2010-62 |
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4 |
| Expected Improvement in Efficient Global Optimization Through Bootstrapped Kriging - Replaces CentER DP 2010-62 |
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1 |
23 |
| Experimental Design for Sensitivity Analysis of Simulation Models |
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4 |
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14 |
| Experimental Design for Sensitivity Analysis of Simulation Models |
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3 |
| Experimental Design for Sensitivity Analysis, Optimization and Validation of Simulation Models |
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1 |
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5 |
| Experimental Design for Sensitivity Analysis, Optimization and Validation of Simulation Models |
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80 |
1 |
1 |
1 |
181 |
| Experimental design and regression analysis in simulation: An FMS case study |
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1 |
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28 |
| Experimental design and regression analysis in simulation: An FMS case study |
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1 |
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6 |
| Factor Screening For Simulation With Multiple Responses: Sequential Bifurcation |
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1 |
1 |
1 |
0 |
1 |
1 |
5 |
| Factor Screening For Simulation With Multiple Responses: Sequential Bifurcation |
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3 |
0 |
0 |
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34 |
| Factor Screening for Simulation with Multiple Responses: Sequential Bifurcation |
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0 |
0 |
0 |
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4 |
| Factor Screening for Simulation with Multiple Responses: Sequential Bifurcation |
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0 |
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4 |
0 |
0 |
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37 |
| 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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3 |
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12 |
| Generalizations of simulation results: Practicality of statistical methods (Part one) |
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8 |
| Generalizations of simulation results: Practicality of statistical methods (Part one) |
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1 |
| Generalized Response Surface Methodology: A New Metaheuristic |
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4 |
| Generalized Response Surface Methodology: A New Metaheuristic |
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8 |
0 |
0 |
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35 |
| Generalizing simulation results through metamodels |
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1 |
0 |
0 |
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1 |
| Global Optimization for Black-box Simulation via Sequential Intrinsic Kriging |
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0 |
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9 |
0 |
0 |
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66 |
| Global Optimization for Black-box Simulation via Sequential Intrinsic Kriging |
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0 |
0 |
0 |
0 |
1 |
2 |
| Identifying the important factors in simulation models with many factors |
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1 |
0 |
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17 |
| Identifying the important factors in simulation models with many factors |
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2 |
| Identifying the important factors in simulation models with many factors |
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3 |
| Identifying the important factors in simulation models with many factors |
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41 |
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626 |
| Importance sampling in systems simulation: A practical failure? |
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2 |
| Importance sampling in systems simulation: A practical failure? |
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0 |
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14 |
| Improved design of queuing simulation experiments with highly heteroscedastic responses |
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0 |
1 |
1 |
1 |
1 |
6 |
| Improved design of queuing simulation experiments with highly heteroscedastic responses |
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0 |
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2 |
0 |
0 |
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10 |
| Jackknifing estimated weighted least squares |
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0 |
0 |
0 |
0 |
0 |
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2 |
| Jackknifing estimated weighted least squares |
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0 |
0 |
1 |
1 |
1 |
1 |
15 |
| Kriging Metamodeling in Simulation: A Review |
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0 |
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23 |
0 |
0 |
2 |
105 |
| Kriging Metamodeling in Simulation: A Review |
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4 |
0 |
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2 |
18 |
| Kriging for Interpolation in Random Simulation |
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0 |
0 |
0 |
0 |
