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Healthcare: Consider a healthcare system that categorizes the possible overall state of health of the region into three possible states, say i = {1, 2, 3}. Let us say the state is 2 at time zero. The state can transition from state 1 only to state 2 (let p1,2 = 1 and p1,j = 0 if j ? 2). Similarly, the state can transition from state 3 only to state 2 (let p3,2 = 1 and let p3,j = 0 if j ? 2). However, from state 2, it can transition to state 3 with probability p2,3 > 0 and to state 1 with probability p2,1 = 1 ? p2,3 (let p2,2 = 0). Each time it reaches a state i, it spends a random amount of time, say Ti whose distribution is Fi, independent of all other events. There is some staffing decision that the healthcare system has to make. It would like to follow the policy of having ni members on the staff when the state is i. Assume that the cost per unit time that this system incurs is c(n, i) if the state is i and there are n members on the staff. (So, when we reach i, we start incurring costs at the rate of c(ni, i) under our current policy.) (a) Let ?i = E[Ti] and let {?i} be the stationary probabilities of the discrete time Markov Chain (DTMC) whose probability of immediately transitioning from any state i to any state j is given by pij. Find the long run rate at which this system incurs costs using the concept of Semi-Markov processes in Ross (2014). (b) For the DTMC mentioned above, solve for the stationary probabilities {?i}. Substitute these probabilities into the answer in (a). Let this long run rate be ?. (c) Let us now assume that there is a lead time of k time units involved before a change in the staffing can be implemented. That is, if the state changes from 1 to 2 now, the staffing level will change from n1 to n2 only k time units from now. Assume Ti ? k with probability 1 for all i; thus, the system will not change its state within k time units. So, when we reach state i from state j, we incur costs at the rate of c(nj, i) for the first k time units and costs at the rate of c(ni, i) subsequently until we leave state i. Let the long run rate at which this system incurs costs be denoted by ??. Derive a simple expression for ?? ? ?. Hint: Study the regenerative process defined by the entry of the system into state 2. (We could have also defined a regenerative process based on entry into state 1 or based on entry into state 3. However, state 2 entries offer a much simpler analysis.)

          Healthcare: Consider a healthcare system that categorizes the possible overall state of health
of the region into three possible states, say i = {1, 2, 3}. Let us say the state is 2 at time
zero. The state can transition from state 1 only to state 2 (let p1,2 = 1 and p1,j = 0 if j ? 2).
Similarly, the state can transition from state 3 only to state 2 (let p3,2 = 1 and let p3,j = 0
if j ? 2). However, from state 2, it can transition to state 3 with probability p2,3 > 0 and
to state 1 with probability p2,1 = 1 ? p2,3 (let p2,2 = 0). Each time it reaches a state i, it
spends a random amount of time, say Ti whose distribution is Fi, independent of all other
events. There is some staffing decision that the healthcare system has to make. It would like
to follow the policy of having ni members on the staff when the state is i. Assume that the
cost per unit time that this system incurs is c(n, i) if the state is i and there are n members
on the staff. (So, when we reach i, we start incurring costs at the rate of c(ni, i) under our
current policy.)

(a) Let ?i = E[Ti] and let {?i} be the stationary probabilities of the discrete time Markov
Chain (DTMC) whose probability of immediately transitioning from any state i to any
state j is given by pij. Find the long run rate at which this system incurs costs using
the concept of Semi-Markov processes in Ross (2014).

(b) For the DTMC mentioned above, solve for the stationary probabilities {?i}. Substitute
these probabilities into the answer in (a). Let this long run rate be ?.

(c) Let us now assume that there is a lead time of k time units involved before a change
in the staffing can be implemented. That is, if the state changes from 1 to 2 now, the
staffing level will change from n1 to n2 only k time units from now. Assume Ti ? k with
probability 1 for all i; thus, the system will not change its state within k time units.
So, when we reach state i from state j, we incur costs at the rate of c(nj, i) for the first
k time units and costs at the rate of c(ni, i) subsequently until we leave state i. Let
the long run rate at which this system incurs costs be denoted by ??. Derive a simple
expression for ?? ? ?. Hint: Study the regenerative process defined by the entry of the
system into state 2. (We could have also defined a regenerative process based on entry
into state 1 or based on entry into state 3. However, state 2 entries offer a much simpler
analysis.)
        
