Abstract
With time schedule optimization, we have already considered problems in which the processing times were no longer known beforehand and were therefore modeled by random variables: Instead of a fixed value, we assumed a—in principle arbitrary—distribution. As a result, the quantities that are observed in the model (such as the overall completion time) are also random variables, and we are interested in statements regarding their distributions. This model can be extended for situations in which the observed quantities are obtained as the sum of a large number of independent random variables so that a transition from a discrete to a continuous model is advisable (here: a Wiener process).
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Marek Capiński and Tomasz Zastawniak. Mathematics for Finance: An Introduction to Financial Engineering. Springer, 2003.
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© 2014 Springer-Verlag Berlin Heidelberg
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Bungartz, HJ., Zimmer, S., Buchholz, M., Pflüger, D. (2014). Wiener Processes. In: Modeling and Simulation. Springer Undergraduate Texts in Mathematics and Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39524-6_6
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DOI: https://doi.org/10.1007/978-3-642-39524-6_6
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39523-9
Online ISBN: 978-3-642-39524-6
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