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4 |
| Kriging for Interpolation in Random Simulation |
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0 |
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11 |
0 |
0 |
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39 |
| Kriging in Multi-response Simulation, including a Monte Carlo Laboratory (Replaced by 2014-012) |
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20 |
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83 |
| Kriging in Multi-response Simulation, including a Monte Carlo Laboratory (Replaced by 2014-012) |
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0 |
0 |
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3 |
| Kriging: Methods and Applications |
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2 |
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4 |
| Kriging: Methods and Applications |
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29 |
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2 |
42 |
| Maximizing the simulation output: A competition |
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1 |
| Measurement scales and resolution IV designs: A note (Version 3) |
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1 |
| Measurement scales and resolution IV designs: A note (Version 3) |
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4 |
0 |
0 |
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13 |
| Measuring the Quality of Publications: New Methodology and Case Study |
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1 |
0 |
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12 |
| Measuring the Quality of Publications: New Methodology and Case Study |
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1 |
0 |
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4 |
| Methodology for Determining the Acceptability of Given Designs in Uncertain Environments |
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1 |
0 |
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12 |
| Methodology for Determining the Acceptability of Given Designs in Uncertain Environments |
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1 |
1 |
1 |
5 |
| Methodology for determining the acceptability of system designs in uncertain environments |
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1 |
0 |
0 |
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36 |
| Minimizing waiting times using priority classes: A case study in response surface methodology |
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0 |
0 |
0 |
0 |
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2 |
| Monotonicity-Preserving Bootstrapped Kriging Metamodels for Expensive Simulations |
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1 |
0 |
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21 |
| Monotonicity-Preserving Bootstrapped Kriging Metamodels for Expensive Simulations |
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0 |
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5 |
| Monotonicity-preserving bootstrapped kriging metamodels for expensive simulations |
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2 |
0 |
0 |
1 |
14 |
| Monte Carlo sampling and variance reduction techniques |
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0 |
0 |
0 |
0 |
0 |
0 |
| Monte Carlo sampling and variance reduction techniques |
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0 |
0 |
2 |
0 |
0 |
1 |
14 |
| Multivariate Versus Univariate Kriging Metamodels for Multi-Response Simulation Models (Revision of 2012-039) |
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0 |
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23 |
0 |
0 |
0 |
52 |
| Multivariate Versus Univariate Kriging Metamodels for Multi-Response Simulation Models (Revision of 2012-039) |
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0 |
0 |
0 |
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10 |
| New entrants and the role of information technology: Case study: The Tele-Flower Auction in The Netherlands |
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1 |
15 |
| New species of hybrid pull systems |
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1 |
3 |
3 |
3 |
| New species of hybrid pull systems |
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2 |
0 |
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14 |
| On the interpretation of variables |
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1 |
| On the interpretation of variables |
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1 |
1 |
1 |
9 |
| Operations research and computers |
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1 |
0 |
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1 |
17 |
| Operations research and computers |
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2 |
| Optimal design of experiments with simulation models of nearly saturated queues |
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1 |
1 |
1 |
8 |
| Optimal design of experiments with simulation models of nearly saturated queues |
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2 |
| Optimal design of simulation experiments with nearly saturated queues |
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2 |
| Optimal design of simulation experiments with nearly saturated queues |
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3 |
0 |
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6 |
| Optimization Versus Robustness in Simulation: A Practical Methodology, With a Production-Management Case-Study |
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4 |
0 |
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15 |