Show more…
Healthcare: Consider a healthcare system that categorizes the possible overall state of health
of the region into three possible states, say i = 1, 2, 3. Let us say the state is 2 at time
zero. The state can transition from state 1 only to state 2 (let p1,2 = 1 and p1,j = 0 if j ? 2).
Similarly, the state can transition from state 3 only to state 2 (let p3,2 = 1 and let p3,j = 0
if j ? 2). However, from state 2, it can transition to state 3 with probability p2,3 > 0 and
to state 1 with probability p2,1 = 1 ? p2,3 (let p2,2 = 0). Each time it reaches a state i, it
spends a random amount of time, say Ti whose distribution is Fi, independent of all other
events. There is some staffing decision that the healthcare system has to make. It would like
to follow the policy of having ni members on the staff when the state is i. Assume that the
cost per unit time that this system incurs is c(n, i) if the state is i and there are n members
on the staff. (So, when we reach i, we start incurring costs at the rate of c(ni, i) under our
current policy.)

(a) Let ?i = E[Ti] and let ?i be the stationary probabilities of the discrete time Markov
Chain (DTMC) whose probability of immediately transitioning from any state i to any
state j is given by pij. Find the long run rate at which this system incurs costs using
the concept of Semi-Markov processes in Ross (2014).

(b) For the DTMC mentioned above, solve for the stationary probabilities ?i. Substitute
these probabilities into the answer in (a). Let this long run rate be ?.

(c) Let us now assume that there is a lead time of k time units involved before a change
in the staffing can be implemented. That is, if the state changes from 1 to 2 now, the
staffing level will change from n1 to n2 only k time units from now. Assume Ti ? k with
probability 1 for all i; thus, the system will not change its state within k time units.
So, when we reach state i from state j, we incur costs at the rate of c(nj, i) for the first
k time units and costs at the rate of c(ni, i) subsequently until we leave state i. Let
the long run rate at which this system incurs costs be denoted by ??. Derive a simple
expression for ?? ? ?. Hint: Study the regenerative process defined by the entry of the
system into state 2. (We could have also defined a regenerative process based on entry
into state 1 or based on entry into state 3. However, state 2 entries offer a much simpler
analysis.)

Added by Cindy B.

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Elementary Statistics a Step by Step Approach
Elementary Statistics a Step by Step Approach
Allan G. Bluman 9th Edition
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Healthcare: Consider a healthcare system that categorizes the possible overall state of health of the region into three possible states, say i = {1, 2, 3}. Let us say the state is 2 at time zero. The state can transition from state 1 only to state 2 (let p1,2 = 1 and p1,j = 0 if j ≠ 2). Similarly, the state can transition from state 3 only to state 2 (let p3,2 = 1 and let p3,j = 0 if j ≠ 2). However, from state 2, it can transition to state 3 with probability p2,3 > 0 and to state 1 with probability p2,1 = 1 − p2,3 (let p2,2 = 0). Each time it reaches a state i, it spends a random amount of time, say Ti whose distribution is Fi, independent of all other events. There is some staffing decision that the healthcare system has to make. It would like to follow the policy of having ni members on the staff when the state is i. Assume that the cost per unit time that this system incurs is c(n, i) if the state is i and there are n members on the staff. (So, when we reach i, we start incurring costs at the rate of c(ni, i) under our current policy.) (a) Let μi = E[Ti] and let {πi} be the stationary probabilities of the discrete time Markov Chain (DTMC) whose probability of immediately transitioning from any state i to any state j is given by pij. Find the long run rate at which this system incurs costs using the concept of Semi-Markov processes in Ross (2014). (b) For the DTMC mentioned above, solve for the stationary probabilities {πi}. Substitute these probabilities into the answer in (a). Let this long run rate be θ. (c) Let us now assume that there is a lead time of k time units involved before a change in the staffing can be implemented. That is, if the state changes from 1 to 2 now, the staffing level will change from n1 to n2 only k time units from now. Assume Ti ≥ k with probability 1 for all i; thus, the system will not change its state within k time units. So, when we reach state i from state j, we incur costs at the rate of c(nj, i) for the first k time units and costs at the rate of c(ni, i) subsequently until we leave state i. Let the long run rate at which this system incurs costs be denoted by θ̂. Derive a simple expression for θ̂ − θ. Hint: Study the regenerative process defined by the entry of the system into state 2. (We could have also defined a regenerative process based on entry into state 1 or based on entry into state 3. However, state 2 entries offer a much simpler analysis.)
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healthcare-consider-healthcare-system-that-categorizes-the-possible-overall-state-of-health-of-thc-region-into-thrce-possible-states_-say-123-let-us-say-the-state-is-2-at-time-zero-the-state-71146

