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Queuing Theory Queuing Theory Jackson Networks Jackson Networks

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Queuing Theory Jackson Networks. Network Model. Consider a simple 3 stage model where the output of 2 queues becomes the input process for a third. Station A. Station C. Station B. Network Model. Station A. Station C. Proposition 1: rate in = rate out, that is, if l A and l B - PowerPoint PPT Presentation

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Page 1: Queuing Theory  Jackson Networks

Queuing Theory Queuing Theory Jackson Networks Jackson Networks

Page 2: Queuing Theory  Jackson Networks

Network Model Consider a simple 3 stage model where the output of 2 queues becomes the input process for a third

Station A

Station B

Station C

Page 3: Queuing Theory  Jackson Networks

Network Model Station A

Station B

Station C

Proposition 1: rate in = rate out, that is, if A and are the input rates for station A and B respectively, then the input rate for station C is A+B.

Proposition 2: exponential inter-arrivals at A and B provide exponential inter-arrivals at station C.

Page 4: Queuing Theory  Jackson Networks

Network Model Station A

Station B

Station C

Instructor Derivation of the minimum of 2 exponentials.

ddistributellyexponentiaXXprovided

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21

)(21

,,

}),{min( 2121

Page 5: Queuing Theory  Jackson Networks

Jackson Network Station 1

Station v

Station j

1

2

s1

1

1

2

sv

1

2

sj

j..

11 )1( j

)1( j

11 j

j

j

Page 6: Queuing Theory  Jackson Networks

Jackson Network• All external arrivals to each station must follow a Poisson

process (exponential inter-arrivals)• All service times must be exponentially distributed• All queues must have unlimited capacity• When a job leaves a station, the probability that it will go

to another station is independent of its past history and of the location of any other job

1. Calculate the input arrival rate to each station 2. Treat each station independently as an M/M/s queue

Page 7: Queuing Theory  Jackson Networks

Calculate Arrival Rates

istationtogoeskstationfromjobayprobabilit

istationatratearrivalexternal

istationatratearrivalcalculate

where

ki

i

i

k

m

kkiii

1

Page 8: Queuing Theory  Jackson Networks

Example

Jobs submitted to a computer center must first pass through an input processor before arriving at the central processor. 80% of jobs get passed on the central processor and 20% of jobs are rejected. Of the jobs that pass through the central processor, 60% are returned to the customer and 40% are passed to the printer. Jobs arrive at the station at a rate of 10 per minute. Calculate the arrival rate to each station.

Page 9: Queuing Theory  Jackson Networks

Example

10 .8 .4

.6.2

Input Central Printer

2.304.00

8008.0

1000010

23

12

1

33322311333

33222211222

33122111111

Page 10: Queuing Theory  Jackson Networks

Example (cont.)

• For the computer center the processing times are 10 seconds for the input processor, 5 seconds for the central processor, and 70 seconds for the printer. Our task is to determine the number of parallel stations (multiple servers) to have at each station to balance workload.

Page 11: Queuing Theory  Jackson Networks

Example

10 8 3.2Input Central Printer

6

10

// 1

sMM

12

8

// 2

sMM

7/6

2.3

// 3

sMM

For our initial try, we will solve for s1 = 2, s2 = 1, s3 = 4

Page 12: Queuing Theory  Jackson Networks

10 8 3.2Input Central Printer

6

10

2//

MM

12

8

1//

MM

7/6

2.3

4//

MM

Metrics Input Central PrinterModel M/M/2 M/M/1 M/M/4

10 8 3.2 6 12 6/7r 0.833 0.667 0.933po 0.091 0.333 0.009Wq 3.78 1.33 8.68Lq 0.378 0.167 2.71

Page 13: Queuing Theory  Jackson Networks

Metrics Input Central PrinterModel M/M/2 M/M/1 M/M/4

10 8 3.2 6 12 6/7r 0.833 0.667 0.933po 0.091 0.333 0.009Wq 3.78 1.33 8.68Lq 0.378 0.167 2.71

If these numbers are correct, clearly Lq and Wq indicate the bottleneck is at the printer station. We may wish to add a printer if speedy return of printouts is required. Secondarily, overall processing may be increased by adding another input processor.

Page 14: Queuing Theory  Jackson Networks

Job Shop Example

• An electronics firm has 3 different products in a job shop environment. The job shop has six different machines with multiple machines at 5 of the 6 stations. Product Order rate Flow 1 30/month ABDF 2 10/month ABEF 3 20/month ACEF

Summary information is on the network below.

