thorsten pawletta & olaf hagendorf · 2017. 5. 30. · hochschule wismar - university of applied...
TRANSCRIPT
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Hochschule Wismar - University of Applied SciencesRG Computational Engineering & Automation (CEA)
Thorsten Pawletta & Olaf Hagendorf
Invited Talk at the Workshop on Trends in Computational Sciense (TCSE), 13th-14th Feb. 2012, in front of MATHMOD Conf., Vienna, 15th-17th Feb.2012
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1. Motivation2. Modular, hierarchical systems3. Modeling & simulation4. Manual sim. based system optimization (SSO)5. Semi-automatic SSO6. Full-automatic SSO
1. SES/MB framework2. Mapping of system structures3. Combination to a complete approach
7. Application exampleConclusion
2TCSE Workshop, Vienna, 2012/02
Contents
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Engineering systems can be implemented using different system designs and several strategies
⇒Set of system designs Any system design is a composition of systems &
systems are configured using parameters⇒Modular, hierarchical composition of systems (system
structure)⇒Set of system parameters for each system
An engineering objective: find the best system design
⇒Optimal system structure with optimal system parameters
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1. Motivation
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System types: Atomic system: non-decomposable systems
with dynamic behavior A=(X, Y, S, δext, δint, δcon, λ, ta) [ZPK_2000]
Coupled system: set of systems and relationsC=(X, Y, D, {Mdd∈D}, EIC, EOC, IC) [ZPK_2000]
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2. Modular, Hierarchical Systems
B FE CDA
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Modeling1. Specification of reusable models for atomic &
coupled systems => model base (libraries)2. Specification of a specific model (one system
structure with configurable system parameters )
Simulation (most simple experiment)3. Execution of a specific model within a simulation
runtime system
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3. Modeling & Simulation
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One cycle: eval. of one system design(structure, parameters)
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4. Manual Simulation Based System Optimization (SSO)
manual changesof parameters and
structures
system
model
executable model
modeling
programming
simulation
result OK?
yes
no
components steps
man
ual c
hang
es
solution
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Inner cycle: eval. of (structure , {parameters}) Outer cycle: eval. of ({structure}, {parameters}) 7TCSE Workshop, Vienna, 2012/02
5. Semi-Automatic Simulation Based System Optimization (SSO)
(Classic parameter optimization)
optimizationmethod fitness
function parameter changes
modeling
programming
simulation
result OK?
yes
noparameter optimized model
system
model
executable model
No
Yes
solution
components steps
result OK?
man
ual c
hang
es o
f mod
el s
truct
ur
simulationresults
performance
manual changes ofstructures
automatic changes ofparameters
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Current status Modular, hierarchical model of a single system design Simulation based evaluation Configurable system parameters Numerical parameter optimization approach
Additional requirements for full-automatic SSO Formal specification of all system designs
({system.structures}, {system parameters}) Automatic generation of models/executable models Mapping of {system structures} ⇆ {numerical data} for
a structure optimization equivalent to a param. optim.
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6. Full-Automatic Simulation Based System Optimization (SSO)
( )
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Pruned Entity StructurePruned Entity Structure executable model
Formal specification of all system designs & dynamics Automatic generation of single simulation models
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6. Full-Automatic Simulation Based System Optimization (SSO)
SES/MB
Model BaseSystem Entity Structure
{1,3}
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Tree = ({nodes}, {edges}, {attributes}) Entity node: atomic or coupled system◦ node attributes: system parameters
Aspect node: decomposition of a system◦ coupling specification
Multi-aspect node: specific decomposition of a system◦ properties
Specialization node: taxonomy of a system◦ selection rules
Selection constraints: interdependencies of systems
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6. Full-Automatic Simulation Based System Optimization (SSO)
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11TCSE Workshop, Vienna, 2012/02
6. Full-Automatic Simulation Based System Optimization (SSO)
System decomposition/composition
Adec
B CCdec1
F G
A
{(B.out,C.in)}
{(C.in,F.in),(F.out,G.in)}
{p1=42}
A
B
C
F G
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6. Full-Automatic Simulation Based System Optimization (SSO)
System decomposition/composition (2 variants)
Adec
B CCdec1 Cdec2
F G H I
A
{(B.out,C.in)}
{(C.in,H.in1),(H.out,I.in),(I.out,H.in2)}
{(C.in,F.in),(F.out,G.in)}
A
B
C
F G
A
B
C
H I
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6. Full-Automatic Simulation Based System Optimization (SSO)
Specific decomposition/composition
Adec
CDmaspec
{1,2,3}
D
A
{(..),..}
B
AD1 C
AD1
CD2
D3
A D1C
D2
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6. Full-Automatic Simulation Based System Optimization (SSO)
Specialization
Adec
CEspec
E1 E2 E3
A
{(..),..}
{selection rules}
E
AE1 C
AE2 C
AE3 C
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6. Full-Automatic Simulation Based System Optimization (SSO)
Selection constraints/Structure rules
Adec
CCdec1 Cdec2
F G H I
A
{(..),..}
{(..),..}{(..),..}Dmaspec
{1,2,3}
D
BEspec
E1 E2 E3
{selection rules}
E
without constraints/rules:18 variants
with constraints/rules:12 variants
constraints rules
≡ {((E2 ∩ H) ∪(E3 ∩ F) )}
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Adec
CCdec1 Cdec2
F G H I
A
{(..),..}
{(..),..}{(..),..}Dmaspec
{1,2,3}
D
BEspec
E1 E2 E3
{selection rules}
E
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6. Full-Automatic Simulation Based System Optimization (SSO)
Adec
CE3Cdec1
D1
F G
A
D2
SES
PES
A
E3
C
F GD1
D2
≡ {((E2 ∩ H) ∪(E3 ∩ F) )}
x
xx
x
x
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Current state: Modular, hierarchical model of a single system design Simulation based evaluation Configurable system parameters Numerical parameter optimization approach
Additional requirements Formal specification of all system designs
({system structures}, {system parameters}) Automatic generation of models/executable models Mapping of {system structures} ⇆ {numerical data} for
a structure optimization equivalent to a parameter opt.
