The Metamodel in Simulation Analysis: Can It Be Trusted?

Friedman, Linda Weiser; Pressman, Israel

doi:10.1057/palgrave.jors.0391006

Cited by 12 publications

(17 citation statements)

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“…12,13 However, only a few applications exist in medical decision making. For example, Merz and Small 14 used logistic regression to identify important variables that can change the decision of anticoagulation in pregnant women with deep vein thrombosis, Tappenden and others 15 investigated several metamodeling techniques for value of information calculations in CEA of interferon-b and glatiramer acetate for multiple sclerosis, and Stevenson and others 16 used Gaussian process modeling in the CEA of treating established osteoporosis.…”

Section: Discussionmentioning

confidence: 99%

Linear Regression Metamodeling as a Tool to Summarize and Present Simulation Model Results

et al. 2013

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Background/Objective. Modelers lack a tool to systematically and clearly present complex model results, including those from sensitivity analyses. The objective was to propose linear regression metamodeling as a tool to increase transparency of decision analytic models and better communicate their results. Methods. We used a simplified cancer cure model to demonstrate our approach. The model computed the lifetime cost and benefit of 3 treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the regression coefficients to describe measures of sensitivity analyses, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic

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Section: Discussionmentioning

confidence: 99%

Linear Regression Metamodeling as a Tool to Summarize and Present Simulation Model Results

et al. 2013

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“…We used a regression metamodel to provide enhanced exploration, interpretation, and generalization of our simulation results. Friedman and Pressman (1988) note that regression metamodeling is useful in postsimulation analysis and that it provides an efficient means of expressing the interrelationships between response variables and independent variables in a simulation study.…”

Section: Discussionmentioning

confidence: 99%

“…"The regression model acts as a metamodel (a model of a model) so that the simulation results can be generalized" (Kleijnen, van den Burg, and van der Ham 1979, p. 50). In addition to generalization, metamodels provide enhanced exploration and interpretation, the answering of inverse questions and provide the researcher with a greater understanding of the system (Friedman and Pressman 1988). In fact, Friedman and Pressman demonstrated that values obtained from metamodels were sufficiently close (within 5 %) to those computed from theoretical (analytical) models.…”

Section: Metamodelsmentioning

confidence: 91%

A comparative study of FMS tool allocation and part type selection approaches for a varying part type mix

Amoako‐Gyampah

1994

Int J Flex Manuf Syst

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Abstract. Hankins and Rovito (1984) examined the impact of different tool policies on cutting tool inventory levels and spindle utilization for a flexible manufacturing system (FMS). This study provides a broader perspective of the impact of tool allocation approaches on flow times, tardiness, percent of orders tardy, machine utilization, and robot utilization. Part type selection procedures have been suggested for the FMS prerelease planning problem. However, very little research has specifically evaluated the part type selection procedures across different tool allocation approaches. Also, with the exception of Kim (1988, 1991) no other known study has provided any insights on what tool allocation approaches are appropriate when processing different mixes of part types. This research is devoted to addressing those issues.Three tool allocation approaches, three production scheduling rules, and three levels of part mix are evaluated in this study through a similation model of a flexible manufacturing system. The specific impacts of the tool approaches, their interaction effects with the part type selection rules, and their effectiveness at different part type mix levels are provided through the use of a regression metamodel.

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“…The concept of metamodeling for computer simulations of manufacturing problems is known since more than two decades (Friedman and Pressman, 1988;Yu and Popplewell, 1994). At the beginning of this century, metal forming or plasticity numerical problems, which are usually very time consuming due to the complexity of physical phenomena involved at large deformations, have been increasingly metamodeled, for purposes like uncertainty assessment (Baghdasaryan et al, 2002) or design optimization (Do et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Reduction of calibration effort in FEM-based optimization via numerical and experimental data fusion

Colosimo

Pagani

Strano

2014

Struct Multidisc Optim

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In this paper a fusion metamodeling approach is suggested as a method for reducing the experimental and computational effort generally required for calibrating the parameters of FEM simulations models. The metamodel is used inside an optimization routine for linking data coming from two different sources: simulations and experiments. The method is applied to a real problem: the optimal design of a metal foam filled tube to be used as an anti-intrusion bar in vehicles. The model is hierarchical, in the sense that one set of data (the experiments) is considered to be more reliable and it is labeled as "high-fidelity" and the other set (the simulations) is labeled as "low-fidelity". In the proposed approach, Gaussian models are used to describe results of computer experiments because they are flexible and they can easily interpolate data coming from deterministic simulations. Since the results of experiments are obviously fully accurate, but aleatory, a second stage ("linkage") model is used, which adjusts the prediction provided by the first model to more accurately represent the real experimental data. In the paper, the modeling and prediction ability of the method is first demonstrated and explained by means of artificially generated data and then applied to the optimization of foam filled tubular structures. The fusion metamodel yields comparable predictions (and optimal solution) if built over calibrated simulations vs. non-calibrated FEM models.

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The Metamodel in Simulation Analysis: Can It Be Trusted?

Cited by 12 publications

References 0 publications

Linear Regression Metamodeling as a Tool to Summarize and Present Simulation Model Results

Linear Regression Metamodeling as a Tool to Summarize and Present Simulation Model Results

A comparative study of FMS tool allocation and part type selection approaches for a varying part type mix

Reduction of calibration effort in FEM-based optimization via numerical and experimental data fusion

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