Page 15: Queuing Theory  Jackson Networks

Job Shop Example

Machine A s = 3 = 25

Machine E s = 2 = 23

Machine D s = 3 = 11

Machine B s = 2 = 22

Machine F s = 4 = 20

Machine C s = 1 = 29

60A

Page 16: Queuing Theory  Jackson Networks

Job Shop Example

Machine A s = 3 = 25

Machine E s = 2 = 23

Machine D s = 3 = 11

Machine B s = 2 = 22

Machine F s = 4 = 20

Machine C s = 1 = 29

60A

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FFCEECDDCCCCBBCAACCC

FFBEEBDDBCCBBBBAABBB

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Page 17: Queuing Theory  Jackson Networks

Job Shop Example

Machine A s = 3 = 25

Machine E s = 2 = 23

Machine D s = 3 = 11

Machine B s = 2 = 22

Machine F s = 4 = 20

Machine C s = 1 = 29

60A

600)30/30()30/30(0000

30000)20/20()40/10(00

300000)40/30(00

2000000)60/20(0

4000000)60/40(0

6000000060

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Page 18: Queuing Theory  Jackson Networks

Job Shop Example

Machine A s = 3 = 25

Machine E s = 2 = 23

Machine D s = 3 = 11

Machine B s = 2 = 22

Machine F s = 4 = 20

Machine C s = 1 = 29

60A

91.0)20(2

40

22

40

r

s

B

Lets calculate metrics for Machine BM/M/2 model with

Use formulas in Fig. 16.5 p. 564 or use charts

Page 19: Queuing Theory  Jackson Networks

91.0r

04.op

Page 20: Queuing Theory  Jackson Networks

91.0r

4.10L

Page 21: Queuing Theory  Jackson Networks

Machine A s = 3 = 25

Machine E s = 2 = 23

Machine D s = 3 = 11

Machine B s = 2 = 22

Machine F s = 4 = 20

Machine C s = 1 = 29

60A

Repeat for Machines A, C, D, E, F

Metrics A B C D E FModel M/M/3 M/M/2 M/M/1 M/M/3 M/M/2 M/M/4

60 40 20 30 30 60 25 22 29 11 23 20r 0.800 0.909 0.690 0.909 0.652 0.750po 0.06 0.04 0.31 0.02 0.21 0.042Wq 0.043 0.216 0.077 0.278 0.032 0.025Lq 2.589 8.658 1.533 8.332 0.965 1.528W 0.083 0.262 0.111 0.369 0.076 0.075L 4.989 10.476 2.222 11.059 2.27 4.528

Page 22: Queuing Theory  Jackson Networks

Interesting Application to ManufacturingMetrics A B C D E FModel M/M/3 M/M/2 M/M/1 M/M/3 M/M/2 M/M/4

60 40 20 30 30 60 25 22 29 11 23 20r 0.800 0.909 0.690 0.909 0.652 0.750po 0.06 0.04 0.31 0.02 0.21 0.042Wq 0.043 0.216 0.077 0.278 0.032 0.025Lq 2.589 8.658 1.533 8.332 0.965 1.528W 0.083 0.262 0.111 0.369 0.076 0.075L 4.989 10.476 2.222 11.059 2.27 4.528

Note that lead time is just the time in the system which for product 1 which has sequence ABDF is

W = total time in system = lead time = WA + WB + WD + WF = .789

WIP = work in process = parts per month x lead time = 30(.789) = 23.67

Page 23: Queuing Theory  Jackson Networks

Machine A s = 3 = 25

Machine E s = 2 = 23

Machine D s = 3 = 11

Machine B s = 2 = 22

Machine F s = 4 = 20

Machine C s = 1 = 29

60A

Repeat for Machines A, C, D, E, F

Monthly Lead QueueProduct Orders Sequence Time Time WIP

1 30 ABDF 0.789 0.562 23.672 10 ABEF 0.496 0.316 4.963 20 ACEF 0.345 0.177 6.9

Page 24: Queuing Theory  Jackson Networks

Some Recommendations Metrics A B C D E FModel M/M/3 M/M/2 M/M/1 M/M/3 M/M/2 M/M/4

60 40 20 30 30 60 25 22 29 11 23 20r 0.800 0.909 0.690 0.909 0.652 0.750po 0.06 0.04 0.31 0.02 0.21 0.042Wq 0.043 0.216 0.077 0.278 0.032 0.025Lq 2.589 8.658 1.533 8.332 0.965 1.528W 0.083 0.262 0.111 0.369 0.076 0.075L 4.989 10.476 2.222 11.059 2.27 4.528

If lead time is too slow (WIP too high), one possibility is to add additional machining to the bottleneck area. This occurs at stations D and B. Note that this assumes the cost of the machines is not a critical part of the decision. This may or may not need to be included in a final recommendation.