11
votes
Are optimization results stochastic?
Usually no. All solvers worth their salt can be deterministic, and most are by default. That is, given a fixed configuration and seed for their random number generator, they always give the same ...
- 1,842
9
votes
Accepted
Stochastic VRP: Sources of uncertainty and modeling approaches
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 ...
- 5,864
8
votes
Are optimization results stochastic?
I suspect that the answer to your question hinges more on how you understand the word stochastic than any particular insight about optimization.
Multiplicity of optima
In the example you gave, we have ...
- 524
7
votes
Good textbook for queueing theory and performance modeling
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 ...
- 1,885
7
votes
Accepted
Does dispersion really matter?
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 ...
- 1,885
6
votes
Are optimization results stochastic?
Results are only stochastic if your convergence criterion allows for that. If your convergence criterion is not met but you terminate anyway, there are no results to speak of, so this statement is ...
- 11.9k
5
votes
Are optimization results stochastic?
In the example you give, the problem is not stochastic, but the solution that a potential solver gives you, in case multiple optimal solutions do exist. That is an important difference. Deterministic ...
- 1,461
4
votes
Does dispersion really matter?
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 ...
4
votes
Good textbook for queueing theory and performance modeling
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.
...
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4
votes
Good textbook for queueing theory and performance modeling
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.
- 13.1k
4
votes
Model or State Uncertainty in Queueing Model due to uncertain arrival rate
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 ...
- 463
4
votes
Accepted
Optimization for Stochastic Simulations
Simulation Optimization is the name of the field you are looking for. It is closely related to, and overlaps with. Machine Learning, such as training Deep Learning models.
In Simulation Optimization, ...
- 13.1k
3
votes
Are optimization results stochastic?
In Gurobi, there is a parameter called seed which "typically leads to different solution paths". But the default value is ...
- 1,036
3
votes
Accepted
The impact of utilization rate of a queueing system on its average queue time
I do not agree with the assertion that "both high and low utilization rate mean we are going to have a long average queue time". If $\rho$ is low (close to 0), $E[QT]$ is close to 0. Low ...
- 37.8k
2
votes
Good textbook for queueing theory and performance modeling
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....
- 121
2
votes
Good textbook for queueing theory and performance modeling
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 ...
- 8,622
2
votes
Optimize probability parameter in an optimal control problem
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 ...
- 5,341
2
votes
How to verify the correctness of forecast?
Regarding when to be satisfied with your model, one thing to do is to plot the forecast residuals (predicted - actual) v. time and also v. actual. If either plot exhibits a pattern, either there is a ...
- 37.8k
1
vote
Are optimization results stochastic?
As @Max suggested, the answer depends on how we understand the word stochastic. For example, Lionel Pages pleads for the subjectivity of stochasticity, pointing at an ongoing scholarly debate.
In the ...
- 481
1
vote
How to verify the correctness of forecast?
I am not sure about your mentioned method to forecast what you want, but as an answer to the second question, there exist many other techniques to estimate forward on the planning horizon. So there ...
- 8,380
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