| Optimization Versus Robustness in Simulation: A Practical Methodology, With a Production-Management Case-Study |
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0 |
0 |
0 |
1 |
1 |
1 |
1 |
| Optimization and Sensitivity Analysis of Computer Similation Models by the Score Function Method |
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1 |
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393 |
| Optimization and sensitivity analysis of computer simulation models by the score function method |
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1 |
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1 |
3 |
| Optimization and sensitivity analysis of computer simulation models by the score function method |
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4 |
0 |
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1 |
22 |
| Optimization of Simulated Inventory Systems: OptQuest and Alternatives |
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1 |
1 |
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2 |
| Optimization of Simulated Inventory Systems: OptQuest and Alternatives |
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12 |
0 |
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2 |
50 |
| Optimization of System Dynamics Models: a Novel Methodology |
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0 |
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1 |
| Optimization of System Dynamics Models: a Novel Methodology |
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1 |
3 |
3 |
0 |
1 |
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5 |
| Parametric and distribution-free bootstrapping in robust simulation-optimization |
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1 |
0 |
0 |
1 |
8 |
| Prediction for Big Data through Kriging: Small Sequential and One-Shot Designs |
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0 |
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0 |
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1 |
| Prediction for Big Data through Kriging: Small Sequential and One-Shot Designs |
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41 |
0 |
0 |
0 |
42 |
| Pseudorandom number generation on supercomputers |
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0 |
0 |
0 |
0 |
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9 |
| Pseudorandom number generation on supercomputers |
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0 |
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0 |
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| Pseudorandom number generators revisited |
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| Pseudorandom number generators revisited |
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0 |
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0 |
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9 |
| Quantile estimation in regenerative simulation: A case study |
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0 |
0 |
| Regression Models and Experimental Designs: A Tutorial for Simulation Analaysts |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
| Regression Models and Experimental Designs: A Tutorial for Simulation Analaysts |
0 |
0 |
0 |
6 |
0 |
0 |
0 |
24 |
| Regression analysis for simulation practitioners |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
1 |
| Regression analysis for simulation practitioners |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
7 |
| Regression analysis of factoral designs with sequential replication |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
| Regression analysis of factoral designs with sequential replication |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
7 |
| Regression analysis of simulation experiments: Functional software specification |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
10 |
| Regression analysis of simulation experiments: Functional software specification |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
| Regression analysis: Assumptions, alternatives, applications |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
1 |
| Regression analysis: Assumptions, alternatives, applications |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
21 |
| Regression and Kriging Metamodels with Their Experimental Designs in Simulation: Review |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
17 |
| Regression and Kriging Metamodels with Their Experimental Designs in Simulation: Review |
0 |
0 |
0 |
53 |
0 |
0 |
1 |
105 |
| Regression estimators in simulation |
0 |
0 |
0 |
0 |
1 |
2 |
2 |
7 |
| Regression estimators in simulation |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Regression metamodel summarization of model behaviour |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Regression metamodel summarization of model behaviour |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
15 |
| Regression metamodels for simulation with common random numbers: Comparison of techniques |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
8 |
| Regression metamodels for simulation with common random numbers: Comparison of techniques |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Regression sampling in statistical auditing |
0 |
0 |
0 |
6 |
0 |
0 |
0 |
32 |
| Regression sampling in statistical auditing |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Response Surface Methodology |
0 |
0 |
0 |
65 |
0 |
0 |
1 |
254 |
| Response Surface Methodology |
0 |
0 |
0 |
2 |
0 |
1 |
2 |
14 |
| Response Surface Methodology's Steepest Ascent and Step Size Revisited |
0 |
0 |
0 |
11 |
0 |
0 |
2 |
138 |
| Response Surface Methodology's Steepest Ascent and Step Size Revisited |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
8 |
