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Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
1
Modeling in OptimizationModeling in Optimization
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
2
ModelsModels
Questions:
What are “models”?
What is “modeling”?
What is a “good” model?
Questions:
What are “models”?
What is “modeling”?
What is a “good” model?
A model is a necessary ingredient to optimization.A model is a necessary ingredient to optimization.
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
3
Models in ORModels in OR
“The essence of OR lies in the construction and use of models.”
A model is a simplified representation of something real.
There are different types of models.
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
4
EcoSystem Landscape ModelsEcoSystem Landscape Models
Surface Water Height Difference (m) Between Release and Baseline due to Textile Mill in Location 2
Hydrology
Nutrients
DOM
Process Model Unit Model Spatial Landscape Model
Suppose we placed Interface’s factory in this ecosystem. What would happen?
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
5
Facility “Eco-Dash”Facility “Eco-Dash”
ProductionData
User-Configurable Dashboard
AccountingData
Cost Data
PhysicalData
Every shift Every month As info changes As requested
ABC+M&EData
Engineers'Data
PlantLevel
DataLevel
DisplayLevel
Every day
M E $
Total
Per Yd2
M
T
Every second
Report
EstimationsCalculations
Cost SheetsGeneralLedger
Amount/Style
Produced
A
GF
EDCB
H
SensorsCollectingActivity Data
Daily DriverUpdate
Note: F & GReside in aSingle Database
Dashboard Modules
SplashScreen
Sensor ABCEM
H
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
6
Paint Line ModelPaint Line Model
Purge VOC emissions by recovery % and batch size
5.06
3.80
3.04
2.532.17
1.901.69
3.04
2.28
1.821.52
1.301.14 1.011.01
0.76 0.61 0.51 0.43 0.38 0.340.00
1.00
2.00
3.00
4.00
5.00
6.00
0 1 2 3 4 5
Average Car Batch Size
To
tal
VO
C e
mis
sio
ns
(to
ns)
50% recovery
70% recovery
90% recovery
Paint Line Simulation Screenshots
ABCEM
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
7
Gear ManufacturingGear Manufacturing
Dry Hob
Heat Treat
Teeth Grind
Final Wash
Pre-HT Wash
Dry Hob
Teeth Grind
Teeth Grind
Gear Blanks
Finished GearChamfer
ChamferDry Hob
Gear Blanks
Face Grind
Bore Hone
Teeth Grind
Pre-Grind Wash
RollFace Grind
Finished GearFinal
WashHeat Treat
Bore Hone
BurnishPre-HT Wash
Dry Hob
Dry Hob
Dry Hob
Gear Blanks
Gear Blanks
Predictive Model
Inventory(1)
Conversion(2)
Types and Quantity
Potential Process
Machine Database
Intermediate Outputs
Part DesignEnvironmental InventoryEnvironmental ImpactsFinancial Costs
Facility Parameters
Front End Back End
Cost Database
Eco-Indicators Database
Hard FinishGreen FinishunitsEnvironmental SPS 313.140 146.789 mpt / partFinancial Cost 2.083 0.919 $ / partWater Use 3.940 3.138 gal / partLandfill Waste 0.000 0.000 lb / partRecyclable Material 0.287 0.284 lb / partSpecial Waste 0.401 0.000 lb / partEnergy 10.091 4.850 kWh / partCO2 13.482 6.480 lb / part
Mai
nIn
vent
ory
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
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Gears are part of this…Gears are part of this…
How do you “model” this?
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
9
Paper versus Plastic: B2B PackagingPaper versus Plastic: B2B Packaging
. Longbeach, CA
Detroit, MI .
From Shanghai, China
Transmission Part (aluminum)
New Packaging (plastic)
Regrind
Reprocessing into splash shields (parts)
Conventional Packaging (cardboard)
Modeling Interface Economic & Environmental Analysis Report
Data Library Total Cost Analysis
Life Cycle Analysis
Packaging Configuration
Part Configuration
Logistics Processes
Energy Consumption Analysis
MS Excel based decision support model
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
10
Rethinking Sourcing Options
Rethinking Sourcing Options
• Packaging work led to rethinking of where to source from
• Environmental Sourcing Tool helps assessing emissions from
– Production in different localities with different electrical generation emissions.