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6. Full-Automatic Simulation Based System Optimization (SSO)
?
( )
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Adec
CCdec1 Cdec2
F G H I
A
{(..),..}
{(..),..}{(..),..}Dmaspec
{1,2,3}
D
BEspec
E1 E2 E3
{selection rules}
E
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6. Full-Automatic Simulation Based System Optimization (SSO)
xS= (xS1,xS2,xS3)Dmaspec => xS1 ϵ {1,2,3}Espec => xS2 ϵ {1,2,3}Cdec => xS3 ϵ {1,2}
⇒ n decision nodes → n variables
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Adec
CCdec1 Cdec2
F G H I
A
{(..),..}
{(..),..}{(..),..}Dmaspec
{1,2,3}
D
BEspec
E1 E2 E3
{selection rules}
E
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6. Full-Automatic Simulation Based System Optimization (SSO)
xsi= (3,3,1)
xS2=3 => D1,D2,D3xS3=3 => E3xS1=1 => C1
checking structure rules:{((E2 ∩ H) ∪ (E3 ∩ F) )} → OK
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Adec
CCdec1
F G
A
D1 D2 D3 E3
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6. Full-Automatic Simulation Based System Optimization (SSO)
xsi= (3,3,1)
A
E3
C
F G
D1
D2
D3
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Current state: Modular, hierarchical model of a single system design Simulation based evaluation Configurable system parameters Numerical parameter optimization approach
Additional requirements Formal specification of all system designs
({system structures}, {system parameters}) Automatic generation of models/executable models Mapping of {system structures} ⇆ {numerical data} for
a structure optimization equivalent to a parameter opt.
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6. Full-Automatic Simulation Based System Optimization (SSO)
( )
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cycle: eval. of ({structure} , {parameters})
automatic changes ofstructures and
parameters
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6. Full-Automatic Simulation Based System Optimization (SSO)
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7. Application Example
System Design
Splicer URS DigiURS DigiSplicer Software App
Development
AnalogPrinter
Scanner
CD Production
Digital Printer
Development
Cutter DigiCutter
LoginIn-sorter
Out SorterShipping
analog material
digital data
paper/picture/others
analog machine
digital machine
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7. Application Example
controller_lsspec
ctrl1 ctrl2DEP_LOGINdec1 DEP_LOGINdec3
queue_box2
queue_batchsplicermaspec
splicer
{#_of_splicers={1,…,6}}
DEP_LOGINdec2ctrl3
DEP_SPLICERdec
MODELdec
queue_order
queue_box1
sorter_manu
sorter_manu
queue_order
queue_box1
sorter_auto
queue_order
queue_box1
sorter_auto
MODEL
DEP_SPLICERCONTROLLER_LSDEP_LOGIN
model parameter
#_of_operators_ls={1,6}#_of_operators_pc={1,6}filter={0, 0.2, … 0.8, 1}
structure rules:{max(manu_login+auto_login,#_of_splicers)=#_of_operators}
{auto_login=1}
{manu_login=1}
{auto_login=1}
{manu_login=1}
SES
controller_pcspec
ctrl1 ctrl2 ctrl3
CONTROLLER_PC
queue_batch1
queue_batch2printer_analog
DEP_ANALOGdec
DEP_ANALOG
printer_analog cutter_analog
queue_batch1
queue_batch2printer_digi
DEP_DIGITALdec
DEP_DIGITAL
printer_digi cutter_digi
filter
162 system structures 3 system parameters 34992 system designs
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7. Application Example
controller_lsspec
ctrl1 ctrl2DEP_LOGINdec1 DEP_LOGINdec3
queue_box2
queue_batchsplicermaspec
splicer
{#_of_splicers={1,…,6}}
DEP_LOGINdec2ctrl3
DEP_SPLICERdec
MODELdec
queue_order
queue_box1
sorter_manu
sorter_manu
queue_order
queue_box1
sorter_auto
queue_order
queue_box1
sorter_auto
MODEL
DEP_SPLICERCONTROLLER_LSDEP_LOGIN
Model Parameter
#_of_operators_ls={1,6}#_of_operators_pc={1,6}filter={0, 0.2, … 0.8, 1}
structure rules:{max(manu_login+auto_login,#_of_splicers)=#_of_operators}
{auto_login=1}
{manu_login=1}
{auto_login=1}
{manu_login=1}
SES
controller_pcspec
ctrl1 ctrl2 ctrl3
