Home > Operation Research calculators > Queuing Theory M/M/1/N Queuing Model (M/M/1/K) example

2. Queuing Theory, M/M/1/N Queuing Model (M/M/1/K) example ( Enter your problem )
Algorithm and examples
  1. Formula
  2. Example-1: `lambda=8`, `mu=9`, `N=3`
  3. Example-2: `lambda=6`, `mu=7`, `N=3`
  4. Example-3: `lambda=1`, `mu=1.2`, `N=6`
  5. Example-4: `lambda=25`, `mu=40`, `N=12`
  6. Example-5: `lambda=1.5`, `mu=2.1`, `N=10`
  7. Example-6: `lambda=1/10`, `mu=1/4`, `N=5`
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

2. Example-1: `lambda=8`, `mu=9`, `N=3`
(Previous example)
4. Example-3: `lambda=1`, `mu=1.2`, `N=6`
(Next example)

3. Example-2: `lambda=6`, `mu=7`, `N=3`





Queuing Model = mm1n, Arrival Rate `lambda=6` per 1 hr, Service Rate `mu=7` per 1 hr, Capacity `N=3`

Solution:
Arrival Rate `lambda=6` per 1 hr and Service Rate `mu=7` per 1 hr (given)

Queuing Model : M/M/1/N

Arrival rate `lambda=6,` Service rate `mu=7,` Capacity `N=3` (given)


1. Traffic Intensity
`rho=lambda/mu`

`=(6)/(7)`

`=0.85714286`


2. Probability of no customers in the system
`P_0=(1-rho)/(1-rho^(N+1))`

`=(1-0.85714286)/(1-(0.85714286)^(3+1))`

`=(0.14285714)/(1-(0.85714286)^4)`

`=(0.14285714)/(0.46022491)`

`=0.31040724` or `0.31040724xx100=31.040724%`


3. Probability of N customers in the system
`P_N=rho^N*P_0`

`=(0.85714286)^3*0.31040724`

`=0.62973761*0.31040724`

`=0.19547511`


4. Average number of customers in the system
`L_s=rho/(1-rho) - ((N+1)*rho^(N+1))/(1-rho^(N+1))`

`=0.85714286/(1-0.85714286) - ((3+1)*(0.85714286)^(3+1))/(1-(0.85714286)^(3+1))`

`=0.85714286/0.14285714 - (4*(0.85714286)^4)/(1-(0.85714286)^4)`

`=6 - (4*(0.53977509))/(1-(0.53977509))`

`=6 - (2.15910037)/(0.46022490628904633)`

`=6 - 4.69140271`

`=1.30859729`


5. Effective Arrival rate
`lambda_e=lambda(1-P_N)`

`=6*(1-0.19547511)`

`=4.82714932`


6. Average number of customers in the queue
`L_q=L_s-(lambda_e)/(mu)=L_s-(lambda(1-P_N))/(mu)`

`=1.30859729-4.82714932/7`

`=0.61900452`


7. Average time spent in the system
`W_s=(L_s)/(lambda_e)=(L_s)/(lambda(1-P_N))`

`=(1.30859729)/(4.82714932)`

`=0.27109111` hr or `0.27109111xx60=16.26546682` min


8. Average Time spent in the queue
`W_q=(L_q)/(lambda_e)=(L_q)/(lambda(1-P_N))`

`=(0.61900452)/(4.82714932)`

`=0.12823397` hr or `0.12823397xx60=7.69403825` min


9. Utilization factor
`U=L_s-L_q`

`=1.30859729-0.61900452`

`=0.68959276` or `0.68959276xx100=68.959276%`



10. Probability that there are n customers in the system
`P_n=rho^n*P_0`

`P_n=(0.85714286)^n*P_0`

`P_1=(0.85714286)^1*P_0=0.85714286*0.31040724=0.26606335`

`P_2=(0.85714286)^2*P_0=0.73469388*0.31040724=0.2280543`

`P_3=(0.85714286)^3*P_0=0.62973761*0.31040724=0.19547511`

`P_4=(0.85714286)^4*P_0=0.53977509*0.31040724=0.1675501`

`P_5=(0.85714286)^5*P_0=0.46266437*0.31040724=0.14361437`

`P_6=(0.85714286)^6*P_0=0.39656946*0.31040724=0.12309803`

`P_7=(0.85714286)^7*P_0=0.33991668*0.31040724=0.1055126`

`P_8=(0.85714286)^8*P_0=0.29135715*0.31040724=0.09043937`

`P_9=(0.85714286)^9*P_0=0.2497347*0.31040724=0.07751946`

`P_10=(0.85714286)^10*P_0=0.21405832*0.31040724=0.06644525`


This material is intended as a summary. Use your textbook for detail explanation.
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2. Example-1: `lambda=8`, `mu=9`, `N=3`
(Previous example)
4. Example-3: `lambda=1`, `mu=1.2`, `N=6`
(Next example)





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