Healthcare: Consider a healthcare system that categorizes the possible overall state of health of the region into three possible states, say i = {1, 2, 3}. Let us say the state is 2 at time zero. The state can transition from state 1 only to state 2 (let p1,2 = 1 and p1,j = 0 if j ≠ 2). Similarly, the state can transition from state 3 only to state 2 (let p3,2 = 1 and let p3,j = 0 if j ≠ 2). However, from state 2, it can transition to state 3 with probability p2,3 > 0 and to state 1 with probability p2,1 = 1 − p2,3 (let p2,2 = 0). Each time it reaches a state i, it spends a random amount of time, say Ti whose distribution is Fi, independent of all other events. There is some staffing decision that the healthcare system has to make. It would like to follow the policy of having ni members on the staff when the state is i. Assume that the cost per unit time that this system incurs is c(n, i) if the state is i and there are n members on the staff. (So, when we reach i, we start incurring costs at the rate of c(ni, i) under our current policy.) (a) Let μi = E[Ti] and let {πi} be the stationary probabilities of the discrete time Markov Chain (DTMC) whose probability of immediately transitioning from any state i to any state j is given by pij. Find the long run rate at which this system incurs costs using the concept of Semi-Markov processes in Ross (2014). (b) For the DTMC mentioned above, solve for the stationary probabilities {πi}. Substitute these probabilities into the answer in (a). Let this long run rate be θ. (c) Let us now assume that there is a lead time of k time units involved before a change in the staffing can be implemented. That is, if the state changes from 1 to 2 now, the staffing level will change from n1 to n2 only k time units from now. Assume Ti ≥ k with probability 1 for all i; thus, the system will not change its state within k time units. So, when we reach state i from state j, we incur costs at the rate of c(nj, i) for the first k time units and costs at the rate of c(ni, i) subsequently until we leave state i. Let the long run rate at which this system incurs costs be denoted by θ̂. Derive a simple expression for θ̂ − θ. Hint: Study the regenerative process defined by the entry of the system into state 2. (We could have also defined a regenerative process based on entry into state 1 or based on entry into state 3. However, state 2 entries offer a much simpler analysis.)

Sri K.

a-manufacturer-has-one-key-machine-at-the-core-of-one-of-its-production-processes-because-of-heavy-use-the-machine-deteriorates-rapidly-in-both-quality-and-output-therefore-at-the-end-of-eac-95078

