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Transcript of SMA 6304/MIT2.853/MIT2.854 Manufacturing Systems Lecture 19-20: Single-part-type, multiple stage...
![Page 1: SMA 6304/MIT2.853/MIT2.854 Manufacturing Systems Lecture 19-20: Single-part-type, multiple stage systems Lecturer: Stanley B. Gershwin Copyright @2002,](https://reader033.fdocuments.us/reader033/viewer/2022051819/551c504e5503467b488b5233/html5/thumbnails/1.jpg)
SMA 6304/MIT2.853/MIT2.854
Manufacturing Systems
Lecture 19-20: Single-part-type, multiple
stage systems
Lecturer: Stanley B. Gershwin
Copyright @2002, 2003 Stanley B. Gershwin
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Flow Line
also known as a Production or Transfer Line
Machine Buffer
Machine are unreliable
Buffers are finite
Copyright @2002, 2003 Stanley B. Gershwin
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Flow Line
Production output from a simulation of a transfer line.
Copyright @2002, 2003 Stanley B. Gershwin
week
Weekly Production
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Single ReliableMachine
Copyright @2002, 2003 Stanley B. Gershwin
If the machine is perfectly reliable, and its average operation time is τ, then its maximum production rate is 1/ τ.
Note:
Sometimes cycle time is used instead of operation time, but BEWARE: cycle time has two meanings!
The other meaning is the time a part spends in a system. If the system is a single, reliable machine, the two meanings are the same.
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Single ReliableMachine
ODFs
Copyright @2002, 2003 Stanley B. Gershwin
Operation-Dependent Failures
A machine can only fail while it is working.
Note:
This is the usual assumption.
IMPORTANT! MTTF must be measured in working time!
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Single ReliableMachine
Production rate
Copyright @2002, 2003 Stanley B. Gershwin
If the machine is unreliable, and
then its maximum production rate is
its average operation time is τits mean time to fail is MTTFits mean time to repair is MTTR
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Single ReliableMachine
Production rate
Proof
Copyright @2002, 2003 Stanley B. Gershwin All rights reserved
Average production rate, while machine is up, is 1/τ.
Average production during an up period is MTTF/τ
Average duration of an up period is MTTF.
Average duration of up-down period : MTTF+ MTTR.
Average production during up-down period : MTTF/τ
Therefore, average production rate is(MTTF/τ) / (MTTF+MTTR)
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Single ReliableMachine
Geometric up- and Down-Times
Copyright @2002, 2003 Stanley B. Gershwin
Assumptions: Operation time is constant (τ). Failure and repair times are geometrically distributed.
Let p be the probability that a machine fails during any given operation. Then p=τ/MTTF.
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Single ReliableMachine
Geometric up- and Down-Times
Copyright @2002, 2003 Stanley B. Gershwin
(Sometimes we forget to say “average”)
Then the average production rate of M is
Let γ be the probability that M gets repaired in during any operation time when it is down. Then
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Single ReliableMachine
Production rates
Copyright @2002, 2003 Stanley B. Gershwin
when it is up (short-term capacity),
When it is down (short-term capacity),
on the average (long-term
capacity)
So far, the machine really has three production rates:
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Infinite-BufferLine
Copyright @2002, 2003 Stanley B. Gershwin
Assumptions:
A machine is not idle if it is not starved.
The first machine never starved
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Infinite-BufferLine
Copyright @2002, 2003 Stanley B. Gershwin
The production rate of the line is the production rate of the slowest machine in the line- called bottleneck.
Slowest means least average production rate, where average production rate is calculates from one of the previous formulas.
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Infinite-BufferLine
Copyright @2002, 2003 Stanley B. Gershwin
Production rate is therefore
and Is the bottleneck.
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Infinite-BufferLine
Copyright @2002, 2003 Stanley B. Gershwin
The system is not in steady state.
An infinite amount of inventory accumulates in the buffer upstream of the bottleneck.
A finite amount of inventory appears downstream of the bottleneck.