| Robust Optimization in Simulation: Taguchi and Krige Combined |
0 |
0 |
0 |
6 |
1 |
1 |
1 |
36 |
| Robust Optimization in Simulation: Taguchi and Krige Combined |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
| Robust Optimization in Simulation: Taguchi and Response Surface Methodology |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
34 |
| Robust Optimization in Simulation: Taguchi and Response Surface Methodology |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
| Robust optimization in simulation: Taguchi and Response Surface Methodology |
0 |
0 |
0 |
10 |
0 |
1 |
1 |
40 |
| 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 |
1 |
| Sampling for quality inspection and correction: AOQL performance criteria (Version 3) |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
28 |
| Sampling for quality inspection and correction: AOQL performance criteria (Version 3) |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Scoring methods, multiple criteria, and utility analysis |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
17 |
| 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 |
1 |
23 |
| Sealed-Bid Auction of Dutch Mussels: Statistical Analysis |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Sealed-Bid Auctions: Case Study |
0 |
0 |
0 |
2 |
0 |
0 |
3 |
20 |
| Sealed-Bid Auctions: Case Study |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Selecting random number seeds in practice |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Selecting random number seeds in practice |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
6 |
| Sensitivity Analysis of Censored Output Through Polynomial, Logistic and Tobit Regression Meta-Models: Theory and Case Study |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
6 |
| Sensitivity Analysis of Censored Output Through Polynomial, Logistic and Tobit Regression Meta-Models: Theory and Case Study |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
18 |
| Sensitivity Analysis of Simulation Models |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
8 |
| Sensitivity Analysis of Simulation Models |
0 |
0 |
0 |
12 |
0 |
0 |
0 |
37 |
| Sensitivity analysis and optimization of system dynamics models: Regression analysis and statistical design of experiments |
0 |
0 |
0 |
49 |
1 |
1 |
4 |
159 |
| Sensitivity analysis and optimization of system dynamics models: Regression analysis and statistical design of experiments |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
6 |
| Sensitivity analysis and related analysis: A survey of statistical techniques |
0 |
0 |
0 |
26 |
0 |
0 |
1 |
121 |
| Sensitivity analysis and related analysis: A survey of statistical techniques |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
| Sensitivity analysis of simulation experiments: Regression analysis and statistical design |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
| 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 |
1 |
1 |
2 |
23 |
| 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 |
32 |
0 |
1 |
1 |
37 |
| Sequential Probability Ration Tests: Conservative and Robust |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
5 |
| 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 |
1 |
1 |
1 |
3 |
| Short-term robustness of production management systems |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Short-term robustness of production management systems |
0 |
0 |
0 |
6 |
0 |
0 |
6 |
33 |
| 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 |
1 |
0 |
0 |
0 |
17 |
| Simulation Experiments in Practice: Statistical Design and Regression Analysis |
0 |
0 |
0 |
8 |
0 |
0 |
0 |
25 |
| Simulation Experiments in Practice: Statistical Design and Regression Analysis |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
| Simulation Optimization through Regression or Kriging Metamodels |
0 |
0 |
0 |
17 |
0 |
0 |
0 |
28 |
| Simulation Optimization through Regression or Kriging Metamodels |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
3 |
| Simulation and optimization in production planning: A case study (Version 2) |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
6 |
| Simulation and optimization in production planning: A case study (Version 2) |
0 |
0 |
0 |
5 |
1 |
1 |
1 |
23 |
| Simulation in the evaluation of management information systems: An overview and evaluation |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
| Simulation in the evaluation of management information systems: An overview and evaluation |
0 |
0 |
0 |
0 |
1 |
2 |
2 |
24 |
| Simulation with too many factors: Review of random and group-screening designs |
0 |
0 |
0 |
1 |
1 |
1 |
1 |
13 |
| 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 |
1 |
0 |
0 |
1 |
8 |
| Simulation-Optimization via Kriging and Bootstrapping: A Survey (Revision of CentER DP 2011-064) |
0 |
0 |
0 |
23 |
0 |
0 |
0 |
50 |
| Small-jobs-first: A combined queuing, simulation, and regression analysis |
0 |
0 |
0 |
2 |
1 |
1 |
1 |
17 |
| Small-jobs-first: A combined queuing, simulation, and regression analysis |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Statistical Analysis of Random Simulations: Bootstrap Tutorial |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Statistical Analysis of Random Simulations: Bootstrap Tutorial |
0 |
0 |
0 |
13 |
0 |
0 |
0 |
45 |
| 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 |
28 |
| Statistical Testing of Optimality Conditions in Multiresponse Simulation-based Optimization (Revision of 2005-81) |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
| Statistical Tests for Cross-Validation of Kriging Models |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
| Statistical Tests for Cross-Validation of Kriging Models |
0 |
0 |
0 |
33 |
0 |
1 |
2 |
39 |
