simulation of end-of-life computer recovery operations design team jordan akselrad, john marshall...
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Simulation of End-of-Life Computer Recovery Operations
Design TeamJordan Akselrad, John MarshallMikayla Shorrock, Nestor Velilla
Nicolas Yunis
Project AdvisorProf. James Benneyan
Project SponsorProf. Sagar Kamarthi
Background Information Research project by sponsor, Professor Kamarthi
Sensors are being developed for computer components
Sensor Embedded Computers (SEC)
Sensors closely estimate remaining useful life
Product Recovery Facilities (PRF) exist that refurbish
computers
Ongoing research to determine sensors’ impact on
entire reclamation process
B A C K G R O U N D
Project Scope Determine the effect expected component life information has on a Product
Recovery Facility
S C O P E
Project Goal
Develop a simulation tool which models Product Recovery Facilities
Comparative model analysis
Apply optimization techniques across simulation model
Determine if sensors improve the cost effectiveness of computer recovery operations
S C O P E
Refurbishing Process
S C O P E
Design Concepts Considered
ARENA
Complex logic needs to be implemented
Excel Interface
Amount of data is overwhelming to user
Event Based Simulation
Unnecessary due to lack of queuing
S I M U L A T I O N
Simulation Design Custom user interface
C# / .Net backend Serves as window into simulation Assists in debugging model Rapid development, run anywhere
Human Factors Considerations Simple Interface with powerful capabilities
Easy to run large scale experiments Data easily importable / exportable Built in graphing for real-time analysis
S I M U L A T I O N
Price Generation
Arbitrary computer configurations
Each price contributor given a
weight to influence score
Weights solved to maximize price
vs. score correlation
Generated equation used to price
dynamically
S I M U L A T I O N
Comp (Ci) Weight (Wi)
CPU 215.4
Cores 817.4
Memory 0.4
HD 4.1
Gfx 116.3
Score vs Price
y = 5.6625x + 1137.3R2 = 0.8065
0
1000
2000
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0 100 200 300 400 500 600 700 800
Price
Sco
re
Simulation Demo
S I M U L A T I O N
Sensor Times Benefit
SensorsNo Sensors
Min
ute
s p
er
Co
mp
on
en
t
A N A L Y S I S
Profit Contributors
A N A L Y S I S
Design of Experiments 2 level, 10 Factor Experiment
1024 Combinations, 15 Runs each
Output for 3 performance objectives
Profit, Waste, Reliability
Minitab used for analysis
Variable interactions examined
Approximation equations developed
Efficient set extracted
A N A L Y S I S
Purchasing Costs
Interaction of Profit Factors
A N A L Y S I S
Purchasing costs have the greatest effect on profit
Reliability Analysis
Per
cen
t o
f C
om
po
nen
ts F
aile
d
War
ran
ty
Sensor Error in Months
Warranty Failure vs Sensor Error
A N A L Y S I S
Without sensors 23% failure rate
Failure rate increasing with sensor error
Estimating Life: Without Sensors
O P T I M I Z A T I O N
Dispose if probability component working in one year is less than tolerance
Profit vs Tolerance
Optimal tolerance 54%
Estimating Life: With Sensors
Per
cen
t P
rofi
t
Correction of Sensor
Profit vs Sensor Correction
O P T I M I Z A T I O N
Expected life reported with mean at failure date
Sensor error is in months of deviation from mean, default 6
Sensor reading is corrected to prevent warranty failures
Optimal profit at 1 deviation of correction
Maximize Profit Minimize Waste Maximize Reliability
Multi-Criteria Optimization
O P T I M I Z A T I O N
Surface is the efficient solution front
Efficient implies non-dominated trade-off between values
Conclusions Fully developed simulation tool
Easy to use Exceeds research needs
Preliminary Analysis Performed Without sensors refurbishment is infeasible
23% failure rate With sensors
21% reduction in time spent per component 22% reduction in processing cost per component
Sensors strongly recommended Overall profit increase 48% Customer failure rate 3%
C O N C L U S I O N S
Future Considerations Improve MTBF data accuracy
Research shows MTBF specified by
manufacturer is unreliable
Ideas to enhance accuracy
Facilities record component failure rates
Sensors report failure time to manufacturer
Integration into facility Simulator used as a prediction engine
C O N C L U S I O N S
Questions
Thank you
Waste AnalysisSensor Error vs Working Disposals
Deviation of Sensor Error in Months
Per
cen
t o
f W
ork
ing
Co
mp
on
ents
Dis
po
sed
A N A L Y S I S
Without sensors 11% of disposed
components are working
Working components disposed
increases with sensor error
Sensor Cost Benefit
SensorsNo Sensors
Do
lla
rs p
er
Co
mp
on
en
t
A N A L Y S I S