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Infinite-BufferLine
Copyright @2002, 2003 Stanley B. Gershwin All rights reserved
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Infinite-BufferLine
Copyright @2002, 2003 Stanley B. Gershwin
The second bottleneck is the slowest machine upstream of the bottleneck. An infinite amount of inventory accumulates just upstream of it.
A finite amount of inventory appears between the second bottleneck and the machine upstream of the first bottleneck.
Et cetera.
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Infinite-BufferLine
Copyright @2002, 2003 Stanley B. Gershwin All rights reserved
A 10-machine line with bottlenecks at Machines 5 and 10.
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Infinite-BufferLine
Copyright @2002, 2003 Stanley B. Gershwin
Question: ◎ What are the slopes (roughly!) of the two indicated graphs?
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Copyright @2002, 2003 Stanley B. Gershwin
Infinite-BufferLine
Question:
◎ If we want to increase production rate, which machine should we improve?
◎What would happen to production rate if we improved any other machine?
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Copyright @2002, 2003 Stanley B. Gershwin
Zero-Buffer Line
◎ If any one machine fails, or takes a very long time to do an operation, all the other machines must wait.
◎ Therefore the production rate is usually less- possibly much less – than the slowest machine.
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Copyright @2002, 2003 Stanley B. Gershwin
Zero-Buffer Line
◎ Example: Constant, unequal operation times, perfectly reliable machines.
◎ The operation time of the line is equal to the operation time of the slowest machine, so the production rate of the line is equal to that of the slowest machine.
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Copyright @2002, 2003 Stanley B. Gershwin
Zero-Buffer Line
Constant, equal operation times, unreliable machines
◎ Assumption: Failure and repair times are geometrically distributed.
◎ Define pi=τ/MTTFi=probability of failure during an operation.
◎ Define ri=τ/MTTRi probability to repair during an interval of length τwhen the machine is down.
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Copyright @2002, 2003 Stanley B. Gershwin
Zero-Buffer Line
Constant, equal operation times, unreliable machines
Buzacott`s Zero-Buffer Line Formula:
Let K be the number of machines in the line. Then
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Copyright @2002, 2003 Stanley B. Gershwin
Constant, equal operation times, unreliable machinesZero-Buffer Line
◎ Same as the earlier formula (page6, page9) when K=1. The isolated production rate of a single machine Mi is
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Copyright @2002, 2003 Stanley B. Gershwin
Zero-Buffer LineProof of formula
◎ let τ(the operation time) be the time unit.
◎ Assumption: At most, one machine can be down. ◎ Consider a long time interval of length Tτduring which M
achine Mi fails mi times (i= 1, …, k)
◎ Without failures, the line would produce T parts.
All up Some machine down
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Copyright @2002, 2003 Stanley B. Gershwin
Proof of formulaZero-Buffer Line
◎ The average repair time of Mi is τ/τi each time it fail, so the total system down time is close to
where D is the number of operation times in which a machine is down.
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Copyright @2002, 2003 Stanley B. Gershwin
Zero-Buffer LineProof of formula
The total up time is approximately
where U is the number of operation times in which all machines are up.
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Copyright @2002, 2003 Stanley B. Gershwin
Zero-Buffer LineProof of formula
Note that, approximately,
because Mi can only fail while it is operational.
Since the system produces one part per time unit while it is working, it produces U parts during the interval of length Tτ.
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Copyright @2002, 2003 Stanley B. Gershwin
Zero-Buffer LineProof of formula
Thus,
or,
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Copyright @2002, 2003 Stanley B. Gershwin
Zero-Buffer LineProof of formula
and
Note that P is a function of the ratio pi/ri and not pi or ri separately.
The same statement is true for the infinite-buffer line.However, the same statement is not true for a line with finite, non-zero buffers
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Copyright @2002, 2003 Stanley B. Gershwin
Zero-Buffer LineProof of formula
Questions:
◎ If we want to increase production rate, which machine should we improve?
◎ What would happen to production rate if we improved any other machine.
