Experimental design and regression analysis in simulation: An FMS case study

Kleijnen, J.P.C.; Standridge, Charles R.

doi:10.1016/0377-2217(88)90168-3

Cited by 82 publications

(22 citation statements)

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“…In this sense, this was a so-called perfect model study (Day et al, 2014). We present the combination of an experimental design (Williams, 1970;Kleijnen and Standridge, 1988;Kleijnen, 2015) and analytical techniques that can address this question. The results were taken from a "synthesis" experiment with a moderately high-resolution earth system model.…”

Section: Introductionmentioning

confidence: 99%

The use of regression for assessing a seasonal forecast model experiment

Benestad¹,

Senan²,

Orsolini³

2016

Earth Syst. Dynam.

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Abstract. We show how factorial regression can be used to analyse numerical model experiments, testing the effect of different model settings. We analysed results from a coupled atmosphere-ocean model to explore how the different choices in the experimental set-up influence the seasonal predictions. These choices included a representation of the sea ice and the height of top of the atmosphere, and the results suggested that the simulated monthly mean air temperatures poleward of the mid-latitudes were highly sensitivity to the specification of the top of the atmosphere, interpreted as the presence or absence of a stratosphere. The seasonal forecasts for the midlatitudes to high latitudes were also sensitive to whether the model set-up included a dynamic or non-dynamic sea-ice representation, although this effect was somewhat less important than the role of the stratosphere. The air temperature in the tropics was insensitive to these choices.

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Section: Introductionmentioning

confidence: 99%

The use of regression for assessing a seasonal forecast model experiment

Benestad¹,

Senan²,

Orsolini³

2016

Earth Syst. Dynam.

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“…The actual values are given in Kleijnen and Standridge (1988), but they are not relevant here. Initially eight intuitively selected combinations are simulated; see Table 9.…”

Section: Case Study: Fmsmentioning

confidence: 99%

“…A machine-mix problem for a particular FMS is studied in Kleijnen and Standridge (1988), and summarized in Kleijnen and Van Groenendaal (1992, pp. 162-164).…”

Section: Case Study: Fmsmentioning

confidence: 99%

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Experimental Design for Sensitivity Analysis, Optimization, and Validation of Simulation Models

Kleijnen

1998

Handbook of Simulation

155

106

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This chapter gives a survey on the use of statistical designs for what-if analysis in simulation, including sensitivity analysis, optimization, and validation/verification. Sensitivity analysis is divided into two phases. The first phase is a pilot stage, which consists of screening or searching for the important factors among (say) hundreds of potentially important factors. A novel screening technique is presented, namely sequential bifurcation. The second phase uses regression analysis to approximate the input/output transformation that is implied by the simulation model; the resulting regression model is also known as a metamodel or a response surface. Regression analysis gives better results when the simulation experiment is well designed, using either classical statistical designs (such as fractional factorials) or optimal designs (such as pioneered by Fedorov, Kiefer, and Wolfowitz). To optimize the simulated system, the analysts may apply Response Surface Methodology (RSM); RSM combines regression analysis, statistical designs, and steepest-ascent hill-climbing. To validate a simulation model, again regression analysis and statistical designs may be applied. Several numerical examples and case-studies illustrate how statistical techniques can reduce the ad hoc character of simulation; that is, these statistical techniques can make simulation studies give more general results, in less time. Appendix 1 summarizes confidence intervals for expected values, proportions, and quantiles, in terminating and steady-state simulations. Appendix 2 gives details on four variance reduction techniques, namely common pseudorandom numbers, antithetic numbers, control variates or regression sampling, and importance sampling. Appendix 3 describes jackknifing, which may give robust confidence intervals.

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“…The first approach assumes that specific system parameters are unknown (e.g., buffer sizes, number of pallets, and conveyor speed). The aim of such research is either to determine these parameters through a search that optimizes one or more performance criteria using so-called simulation optimization approaches ( [7] and [14]) or to evaluate the importance of these design parameters on the system performance using so-called sensitivity analysis ( [4] and [11]). Such an approach, however, does not answer our initial question since in our case the design team has already specified the future system.…”

Section: Introductionmentioning

confidence: 99%

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

Kleijnen

Pierreval

Zhang

2009

2009 International Conference on Computers &Amp; Industrial Engineering

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When is the design of a manufacturing system acceptable in the presence of uncertainty? Kleijnen, Jack; Pierreval, H.; Zhang, J. General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.-Users may download and print one copy of any publication from the public portal for the purpose of private study or research -You may not further distribute the material or use it for any profit-making activity or commercial gain -You may freely distribute the URL identifying the publication in the public portal Take down policyIf you believe that this document breaches copyright, please contact us providing details, and we will remove access to the work immediately and investigate your claim. ABSTRACTIn the design of a manufacturing system, the design specification is often suggested by a design team. Managers are interested in verifying that this specification will satisfy the production requirements. Because the future production environment will likely differ from the one assumed, it is important to determine in which situations the suggested design becomes unacceptable. This paper suggests an approach that allows determining which uncertain parameters are important and which combinations of these parameters can lead to an unacceptable design. This approach combines several methods, namely, simulation, bootstrapping, and metamodeling. The methodology is explained and illustrated through a stochastic simulated manufacturing system, which includes uncertain parameters related to the arrival and the processing times of jobs. This example shows the conditions under which the system does not meet the requirements.

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Experimental design and regression analysis in simulation: An FMS case study

Cited by 82 publications

References 1 publication

The use of regression for assessing a seasonal forecast model experiment

The use of regression for assessing a seasonal forecast model experiment

Experimental Design for Sensitivity Analysis, Optimization, and Validation of Simulation Models

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

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