A discrete-time queue Markov Chain problem

In summary: &\frac{1}{4} & \frac{1}{4} \\\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\\frac{1}{4} & \
  • #1
Joe1998
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The following problem is seriously tricky and I urgently need help with it, thanks.

phpCahCVW.png




For part a: we have the following transition probability matrix

P = a0 a1 a2 a3

a0 a1 a2 a3

0 a0 a1 b2

0 0 a0 b1

Now, is a0 = a1 = a2 = a3 = 1/4? And is b1 = 1/4 whereas b2 = 1/4 + 1/4 = 1/2?

Note that bk = P(An ≥ 4) = ∑ aj (Where the sum is over all j≥ K) ; and aj = P(An = j) , j = 0,1,2,3.



For part b: is it just (m0j(10) / 11) = (m0j(10) / 11) and (m1j(10) / 11), etc.? Of course we calculate P10, right?



For part c: Does that mean we are dealing with Xn = 4? And to calculate the expected time until the buffer reaches its capacity for the first time, then do we just use the formula (mij(n) / n+1)? If yes, then at what values of i,j and n do we look at? E.g., is it m04(10) / 10+1, or...?



For part d: Since in the question it says that "if arrivals exceed the buffer capacity, then the excess is lost", then I understand that Yn is representing that waste. Now, do we calculate the transition matrix for Yn or what? Because I seriously have no idea how to even solve this question, so any quick help would be really appreciate it because I have very limited time left.

Thanks for your help, I really appreciate it.
Kind regards
 
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  • #2
It's virtually unreadable. You have five states, therefore the transition matrix is ##5\times 5##. When you have a candidate for the transition matrix, check it's a right stochastic matrix, otherwise your result would be wrong.

I do not have the mental capacity required to decipher the rest of this :nb) My condolences to your grader. Please, format it with TeX, it's not implemented without reason.

Your being in a hurry is irrelevant. It is your responsibility to get things done on time.
 
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  • #3
nuuskur said:
It's virtually unreadable. You have five states, therefore the transition matrix is ##5\times 5##. When you have a candidate for the transition matrix, check it's a right stochastic matrix, otherwise your result would be wrong.

I do not have the mental capacity required to decipher the rest of this :nb) My condolences to your grader. Please, format it with TeX, it's not implemented without reason.

Your being in a hurry is irrelevant. It is your responsibility to get things done on time.
Do you have any idea how to approach part c or d?
 
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  • #4
Joe1998 said:
Do you have any idea how to approach part c or d?
Yes, start by showing your answer for (a): I'll help by giving you a template to amend:
$$
\begin{bmatrix}
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\
\end{bmatrix}
$$
 
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  • #5
Yeah I know how to do parts a and b, I have already done them.

Part a:
1/4 1/4 1/4 1/4 0

0 1/4 1/4 1/4 1/4

0 0 1/4 1/4 1/2

0 0 0 1/4 3/4

0 0 0 0 1

Part b:
I just calculate the sum of P^m for m = 0,1,2,3,4,5,6,7,8,9,10 and then add up the first row.

Now, I am stuck at parts c and d, so any help would ne appreciate it.
 
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  • #6
pbuk said:
Yes, start by showing your answer for (a): I'll help by giving you a template to amend:
$$
\begin{bmatrix}
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\
\end{bmatrix}
$$
If you can't work out how to copy and amend that template, copy the code below into your message.
Code:
$$
\begin{bmatrix}
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & 0 \\
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\
\end{bmatrix}
$$

Only the first row of your answer to (a) is correct.
 
  • #7
pbuk said:
If you can't work out how to copy and amend that template, copy the code below into your message.
Code:
$$
\begin{bmatrix}
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & 0 \\
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\
\frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} & \frac{1}{4} \\
\end{bmatrix}
$$

Only the first row of your answer to (a) is correct.
Wait, why only my first row is correct? I got it checked by my professor and he confirmed that it is correct! So why do you think it is not correct?
 
  • #8
Take for instance your last row representing the transitions from the state where the buffer contains 4 items immediately before dispatch (i.e. it is full) at time ## n ## . The next thing that happens is that an item is dispatched, so the buffer contains 3 items, and then {0..3} items arrive with equal probability. If 0 items arrive between times ## n ## and ## n + 1 ## (with probability ## \frac 1 4 ##) then there will still be only 3 items at time ## n + 1 ##.
 
  • #9
pbuk said:
Take for instance your last row representing the transitions from the state where the buffer contains 4 items immediately before dispatch (i.e. it is full) at time ## n ## . The next thing that happens is that an item is dispatched, so the buffer contains 3 items, and then {0..3} items arrive with equal probability. If 0 items arrive between times ## n ## and ## n + 1 ## (with probability ## \frac 1 4 ##) then there will still be only 3 items at time ## n + 1 ##.
Ok so let's not waste time on parts a and b, because I have checked with my professor and said they are correct, so all what I am asking is if you could give me some help, tips and guidance regarding how to do parts c and d, thanks.
 
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  • #10
Joe1998 said:
Ok so let's not waste time on parts a and b, because I have checked with my professor and said they are correct, so all what I am asking is if you could give me some help, tips and guidance regarding how to do parts c and d, thanks.
For part c), in general, if something happens for the first time in phase ##n##, then it didn't happen in phases ##1## to ##n -1## and then did happen. That suggests an interative or inductive approach here.

I think this is now too advanced to avoid Latex. I have a particular inability to read unformatted mathematics.
 
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  • #11
PeroK said:
For part c), in general, if something happens for the first time in phase ##n##, then it didn't happen in phases ##1## to ##n -1## and then did happen. That suggests an interative or inductive approach here.

I think this is now too advanced to avoid Latex. I have a particular inability to read unformatted mathematics.
Ok, what about part d, how to go about it?
Thanks
 
  • #12
Joe1998 said:
Ok, what about part d, how to go about it?
What about following the expansive hint in the question?
 
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  • #13
PeroK said:
What about following the expansive hint in the question?
Yeah cool, thanks.
 

FAQ: A discrete-time queue Markov Chain problem

What is a discrete-time queue Markov Chain problem?

A discrete-time queue Markov Chain problem is a mathematical model used to analyze the behavior of a queueing system over time. It involves a sequence of events that occur at discrete time intervals, where the state of the system at each time interval is dependent only on the previous state and not on any earlier states.

What are the main applications of discrete-time queue Markov Chain problems?

Discrete-time queue Markov Chain problems are commonly used in the fields of operations research, computer science, and telecommunications to study queueing systems such as call centers, computer networks, and transportation systems. They are also used in the analysis of inventory management and supply chain systems.

How is a discrete-time queue Markov Chain problem solved?

The solution to a discrete-time queue Markov Chain problem involves determining the steady-state probabilities of the system, which represent the long-term behavior of the queue. This can be done using various methods such as the matrix-geometric method, the recursive method, or the Fourier transform method.

What are the key assumptions made in a discrete-time queue Markov Chain problem?

The key assumptions made in a discrete-time queue Markov Chain problem include the assumption of a finite number of states, the assumption of a first-in-first-out (FIFO) queue discipline, and the assumption of a constant arrival rate and service rate. These assumptions allow for the simplification of the model and make it easier to analyze.

What are the limitations of using a discrete-time queue Markov Chain problem?

One of the main limitations of using a discrete-time queue Markov Chain problem is that it assumes a stationary system, meaning that the arrival and service rates do not change over time. In real-world scenarios, these rates may vary, which can affect the accuracy of the model. Additionally, the assumptions made in the model may not always hold true, leading to potential inaccuracies in the results.

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