Corpus ID: 11906519

The Stochastic Analysis of Investments in Industrial Plants by Simulation Models with Control of Experimental Error: Theory and Application to a Real Business Case

@inproceedings{Cassettari2010TheSA,
  title={The Stochastic Analysis of Investments in Industrial Plants by Simulation Models with Control of Experimental Error: Theory and Application to a Real Business Case},
  author={Lucia Cassettari and Pier Giuseppe Giribone},
  year={2010}
}
In literature, the applications of simulation to the stochastic analysis of investments do not often give a satisfactory result to, at least, two problems that can condition in a determinant way the validity of the analysis made. In synthesis: 1. no knowledge of the entity of the experimental error expressed in terms of Mean Square Pure Error (MSpe) 
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References

SHOWING 1-10 OF 13 REFERENCES
Risk analysis in investment appraisal based on the Monte Carlo simulation technique
Abstract:This work has been prepared for the purpose of presenting the methodology and uses of the Monte Carlo simulation technique as applied in the evaluation of investment projects to analyze andExpand
Design and Analysis of Experiment
  • J. V. Grice
  • Computer Science, Mathematics
  • Technometrics
  • 2000
TLDR
A designer reading Carter’s book will not learn much about reliability statistics, but a reliability engineer who already knows how to use statistical methods will gain some insight into the methods and practical problems of mechanical design. Expand
Design and Analysis of Experiment
TLDR
This research presents a probabilistic procedure to estimate the intensity of the response of the immune system to repeated exposure to carbon monoxide poisoning. Expand
Problems of simulation
A multi-purpose baking pan is formed from a bottom pan member having a convex bottom surface and a pair of side members having a number of pairs of spaced apart holes. First and second end pieces areExpand
Theoretical Development and Applications of the MSPE Methodology in Discrete and Stochastic Simulation Models Evolving in Replicated Runs
  • Journal of Engineering, Computing and Architecture Volume
  • 2008
Using Monte Carlo simulation to improve long-term investment decisions, The Appraisal
  • Journal Vol.68,
  • 2000
Simulation Experiments of Large Scale Production System
  • 1997
Optimal Length in O.R. Simulation Experiments of Large Scale Production System
  • Proc. IASTED International Symposium “Applied Modelling and Simulation” - AMS’82,
  • 1982
...
1
2
...