scholarly journals Monte Carlo Simulation Experiments for Engineering Optimisation

2015 ◽  
Vol 2 (1) ◽  
pp. 97
Author(s):  
Robert Anderson ◽  
Zhou Wei ◽  
Ian Cox ◽  
Malcolm Moore ◽  
Florence Kussener
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Tolerance Analysis ◽  
Simulation Experiments ◽  
Practical Applications ◽  
Study Approach ◽  
Good Set ◽  
Robust Process ◽  
And Control

Design of Experiments (DoE) is widely used in design, manufacturing and quality management. The resulting data is usually analysed with multiple linear regression to generate polynomial equations that describe the relationship between process inputs and outputs. These equations enable us to understand how input values affect the predicted value of one or more outputs and find good set points for the inputs. However, to develop robust manufacturing processes, we also need to understand how variation in these inputs appears as variation in the output. This understanding allows us to define set points and control tolerances for the inputs that will keep the outputs within their required specification windows. Tolerance analysis provides a powerful way of finding input settings and ranges that minimise output variation to produce a process that is robust. In many practical applications, tolerance analysis exploits Monte Carlo simulation of the polynomial model generated from DoE’s. This paper briefly describes tolerance analysis and then shows how Monte Carlo simulation experiments using space-filling designs can be used to find the input settings that result in a robust process. Using this approach, engineers can quickly and easily identify the key inputs responsible for transferring undesired variation to their process outputs and identify the set points and ranges that make their process as robust as possible. If the process is not sufficiently robust, they can rationally investigate different strategies to improve it. A case study approach is used to aid explanation and understanding.

scholarly journals An Improved Model for Evaluating Change in Randomized Pretest, Posttest, Follow-Up Designs

Methodology ◽  
2012 ◽  
Vol 8 (3) ◽  
pp. 97-103 ◽  
Author(s):  
Constance A. Mara ◽  
Robert A. Cribbie ◽  
David B. Flora ◽  
Cathy LaBrish ◽  
Laura Mills ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Group Differences ◽  
Primary Research ◽  
Control Groups ◽  
Proposed Model ◽  
Research Questions ◽  
Improved Model ◽  
And Control

Randomized pretest, posttest, follow-up (RPPF) designs are often used for evaluating the effectiveness of an intervention. These designs typically address two primary research questions: (1) Do the treatment and control groups differ in the amount of change from pretest to posttest? and (2) Do the treatment and control groups differ in the amount of change from posttest to follow-up? This study presents a model for answering these questions and compares it to recently proposed models for analyzing RPPF designs due to Mun, von Eye, and White (2009) using Monte Carlo simulation. The proposed model provides increased power over previous models for evaluating group differences in RPPF designs.

The accountability process during the centrality of state institutional logics: a case from an African rural context

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ahmed Diab
Keyword(s):  
Institutional Logics ◽  
Corporate Accountability ◽  
Content Type ◽  
State Institutions ◽  
Study Approach ◽  
Control Practice ◽  
African Context ◽  
And Control

PurposeThis study investigates state institutions' influence on corporate accountability and control practices in a rural African context. Exploring the different rationales behind state existence in the context of sugar production in Egypt, this work clarifies how accountability is practised differently in the case of the high centrality of state logics in the business sector.Design/methodology/approachTheoretically, the study draws insights from the institutional logics perspective. Following the case study approach, data are collected through interviews, observations and documents.FindingsThe study found that state institutions can play a supportive rather than a mere constraining role in the management, accountability and control processes. Notably, it clarified how state-related institutions were highly central and influential in a way that enabled them to curb the (negative) influences of the community and business institutions. In this context, it is social – rather than functional – accountability which emerges as the central control practice to achieve answerability and enforcement.Originality/valueThus, this study's reported findings confirm the role of institutional (political) logics as supportive in society.

Comparison of Assembly Tolerance Analysis by the Direct Linearization and Modified Monte Carlo Simulation Methods

1995 ◽  
Author(s):  
Jinsong Gao ◽  
Kenneth W. Chase ◽  
Spencer P. Magleby
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Closed Loop ◽  
Great Influence ◽  
Tolerance Analysis ◽  
Linearization Method ◽  
Direct Linearization ◽  
Highly Nonlinear ◽  
Assembly Features ◽  
Assembly Constraints

Abstract Two methods for performing statistical tolerance analysis of mechanical assemblies are compared: the Direct Linearization Method (DLM), and Monte Carlo simulation. A selection of 2-D and 3-D vector models of assemblies were analyzed, including problems with closed loop assembly constraints. Closed vector loops describe the small kinematic adjustments that occur at assembly time. Open loops describe critical clearances or other assembly features. The DLM uses linearized assembly constraints and matrix algebra to estimate the variations of the assembly or kinematic variables, and to predict assembly rejects. A modified Monte Carlo simulation, employing an iterative technique for closed loop assemblies, was applied to the same problem set. The results of the comparison show that the DLM is accurate if the tolerances are relatively small compared to the nominal dimensions of the components, and the assembly functions are not highly nonlinear. Sample size is shown to have great influence on the accuracy of Monte Carlo simulation.

Some Results on the Behavior of Alternate Covariance Structure Estimation Procedures in the Presence of Non-Normal Data

1989 ◽  
Vol 26 (2) ◽  
pp. 214-221 ◽  
Author(s):  
Subhash Sharma ◽  
Srinivas Durvasula ◽  
William R. Dillon
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Covariance Structure ◽  
Standard Errors ◽  
Parameter Estimates ◽  
Simulation Experiments ◽  
Estimation Techniques ◽  
Structure Estimation ◽  
Estimation Procedures ◽  
Simulation Results

The authors report some results on the behavior of alternative covariance structure estimation procedures in the presence of non-normal data. They conducted Monté Carlo simulation experiments with a factorial design involving three levels of skewness, three level of kurtosis, and three different sample sizes. For normal data, among all the elliptical estimation techniques, elliptical reweighted least squares (ERLS) was equivalent in performance to ML. However, as expected, for non-normal data parameter estimates were unbiased for ML and the elliptical estimation techniques, whereas the bias in standard errors was substantial for GLS and ML. Among elliptical estimation techniques, ERLS was superior in performance. On the basis of the simulation results, the authors recommend that researchers use ERLS for both normal and non-normal data.

Monte Carlo simulation of classical swine fever epidemics and control

2005 ◽  
Vol 108 (3-4) ◽  
pp. 199-205 ◽  
Author(s):  
S. Karsten ◽  
G. Rave ◽  
J. Krieter
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Classical Swine Fever ◽  
And Control
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