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4. Queuing Theory, M/M/s Queuing Model (M/M/c) example ( Enter your problem )
Algorithm and examples
  1. Formula
  2. Example-1: `lambda=30`, `mu=20`, `s=2`
  3. Example-2: `lambda=10`, `mu=3`, `s=4`
  4. Example-3: `lambda=1` per 6 min, `mu=1` per 4 min, `s=2`
  5. Example-4: `lambda=15` per 1 hr, `mu=1` per 5 min, `s=2`
  6. Example-5: `lambda=20` per 8 hr, `mu=1` per 40 min, `s=3`
  7. Example-6: `lambda=1` per 5 hr, `mu=1` per 1 hr, `s=4`
Other related methods
  1. M/M/1 Model
  2. M/M/1/N Model (M/M/1/K Model)
  3. M/M/1/N/N Model (M/M/1/K/K Model)
  4. M/M/s Model (M/M/c Model)
  5. M/M/s/N Model (M/M/c/K Model)
  6. M/M/s/N/N Model (M/M/c/K/K Model)
  7. M/M/Infinity Model

3. M/M/1/N/N Model (M/M/1/K/K Model)
(Previous method)
2. Example-1: `lambda=30`, `mu=20`, `s=2`
(Next example)

1. Formula





Queuing Model : M/M/s

Arrival rate `lambda,` Service rate `mu,` Number of servers `s`


1. Traffic Intensity
`rho=lambda/mu`


2. Probability of no customers in the system
`P_0=[sum_{n=0}^(s-1) (rho^n)/(n!) + (rho^s)/(s!)*(s mu)/(s mu-lambda)]^(-1)`


3. Average number of customers in the queue
`L_q=((rho^s)/((s-1)!)*(lambda mu)/(s mu-lambda)^2)*P_0`


4. Average Time spent in the queue
`W_q=L_q/lambda`

Or
`W_q=((rho^s)/((s-1)!)*(mu)/(s mu-lambda)^2) * P_0`


5. Average number of customers in the system
`L_s=L_q+rho`


6. Average Time spent in the queue
`W_s=L_s/lambda`

Or
`W_s=W_q+1/mu`


7. Utilization factor
`U=rho/s=(lambda)/(s mu)`

Or
`U=(L_s-L_q)/s`


8. Probability that there are n customers in the system
`P_n={((rho^n)/(n!)*P_0, "for "0<=n< s),((rho^n)/(s!*s^(n-s))*P_0, "for "n>=2):}`




This material is intended as a summary. Use your textbook for detail explanation.
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3. M/M/1/N/N Model (M/M/1/K/K Model)
(Previous method)
2. Example-1: `lambda=30`, `mu=20`, `s=2`
(Next example)





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