CONTROLLER_PC
queue_batch1
queue_batch2printer_analog
DEP_ANALOGdec
DEP_ANALOG
printer_analog cutter_analog
queue_batch1
queue_batch2printer_digi
DEP_DIGITALdec
DEP_DIGITAL
printer_digi cutter_digi
filter
162 system structures 3 system parameters 18145 system designs
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7. Application Example
xS=(xDEP_LOGIN, xcontroller_ls_spec, xsplicermaspec, xcontroller_pc_spec )xP=(x#_of_operators_ls, x#_of_operators_pc, xfilter)S=(xS×xP) ⇒ 7 dimensional search room
controller_lsspec
ctrl1 ctrl2DEP_LOGINdec1 DEP_LOGINdec3
queue_box2
queue_batchsplicermaspec
splicer
{#_of_splicers={1,…,6}}
DEP_LOGINdec2ctrl3
DEP_SPLICERdec
MODELdec
queue_order
queue_box1
sorter_manu
sorter_manu
queue_order
queue_box1
sorter_auto
queue_order
queue_box1
sorter_auto
MODEL
DEP_SPLICERCONTROLLER_LSDEP_LOGIN
Model Parameter
#_of_operators_ls={1,6}#_of_operators_pc={1,6}filter={0, 0.2, … 0.8, 1}
structure rules:{max(manu_login+auto_login,#_of_splicers)=#_of_operators}
{auto_login=1}
{manu_login=1}
{auto_login=1}
{manu_login=1}
SES
controller_pcspec
ctrl1 ctrl2 ctrl3
CONTROLLER_PC
queue_batch1
queue_batch2printer_analog
DEP_ANALOGdec
DEP_ANALOG
printer_analog cutter_analog
queue_batch1
queue_batch2printer_digi
DEP_DIGITALdec
DEP_DIGITAL
printer_digi cutter_digi
filter
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7. Application Example
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7. Application Example
26 system designs with Fmin=0.26
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7. Application Example
average number of investigated individuals to find a global optimum 226,4
global optimum 47x
near optimal results with max 1% error 26x
results with 1 … 5% error 9x
results with 5 … 10% error 18x
numerical optimisation method: GA
Average results of 100 optimization experiments:
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7. Application Example
complete enumeration 18145 simulation runs Finding of global
optimum guaranteed
SSO ca. 226 simulation runs Finding of global
optimum not guaranteedBut with 73% probability
finding of a solution with error
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manual simulation based system optimization semi-automatic simulation based system
optimization⇒full-automatic simulation based system
optimization◦ formal description of {system designs}◦ automatic model generation◦ mapping {system structures} → {numerical parameter}⇒using of existing numerical parameter optimization
method possible◦ integration into traditional optimization algorithm
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8. Conclusion
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• O. Hagendorf, T. Pawletta: A Framework for Simulation-Based Structure and Parameter Optimization of Discrete Event Systems. In: Discrete-Event Modeling And Simulation, Ed. G.A. Wainer and P. J. Mosterman, CRC Press, 2011, 199-222
• [ZPK_2000] B.P. Zeigler, H. Prähofer, T.G. Kim: Theory of Modelingand Simulation (2nd Ed.), Academic Press, 2000
Simulation Based�Evaluation and Optimization of Modular, Hierarchical System Designs Using A Graph Based SpecificationContents1. Motivation2. Modular, Hierarchical Systems3. Modeling & Simulation4. Manual Simulation Based� System Optimization(SSO)5. Semi-Automatic SSO6. Full-Automatic SSO6.1 SES/MB Framework (Zeigler et al.)Characteristics of SESCharacteristics of SESCharacteristics of SESCharacteristics of SESCharacteristics of SESCharacteristics of SESSES/MB Based Model GenerationFull-Automatic SSO6.2 Mapping of �{system structures} → {numerical data}6.2 Mapping of �{system structures} ← {numerical data}6.2 Mapping of �{system structures} ← {numerical data}Full-Automatic SSO6.3 Full Approach7. Application: Production Planning of a Photofinishing LabSES of the exampleSES of the exampleSES of the exampleFitness functionResults: Complete EnumerationResults: SSOResults: comparisonConclusionFoliennummer 32