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Copyright @2002, 2003 Stanley B. Gershwin
Zero-Buffer LineP as a function of pi
All machines are the same except Mi. As pi increases, the production rate decreases.
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Copyright @2002, 2003 Stanley B. Gershwin
P as a function of pi
Zero-Buffer Line
All machines are the same. As the line gets longer, the production rate decreases.
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Copyright @2002, 2003 Stanley B. Gershwin
Finite-BufferLine
Difficulty: * No simple formula for calculation production rate or inventory levels.
Solution: * Simulation * Analytical approximation
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Copyright @2002, 2003 Stanley B. Gershwin
Two Machine, Finite-BufferLine
Exact solution is available to Markov process model.Discrete time-discrete state Markov process:
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Copyright @2002, 2003 Stanley B. Gershwin
Two Machine, Finite-BufferLine
Here, where
is the number of parts in the buffer;
Is the repair state of
means the machine is up or operational;means the machine is down or under repair.
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Copyright @2002, 2003 Stanley B. Gershwin
Two Machine, Finite-BufferLine
Several models available: ◎ Deterministic processing time, or Buzacott model:
deterministic processing time, geometric failure and repair times; discrete state, discrete time.
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Copyright @2002, 2003 Stanley B. Gershwin All rights reserved
Two Machine, Finite-BufferLine
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Copyright @2002, 2003 Stanley B. Gershwin
Two Machine, Finite-BufferLine
◎Exponential processing time: exponential processing, failure, and repair time; discrete state, continuous time.
◎Continuous material, or fluid: deterministic processing, exponential failure and repair time; mixed state, continuous time.
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Copyright @2002, 2003 Stanley B. Gershwin All rights reserved
Two Machine, Finite-BufferLine
Deterministic Processing Time
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Copyright @2002, 2003 Stanley B. Gershwin All rights reserved
Two Machine, Finite-BufferLine
Deterministic Processing Time
Discussion: ◎Why are the curves increasing? ◎Why do they reach an asymptote? ◎What is P when N=0 ? ◎What is the limit of P as N→∞ ? ◎Why are the curves with smaller r1 lower?
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Copyright @2002, 2003 Stanley B. Gershwin All rights reserved
Two Machine, Finite-BufferLine
Deterministic Processing Time
Discussion: ◎Why are the curves increasing? ◎Why different asymptote? ◎What is n when N=0 ? ◎What is the limit of n as N→∞ ? ◎Why are the curves with smaller r1 lower?
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Copyright @2002, 2003 Stanley B. Gershwin All rights reserved
Two Machine, Finite-BufferLine
Deterministic Processing TimeDeterministic Processing Time
◎ What can you say about the optimal buffer size?
◎How should it be related to ri, pi ?
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Copyright @2002, 2003 Stanley B. Gershwin
Two Machine, Finite-BufferLine
Should we prefer short, frequent, disruptions or long, infrequent, disruptions?
And p1 vary together and
Answer: evidently, short, frequent failures.Why ?
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Copyright @2002, 2003 Stanley B. Gershwin
Two Machine, Finite-BufferLine
Questions:
◎ If we want to increase production rate, which machine should we improve?
◎ What would happen to production rate if we improved any other machine.
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Copyright @2002, 2003 Stanley B. Gershwin All rights reserved
Two Machine, Finite-BufferLine Production rate vs. storage spac
Improvements to non-bottleneck machine.
Machine 1 more improved
Machine 1 improved
Identical machines
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Copyright @2002, 2003 Stanley B. Gershwin All rights reserved
Two Machine, Finite-BufferLine Avg. inventory vs. storage spac
◎Inventory increases as the (non-bottleneck) upstream machine is improved and as the buffer space is increased.