| 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 |
6 |
| 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 |
0 |
1 |
5 |
| Statistical validation of simulation models: A case study |
0 |
0 |
0 |
5 |
0 |
0 |
0 |
21 |
| Statistics and deterministic simulation models: Why not? |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
11 |
| Statistics and deterministic simulation models: Why not? |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Stochastic Intrinsic Kriging for Simulation Metamodelling |
0 |
0 |
1 |
15 |
0 |
0 |
1 |
48 |
| Stochastic Intrinsic Kriging for Simulation Metamodelling |
0 |
0 |
0 |
26 |
0 |
0 |
1 |
62 |
| Stochastic Intrinsic Kriging for Simulation Metamodelling |
0 |
0 |
0 |
0 |
2 |
3 |
3 |
6 |
| Stochastic Intrinsic Kriging for Simulation Metamodelling |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
5 |
| Supercomputers, Monte Carlo simulation and regression analysis |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Supercomputers, Monte Carlo simulation and regression analysis |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
31 |
| Superefficient estimation of power functions in simulation experiments |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
| Superefficient estimation of power functions in simulation experiments |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
| Supply Chain Simulation: A Survey |
0 |
0 |
0 |
9 |
0 |
0 |
3 |
49 |
| Supply Chain Simulation: A Survey |
0 |
0 |
1 |
1 |
0 |
0 |
2 |
11 |
| Techniques for sensitivity analysis of simulation models: A case study of the CO2 greenhouse effect |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Techniques for sensitivity analysis of simulation models: A case study of the CO2 greenhouse effect |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
31 |
| Testing the Assumptions of Sequential Bifurcation for Factor Screening (revision of CentER DP 2015-034) |
0 |
0 |
0 |
19 |
0 |
0 |
0 |
41 |
| 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 mean of an asymmetric population: Johnson's modified t test revisited |
0 |
0 |
0 |
17 |
0 |
0 |
1 |
68 |
| Testing the mean of an asymmetric population: Johnson's modified t test revisited |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
| The Correct Kriging Variance Estimated by Bootstrapping |
0 |
0 |
0 |
17 |
0 |
0 |
0 |
65 |
| The Correct Kriging Variance Estimated by Bootstrapping |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
5 |
| The Cost of Risk-Aversion In Inventory Management: An (s,S) Case Study |
0 |
0 |
2 |
2 |
0 |
1 |
5 |
5 |
| The Cost of Risk-Aversion In Inventory Management: An (s,S) Case Study |
0 |
0 |
3 |
3 |
0 |
0 |
2 |
2 |
| The power of weighted and ordinary least squares with estimated unequal variances in experimental design |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
7 |
| 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 |
1 |
1 |
1 |
12 |
| Timeliness of information: A basic model |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| Timeliness of information: A basic model |
0 |
0 |
0 |
5 |
0 |
0 |
0 |
24 |
| Two-stage versus sequential sample-size determination in regression analysis of simulation experiments |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
10 |
| 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 |
1 |
4 |
| Two-stage versus sequential sample-size determination in regression analysis of simulation experiments |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
15 |
| Validating the Assumptions of Sequential Bifurcation in Factor Screening |
0 |
0 |
0 |
19 |
0 |
0 |
0 |
35 |
| 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 |
1 |
1 |
29 |
| 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 Models: Regression Analysis Revisited |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
20 |
| Validation of Simulation, With and Without Real Data |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
| Validation of Simulation, With and Without Real Data |
0 |
0 |
0 |
8 |
0 |
0 |
0 |
20 |
| Validation of simulation models: Mine-hunting case-study |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
3 |
| Validation of simulation models: Mine-hunting case-study |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
15 |
| Variance Reduction Techniques in Monte Carlo Methods |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
| Variance Reduction Techniques in Monte Carlo Methods |
0 |
0 |
0 |
9 |
0 |
0 |
1 |
32 |
| Variance heterogeneity in experimental design |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
14 |
| Variance heterogeneity in experimental design |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Vector computers, Monte Carlo simulation, and regression analysis: An introduction (Version 2) |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
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 |
0 |
11 |
| Verification and validation of models |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
| Verification and validation of simulation models |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
28 |
| Verification and validation of simulation models |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
7 |
| When is the design of a manufacturing system acceptable in the presence of uncertainty? |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
8 |
| White Noise Assumptions Revisited: Regression Models and Statistical Designs for Simulation Practice |
0 |
0 |
0 |
6 |
0 |
1 |
1 |
39 |
| White Noise Assumptions Revisited: Regression Models and Statistical Designs for Simulation Practice |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
2 |
| Total Working Papers |
1 |
3 |
12 |
1,269 |
39 |
74 |
186 |
6,114 |
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