» E.g., hydro vs coal
– Transport modes and distances
1) Enter Energy/Part
2) Select Region (or US)
3) Select Country (or US State) 1
4) Total CO2 Emissions/Part
Country (state) CO2 Coefficient
1) Enter Transporation Data
Enter Weight per Conveyance
Enter Pieces per Conveyance
Enter Pieces per Unit Load
Enter Tare Weight per Unit Load
2) Select Transportation Modes (deselect for unused modes)TRUE
FALSE
FALSE3) Enter Total Mileage for Each Mode (miles)
By Truck 00
4) Inbound Trip Shipping Energy/partOutbound Trip Shipping Energy/partTotal Shipping Energy/part
5) Total CO2 Emissions/Part
Total Energy/part
Total CO2/part
Manufacturing 4,130.06 79
Transportation 309.39 15.35
Rank Source Country (or US State) Tot. Energy Tot. CO2 CO2 Coeff.
Saved CasesTo delete a case, select the entire row, right click, and select 'Delete'
MJ/part
(scroll up)
g/part
g/MJ
lbs
parts
parts
lbs
g/part
MJ/part
FEST
94.35
4439.45
15.35
24
1248
25409
25323453
79
4,130.06
52.28
158
Ford Environmental Sourcing Tool
Manufacturing Module
Transportation Module
150
3.6011.74
309.39
Case Results
MJ/part
MJ/part
MJ/part
g/part
North America
Canada
Truck
Train
Ship
CO2 Emmissions by Activity
(g of CO2/part)
0.001,000.002,000.003,000.004,000.005,000.00
Manufacturing Transportation
Energy Usage by Activity (MJ/part)
020406080
100
Manufacturing Transportation
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
11
Decision SupportDecision Support
Analysis & Decision Support Models &Tools
Component
Material
Urban Region
Ecosystem
Product
Industry
Material Flows
Recycling
Land FillingPhysical Systems
Remanufacturing
A Goal: Integration of data, models, knowledge & learning to support high impact decision-making
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
12
“The” Modeling Process (in OR)“The” Modeling Process (in OR)
Real system Model
Real conclusions Model conclusions
Formulation
Deduction
Interpretation
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
13
FormulationFormulation
Formulation:
• Often considered to be an art.
• Typical questions to answer in formulating a model:
What aspects of the real system should be included, which can be ignored?
What assumptions can and should be made?
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
14
DeductionDeduction
Deduction:
• Involves techniques that depend on the nature of the model.
• It may involve solving equations, running a computer program, expressing a sequence of logical statements – whatever it takes to solve the problem of interest relative to the model.
• It should not be subject to differences of opinion, provided that the assumptions are clearly stated and identified.
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
15
InterpretationInterpretation
Interpretation:
• Again involves a large amount of human judgment.
• The model conclusions must be translated to the real world conclusions, in full cognizance of possible discrepancies between the model and its real-world referent.
Remember:
Ties between model and system are often only at best ties of plausible association,
SO BE CAREFUL WITH THE CONCLUSIONS!!!!
Remember:
Ties between model and system are often only at best ties of plausible association,
SO BE CAREFUL WITH THE CONCLUSIONS!!!!
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
16
Validation – An IntroductionValidation – An Introduction
• The process of acquiring the conviction that a model actually works is commonly called validation.
• When people are persuaded or convinced that a model is useful in some basic context, they will speak of it as a valid model.
• However, the validity is often restricted to a certain context.
• Hence, it is vital to know the limitations of the model.
• Some people may never accept a model.
Validation is a considerably weaker term than "proof"Validation is a considerably weaker term than "proof"
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
17
The Scientific Method (in natural science)The Scientific Method (in natural science)
“Real” system Hypothesis
“Real” conclusions Theory
Inductive generalization
Verification
Application
Testing and revision
Models are invented – Theories are discoveredModels are invented – Theories are discoveredModels are invented – Theories are discoveredModels are invented – Theories are discovered
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
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How about design?How about design?
What is the difference between designing, modeling, and OR?
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
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Difference between OR and designingDifference between OR and designing
Model
Model conclusions
Formulation
Deduction
Interpretation
Real system
Real conclusions
build
and test
Change the system's design
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
20
Modeling and DesigningModeling and Designing
• Designing is very open-ended
• This openness is very unique
• Openness is troublesome, but a fact of life.
• Designing is very open-ended
• This openness is very unique
• Openness is troublesome, but a fact of life.
Ask yourself,
how would you model the process of configuring a general how would you model the process of configuring a general arrangement of parts, and solve it as a mathematical arrangement of parts, and solve it as a mathematical optimization problem?optimization problem?
• You have to model and deal with geometrical forms, type of motions, force transmissions, etc.
