Warning: foreach() argument must be of type array|object, bool given in /var/www/html/web/app/themes/studypress-core-theme/template-parts/header/mobile-offcanvas.php on line 20

A machine in use is replaced by a new machine either when it fails or when it reaches the age of \(T\) years. If the lifetimes of successive machines are independent with a common distribution \(F\) having density \(f\), show that (a) the long-run rate at which machines are replaced equals $$ \left[\int_{0}^{T} x f(x) d x+T(1-F(T))\right]^{-1} $$ (b) the long-run rate at which machines in use fail equals $$ \frac{F(T)}{\int_{0}^{T} x f(x) d x+T[1-F(T)]} $$

Short Answer

Expert verified
The long-run rates at which machines are replaced and machines in use fail are: a) Replacement Rate: $$ \left[\int_{0}^{T} x f(x) d x+T(1-F(T))\right]^{-1} $$ b) Failure Rate: $$ \frac{F(T)}{\int_{0}^{T} x f(x) d x+T[1-F(T)]} $$

Step by step solution

01

Calculate the expected time until a machine is replaced

To find the expected time until a machine is replaced, we have to consider two possibilities: 1. The machine fails before reaching the age of \(T\) years. 2. The machine reaches the age of \(T\) years. For the first possibility, we need to find the expected time it takes for a machine to fail. This can be obtained by integrating the lifespan \(x\) multiplied by the density function \(f(x)\) from 0 to \(T\): $$ \int_{0}^{T} x f(x) d x $$ For the second possibility, the machine reaches the age of \(T\) years and is replaced. The probability that a machine reaches the age of \(T\) years is given by \((1 - F(T))\). Taking the weighted sum of these two possibilities gives the expected time until a machine is replaced: $$ E[T] = \int_{0}^{T} x f(x) d x + T(1-F(T)) $$
02

Calculate the long-run rate at which machines are replaced

The long-run rate at which machines are replaced is just the reciprocal of the expected time until a machine is replaced. So, $$ \text{Replacement Rate} = \frac{1}{E[T]} = \left[\int_{0}^{T} x f(x) d x+T(1-F(T))\right]^{-1} $$
03

Calculate the long-run rate at which machines in use fail

The long-run rate at which machines in use fail can be obtained by taking the product of the probability that a machine fails before reaching the age of \(T\) years and the long-run rate at which machines are replaced: $$ \text{Failure Rate} = F(T) \times \text{Replacement Rate} = \frac{F(T)}{\int_{0}^{T} x f(x) d x+T[1-F(T)]} $$ Thus, we have derived the long-run rates at which machines are replaced and machines in use fail as: a) Replacement Rate: $$ \left[\int_{0}^{T} x f(x) d x+T(1-F(T))\right]^{-1} $$ b) Failure Rate: $$ \frac{F(T)}{\int_{0}^{T} x f(x) d x+T[1-F(T)]} $$

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with Vaia!

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

Each time a certain machine breaks down it is replaced by a new one of the same type. In the long run, what percentage of time is the machine in use less than one year old if the life distribution of a machine is (a) uniformly distributed over \((0,2)\) ? (b) exponentially distributed with mean \(1 ?\)

Let \(h(x)=P\left(\sum_{i=1}^{T} X_{i}>x\right\\}\) where \(X_{1}, X_{2}, \ldots\) are independent random variables having distribution function \(F_{e}\) and \(T\) is independent of the \(X_{i}\) and has probability mass function \(P[T=n\\}=\rho^{n}(1-\rho), n \geqslant 0 .\) Show that \(h(x)\) satisfies Equation (7.53). Hint: Start by conditioning on whether \(T=0\) or \(T>0\).

For an interarrival distribution \(F\) having mean \(\mu\), we defined the equilibrium distribution of \(F\), denoted \(F_{e}\), by $$ F_{e}(x)=\frac{1}{\mu} \int_{0}^{x}[1-F(y)] d y $$ (a) Show that if \(F\) is an exponential distribution, then \(F=F_{e}\). (b) If for some constant \(c\), $$ F(x)=\left\\{\begin{array}{ll} 0, & x

Random digits, each of which is equally likely to be any of the digits 0 through 9 , are observed in sequence. (a) Find the expected time until a run of 10 distinct values occurs. (b) Find the expected time until a run of 5 distinct values occurs.

Let \(\left\\{N_{1}(t), t \geqslant 0\right\\}\) and \(\left[N_{2}(t), t \geqslant 0\right\\}\) be independent renewal processes. Let \(N(t)=\) \(N_{1}(t)+N_{2}(t)\) (a) Are the interarrival times of \(\\{N(t), t \geqslant 0\\}\) independent? (b) Are they identically distributed? (c) Is \(\\{N(t), t \geqslant 0\\}\) a renewal process?

See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.

Sign-up for free