9

The source of uncertainty is usually customer demand, travel time, service time at the location (during pick up or serving the customer), or presence of the customer (customers may not be available to receive their orders). As mentioned in the paper: Capacitated vehicle routing problem with stochastic demand has been by far the most studied version of the ...


7

Yes it matters -- the extent varies by several factors. Applications & Impact In many contexts using stochastic process-based models, one is well-served to use time-varying models to capture the time-dependent behavior of the system. And sometimes close enough, $I(t)\approx 1$ is good enough. In healthcare, appointment-based systems often see ...


6

Unfortunately, much of the performance analysis and transient approximations for time-varying systems with non-Markovian (non-exponential) properties are not easily obtained in book form (see note at bottom). This answer lists some books that don't require measure theory. Some Queueing & Renewal theory books: (non-measure theoretic) Probability, ...


4

SecretAgentMan has given some specific examples of cases where over- and underdispersion will affect outcomes. I thought it might complement that answer to generalise those examples to some general principles about when over/under-dispersion is likely to happen. A Poisson process typically represents a count of many low-probability events, each of which is ...


4

I enjoyed Performance Modeling and Design of Computer Systems: Queueing Theory in Action (Amazon link) by Mor Harchol-Balter, which sounds like it fits your bill pretty well. I have it on my desk. ISBN-13: 978-1107027503


4

I have used Stochastic Modeling: Analysis and Simulation by Barry Nelson and found it to be a pretty gentle introduction. It covers stochastic processes, queuing, and simulation.


4

After having read Chapter 5.3 of Decision Making Under Uncertainty by Mykel J. Kochenderfer, I have come to some conclusions. We are dealing with model uncertainty, in which case we can formulate a Bayes Adaptive Model. In the book that I read, the term model uncertainty refers more to not knowing what the transition probabilities nor the structure of the ...


2

Note that $V(O)$ is simply of the form $\sf Q_1/L_1$ where $\sf Q_1$ is a quadratic and $\sf L_1$ is a linear function of $p$. This can be written as ${\sf{L_2}}+c/\sf{L_1}$ where $\sf L_2$ is also linear in $p$ and $c$ is a constant. Letting ${\sf L_1}:=mp+n$, the second derivative becomes $$V''(O)=c[(mp+n)^{-1}]''=-cm[(mp+n)^{-2}]'=\frac{2cm^2}{(mp+n)^3}$$ ...


2

I learned from Quantitative System Performance Computer System Analysis Using Queueing Network Models by Lazowska, et.al. Unfortunately, it is no longer published, but it is available for free online. It may seem a bit out of date today but it is considered the classic for queueing network analysis of computer performance. It does not really cover the ...


2

Introduction to queueing theory and stochastic teletraffic models$^1$. The aim of this textbook is to provide students with basic knowledge of stochastic models that may apply to telecommunications research areas, such as traffic modeling, performance evaluation, resource provisioning, and traffic management. These research areas are included in a ...


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