• Combining all these issues in a single model is almost impossible
Ask yourself,
how would you model the process of configuring a general how would you model the process of configuring a general arrangement of parts, and solve it as a mathematical arrangement of parts, and solve it as a mathematical optimization problem?optimization problem?
• You have to model and deal with geometrical forms, type of motions, force transmissions, etc.
• Combining all these issues in a single model is almost impossible
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
21
Types of DesignTypes of Design
Three types of design are often considered (e.g., Pahl and Beitz):
• Original Design – an original solution principle is determined for a desired system and used to create the design of a product.
• Adaptive Design – an existing design is adapted to different conditions or tasks; thus, the solution principle remains the same but the product will be sufficiently different so that it can meet the changed tasks that have been specified.
• Variant Design – the size and/or arrangement of parts or subsystems of the chosen system are varied. The desired tasks and solution principle are not changed.
Three types of design are often considered (e.g., Pahl and Beitz):
• Original Design – an original solution principle is determined for a desired system and used to create the design of a product.
• Adaptive Design – an existing design is adapted to different conditions or tasks; thus, the solution principle remains the same but the product will be sufficiently different so that it can meet the changed tasks that have been specified.
• Variant Design – the size and/or arrangement of parts or subsystems of the chosen system are varied. The desired tasks and solution principle are not changed.
Where do you think optimization is most used?Where do you think optimization is most used?Where do you think optimization is most used?Where do you think optimization is most used?
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
22
Modeling and Optimization in Design
Modeling and Optimization in Design
Shape optimization is relatively frequently done.
Configuration optimization is difficult.
Invariably, you have to account for hierarchical interactions.
Questions to be asked when modeling designs:
• Does the problem contain identifiable components?
• How are the components linked?
• Can we identify component variables and system variables?
• Does the system interact with other systems at the same level and/or higher levels?
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
23
Some Basic Principles of ModelingSome Basic Principles of Modeling
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
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Principles of ModelingPrinciples of Modeling
1. Do not build a complicated model when a simple one will suffice.
This can be contrary to traditional mathematics or when one wants to build a general, strong model. Typically, though, a useful model is a simple model.
2. Beware of molding the problem to fit the technique.
This happens often if somebody is an "expert" in a specific solution technique. However, optimization is just one of many decision support techniques.
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
25
Principles of ModelingPrinciples of Modeling
3. The deduction phase of modeling must be conducted rigorously.
If you do this and the conclusions are inconsistent with reality, then the fault lies in the assumptions.
Be extremely careful with computer programs which may contain hidden bugs.
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
26
Principles of ModelingPrinciples of Modeling
4. Models should be validated prior to implementation.
Some ways to do this:
• retrospective testing against historical data (especially for forecasting models).
• If the model is supposed to represent a class of things, test it against members of a class which was not used in the modeling (e.g., as in regression analysis).
• systematically vary parameters in the model and real system (if possible) and see whether the changes in behavior match.
• construct artificial tests, e.g., enter extreme values and see what happens (zero is always an interesting number to try).
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
27
Principles of ModelingPrinciples of Modeling
5. A model should never be taken too literally.
This sounds obvious, but is often forgotten as the model grows and is supposed to be more “accurate”
6. A models should neither be pressed to do, nor criticized for failing to do, that for which it was never intended.
Always remember and investigate the original context in which a model was made (e.g., a model for predicting the resistance of fishing vessels should not be used for aircraft carriers).
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
28
Principles of ModelingPrinciples of Modeling
7. Beware of overselling a model.
It is very tempting to state that your model can solve all problems in the world, but be truthful. Honesty goes a long way and keeps you out of lawsuits.
8. Some of the primary benefits of modeling are associated with the process of developing the model.
The model itself never contains the full knowledge and understanding of the real system that the builder must acquire in order to successfully model it.
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
29
Principles of ModelingPrinciples of Modeling
9. A model cannot be better than the information that goes into it.
Garbage In, Garbage Out (GIGO) is very applicable to modeling.
Also, models do not create information, but condense or convert information.
In some cases, instead of exerting one's efforts on model construction, one would be better off just gathering more information about the real system.
Be careful that you don't put in too much information.
Optimization in Engineering Design
Georgia Institute of TechnologySystems Realization Laboratory
30
Principles of ModelingPrinciples of Modeling
10.Models cannot replace decision makers
One of the most common misconceptions about the purpose of optimization and other OR models is that they are supposed to provide "optimal" solutions, free of human subjectivity and error.
There are so many decisions and assumptions to be made in the modeling, that only in a restricted and tight context we can speak of optimal solutions.
A human decision maker is always necessary, whether you like it or not.