By Alan J. King, Stein W. Wallace
ISBN-10: 0387878165
ISBN-13: 9780387878164
Whereas there are numerous texts on tips on how to resolve and learn stochastic courses, this can be the 1st textual content to deal with simple questions about the best way to version uncertainty, and the way to reformulate a deterministic version in order that it may be analyzed in a stochastic surroundings. this article will be compatible as a stand-alone or complement for a moment path in OR/MS or in optimization-oriented engineering disciplines the place the teacher desires to clarify the place types come from and what the basic concerns are. The e-book is easy-to-read, hugely illustrated with plenty of examples and discussions. it will likely be compatible for graduate scholars and researchers operating in operations learn, arithmetic, engineering and similar departments the place there's curiosity in studying the way to version uncertainty. Alan King is a learn employees Member at IBM's Thomas J. Watson study middle in long island. Stein W. Wallace is a Professor of Operational learn at Lancaster collage administration college in England
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Extra resources for Modeling with Stochastic Programming
Sample text
What looks risky may not be risky at all! Almost all projects and activities are part of a larger portfolio. Risk must be measured at the portfolio level. Even projects with negative expectations may be profitable for a company! ) It is our view that much of the literature treating risk fails to capture the portfolio effect. The net result is a poor understanding of risk. Stochastic programming is well set up to model any kind of covariation. This is contrary to many other tools (but not contrary to all tools, of course).
Demand information for Thursday to Sunday arrives. This latter model still has seven time periods, but only three stages: Monday, Tuesday, and Thursday mornings (it is a question of taste if you also count the final “Summing up” as a stage). So we see how the arrival of information has been aggregated and the number of stages reduced. Finding a good trade-off between time periods and stages is often crucial when modeling as it has consequences for model quality, data collection, and solvability of the model.
Putting all the probability mass in the mean of the distribution is certainly neither right nor good. Alternatively, many prefer to use worst-case analysis to analyze a problem. They may work with intervals and come up with results that do not depend on the actual distributions. This might sound tempting, but in our view it is often not good modeling. Worst-case analysis over an interval will normally amount to putting all the mass in the endpoints of the interval, which we find to be a very peculiar distribution.
Modeling with Stochastic Programming by Alan J. King, Stein W. Wallace
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