◎ If the downstream machine were improved, the inventory would be less and it would increase much less as the space increases
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Copyright @2002, 2003 Stanley B. Gershwin
Two Machine, Finite-BufferLine Other models
Exponential – discrete material, continuous time
the probability that
the probability that
the probability that
completes an
operation in
operation in
Down, in
fails during an
is repaired, while it is
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Copyright @2002, 2003 Stanley B. Gershwin
Two Machine, Finite-BufferLine Other models
Exponential – continuous material, continuous time
the amount of material that Processes,
While it is up, in
the probability that
the probability that
fails, while it is up, in
Is repaired, while it is
down, in
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Copyright @2002, 2003 Stanley B. Gershwin All rights reserved
Two Machine, Finite-BufferLine Other models
Explain the shapes of the graphs.
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Two Machine, Finite-BufferLine Other models
Explain the shapes of the graphs.
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Two Machine, Finite-BufferLine Other models
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Long Lines
◎Difficulty:
* No simple formula for calculating production rate or inventory levels.
* State space is too large for exact numerical solution. * If all buffer sizes are N and the length of the line is k, the number of states is s =2 k (N+1)k-1. * if N = 10 and k=20, s = 6.41×1025
* Decomposition seems to work successfully.
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Long LinesDecomposition
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Long LinesDecomposition
◎ Consider an observer in buffer Bi. * Imagine the material flow that the observer sees entering and leaving the buffer.
◎We construct a two-machine line (ie, we find r1, p1, r2, p2, and N) such that an observer in its buffer will see almost the same thing.
◎ The parameters are chosen as functions of the behaviors of the other two-machine lines.
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Long LinesExamples
There-machine line-production rate.
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Long LinesExamples
There-machine line-total average inventory
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Long LinesExamples
Distribution of material in a line with identical machines and buffers. Explain the shape.
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Long LinesExamples
Effect of a bottleneck. Identical machines and buffers, except for M10.
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Long LinesExamples
continuous material model.Eight-machine,seven-buffer line.
For each machine,
For each buffer (except Buffer 6), N=30.
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Long LinesExamples
Which ni are decreasing and which are increas-ing?
Why ?
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Long LinesExamples
2 buffers equally sized.
8 buffers equally sized; and
Which has a higher production rate?
9-Machine line with two buffering options :
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Long LinesExamples
Continuous model; all machines have R=.019,
What are the asymptotes?
Is 8 buffers always faster ?
Total buffer Space
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Long LinesExamples
Is 8 buffers always faster ?
Perhaps not, but difference is not significant in systems with very small buffers.
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Long LinesOptimal buffer space distribution.
◎ Design the buffers for a 20-machine production line.
◎ The machine have been selected, and the only decision remaining is the amount of space to allocate for in- process inventory.◎The goal is to determine the smallest amount of in- process inventory space so that the line meets a production rate target.
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Long LinesOptimal buffer space distribution.
The common operation time is one operation per minute.
The target production rate is .88 parts per minute.
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Long LinesOptimal buffer space distribution.
Case 1 MTTF=200 minutes and MTTR =10.5 minutes for all machines (P=.95 parts per minute)
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Long LinesOptimal buffer space distribution.
Case 1 MTTF=200 minutes and MTTR =10.5 minutes for all machines (P=.95 parts per minute)
Case 2 Like Case 1 except Machine 5. For Machine 5, MTTF=100 and MTTR =10.5 minutes (P=.905 parts per minute)
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Long LinesOptimal buffer space distribution.
Case 1 MTTF=200 minutes and MTTR =10.5 minutes for all machines (P=.95 parts per minute)
Case 2 Like Case 1 except Machine 5. For Machine 5, MTTF=100 and MTTR =10.5 minutes (P=.905 parts per minute)
Case 3 Like Case 1 except Machine 5. For Machine 5, MTTF=200 and MTTR =21 minutes (P=.905 parts per minute)
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Long LinesOptimal buffer space distribution.
Are buffers really needed?
Yes. How were these numbers calculated?
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Long LinesOptimal buffer space distribution.
solution
Line Space
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Long LinesOptimal buffer space distribution.
Observation from studying buffer space allocation problems:
* Buffer space is needed most where buffer level variability is greatest
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Long LinesProfit as a function of buffer sizes
There-machine , continuous material line.