A manufacturer has one key machine at the core of one of its production processes. Because of heavy use, the machine deteriorates rapidly in both quality and output. Therefore, at the end of each week, a thorough inspection is done that results in classifying the condition of the machine into one of four possible states: State Condition 0 Good as new 1 Operable—minor deterioration 2 Operable—major deterioration 3 Inoperable—output of unacceptable quality After historical data on these inspection results are gathered, statistical analysis is done on how the state of the machine evolves from month to month. The following matrix shows the relative frequency of each possible transition from the state in one month to the state in the following month. State 0 1 2 3 0 0 p01 p02 p03 1 0 p11 p12 p13 2 0 0 p22 p23 3 0 0 0 1 Once the machine becomes inoperable it remains inoperable. Leaving the machine in this state would be intolerable, since this would shut down the production process, so the machine must be replaced. The new machine then will start off in state 0. The replacement process takes 1 week to complete so that production is lost for this period. The cost of the lost production (lost profit) is $c1, and the cost of replacing the machine is $c2, so the total cost incurred whenever the current machine enters state 3 is $c1 plus $c2. Even before the machine reaches state 3, costs may be incurred from the production of defective items. The expected costs per week from this source are as follows: State Expected Cost Due to Defective Items, $ 0 0 1 c3 2 c4 However, there also are other maintenance policies that should be considered and compared with this one. For example, perhaps the machine should be replaced before it reaches state 3. Another alternative is to overhaul the machine at a cost of $c5. This option is not feasible in state 3 and does not improve the machine while in state 0 or 1, so it is of interest only in state 2. In this state, an overhaul would return the machine to state 1. A week is required, so another consequence is $c1 in lost profit from lost production. It is desired to find the optimal maintenance policy? In other words, determine the optimal decisions to make when the machine is in state 0, 1, 2 and 3 that will keep the process running at a minimum cost? P01=0.8710, P02=0.0349, P11=0.7726, P12=0.1479, P22=0.4539, c1=2156, c2=4089, c3=1137, c4=2842, c5=1800

Dominador T.

q-consider-a-drilling-machine-in-a-factory-it-could-be-in-one-of-three-different-states-g-in-which-it-is-working-normally-making-good-parts-b-in-which-it-is-working-but-producing-bad-parts-o-73061

Q: Consider a drilling machine in a factory. It could be in one of three different states: G, in which it is working normally (making good parts); B in which it is working but producing bad parts; or D, in which it is under repair (down") and not making parts. Transitions can occur at times 0, 30 seconds, 60 seconds, etc. Assume that all transitions are governed by Bernoulli distributions. That is, each time at which a transition can occur it occurs with a probability determined by the origin and destination state, and not dependent on how long the system was in the origin state. When it is working or making bad parts, the machine performs an operation in exactly 30 seconds. Assume that when it is in one of these states and it changes state, it changes state before it makes the next part and that the change takes no time. That is, when it goes from G to B, it makes one bad part; and when it goes from G to D, it does not make a part. When the machine is making good parts, it could go to the bad part state or it could go to the downstate. The mean time until it leaves the good state is 20 minutes. When it leaves G, it goes to D 90% of the time and it goes to B 10% of the time. After it reaches the bad state, it stays in that state until it produces an average of 30 parts, and then it always goes to D. After the machine enters D, it stays there for a random length of time whose mean is 3 minutes, and then it always goes to G. Part 1: What are the probabilities of going from each state to each other state in one operation time? (Please answer with 3 decimals) (pij is the probability of going from state j to state I.) The solution will be released after this problem is finished. You will need the solution to complete the rest of the problem. pGG = pGB = pGD = pBG = pBB = pBD = pDG = pDB = pDD = Part 2 What is the steady-state probability of being in each state? (Please answer with 3 decimals) P(G) = P(B) = P(D) = Part 3 What is the production rate of good parts in parts/min? (Please answer with 3 decimals) = What is the production rate of bad parts in parts/min? (Please answer with 3 decimals) = The yield is the number of good parts produced divided by the total number of parts produced (or the production rate of good parts divided by the total production rate). What is the yield? (Please answer with 3 decimals) =

Sri K.


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Transcript

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00:01 Let's solve the given questions according to the question.
00:04 In part a, b is equal to 0 .08, and e is equal to 0 .025.
00:14 Confidence is equal to 95 %.
00:20 Alpha divided 2 is equals to 1 minus confidence divided by 2 is equal to 1 minus confidence divided by 2 is equal to 1 minus 0 .9 5 divided by 2 is equal to 1 minus 0 .9 5, divided 2.
00:32 2 is equal to 0 .025...
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