Transcript Optimization Services Framework and Virtual Prototype System

Optimization via Simulation
under
Optimization Services (OS)
Jun Ma
Industrial Engineering and Management Sciences
Northwestern University
IFORS, Hawaii, 07/14/2005
Robert Fourer, Jun Ma, Kipp Martin, Optimization Services, May 06, 2005
OUTLINE
1. Optimization via Simulation – two case studies
2. Optimization Services (simulation part)
3. Optimization Services via Simulation representation and
communication
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Robert Fourer, Jun Ma, Kipp Martin, Optimization Services, May 06, 2005
Optimization via Simulation
Case 1: Motorola Optimization System
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Robert Fourer, Jun Ma, Kipp Martin, Optimization Services, May 06, 2005
0. Model
Constructor sends
optimization
instance
11. Solver
interface
1. Start
optimization
12. Need
intelligence?
Yes
17. Calculates
runtime statistics
data
No
2. Solver requests
functional values
F(x)
16. Assistant Opt
13. Central server
3. Solver identify
constraint status
and determines
search direction
15. Store
data in
Hash
Table
Evaluated
F(x)
dx
4. Solver carries a
1-dim step search
and finds
a
No
Evaluated
F(x)
Yes,
Retrieved
F(x)
18. Analyzer/Decider
chooses function call
types
14. Simulation
engines
20.
Learner
learns
globally
global
19. Learn
functions?
No
Yes
5. Solver requests
functional values
F(x+a*dx)
22. Retrieved data point
from Hash Table?
Yes.
Retrieved
F(x)
6. objective and/or constraint
improvement?
No
Yes
Yes
23. Closest data point
good enough?
No
7. Solver updates
x = x + a * dx
Estimated
F(x)
No
24. Need exact
function value?
8. Termination?
(convergent, infeasible,
etc.)
Yes
9. Finish
optimization
No
Estimated
F(x)
25. Estimator
calculates F(x)
either from
local learner
or global
learner
10. Return result
to Model
Constructor
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Robert Fourer, Jun Ma, Kipp Martin, Optimization Services, May 06, 2005
local
21.
Learner
learns
locally
Yes
Optimization via Simulation?
Case 2: MPL, NEOS and Kestrel
ampl: option optimizationservices on
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Robert Fourer, Jun Ma, Kipp Martin, Optimization Services, May 06, 2005
OUTLINE
1. Optimization via Simulation – two case studies
2. Optimization Services (Simulation Part)
3. Optimization Services via Simulation representation and
communication
6
Robert Fourer, Jun Ma, Kipp Martin, Optimization Services, May 06, 2005
Optimization Services (OS)
OS
AMPL
Model/Data
Parse to OSiL
Agent
OSP --
Solver
Server
OShL(OSiL)
OSmL
Modeler
Max f(x)
x
s.t. lb1 <= g1(x) <= ub2
lb2 <= g2(x) <= ub2
:objective
:variables
:constraints
f(x) can be sin(x(1))+x(x(2))
g1(x) can be if(x(1)>0) then x(2) else cost(x(2))
g2(x) can be a metric from a finite element simulation
(non-closed form black box function evaluator)
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Robert Fourer, Jun Ma, Kipp Martin, Optimization Services, May 06, 2005
OS
Server
Simulation
location
OUTLINE
1. Optimization via Simulation – two case studies
2. Optimization Services (Simulation Part)
3. Optimization Services via Simulation representation
and communication
8
Robert Fourer, Jun Ma, Kipp Martin, Optimization Services, May 06, 2005
An Optimization Services View of a Simulation
minimize x12  2 x22
x
subject to 2 x1  3x2  9
x1  0, x2  0
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Robert Fourer, Jun Ma, Kipp Martin, Optimization Services, May 06, 2005
Optimization Services Protocol (OSP)
What does the protocol involve? – 20+ OSxL languages
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Robert Fourer, Jun Ma, Kipp Martin, Optimization Services, May 06, 2005
<Simulations> element in OSiL
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Robert Fourer, Jun Ma, Kipp Martin, Optimization Services, May 06, 2005
<Simulations> element in OSiL
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Robert Fourer, Jun Ma, Kipp Martin, Optimization Services, May 06, 2005
<Simulation> element in OSiL
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Robert Fourer, Jun Ma, Kipp Martin, Optimization Services, May 06, 2005
Optimization Services (OS)
OS
AMPL
Model/Data
Parse to OSiL
Agent
OSP --
Solver
Server
OShL(OSiL)
OSmL
Modeler
Simulation
OS
Server
location
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Robert Fourer, Jun Ma, Kipp Martin, Optimization Services, May 06, 2005
http://www.optimizationservices.org
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Robert Fourer, Jun Ma, Kipp Martin, Optimization Services, May 06, 2005