mbse, plm, mip and robust optimization for sos …ibm cplex for the mip model&run computations,...
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SMQ-MDL-MQAQ-0029-MOSS 1.0
MBSE, PLM, MIP and Robust Optimization for SoS
management of SCCOA
N. DUPIN (DGA), T. PEUGEOT, C. DUBECQ (MOSS), M.J. SEMBELY
(Airbus) [email protected], [email protected]
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Gentle reminder
System of systems
engineering must be
problem driven*
- How SE methodology is implemented for the complex industrial SoS SCCOA ?
- How to integrate in a tool stack state-of-the-art method of SE and OR to address a
specific problematic ?
* D. Luzeaux, J. Ruault, J. Wippler, Complex Systems and Systems of Systems, John Wiley & Sons (2013)
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Context presentation
Implementation of SE methods and specific tool stack
RETEX
Conclusion
Agenda
13 DEC 2016 SC-PPT-OT4001-0436-MOSS 3
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SCCOA establishes the Recognized Air Picture 24/7
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SCCOA provides tactical control of military flights
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SCCOA, the French air Defense system, is a real
system of systems
More than 80 systems
(radars, telecom artifacts,
control and command
centers, gateways)
… connected to 80
external systems
More than 150
operational centers
More than 100 sites
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SCCOA's management meets Maier’s criteria
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A lot of
stakeholders…
… many
programs…
… and many
decisions makers
DIRISI SIMMAD
…
TRS
TCS
…
EMA
EMM
SCCOA Stakeholders
(partial view)
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A permanent issue : SCCOA’s management issues
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Systems can be acquired
and/or used independently
SCCOA’s Project Management
(PM) is decomposed into several
management decision levels
SCCOA is interfaced with other
military and civilian programs
Incremental management of
SCCOA to face SoS complexity
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Issue : no tool is available + schedule uncertainty
A transient issue : migration of telecom artifacts
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Real names have been obfuscated
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MBSE enables stakeholders communication with a Domain Specific Ontology on top
of NAF*
MBSE tooling has made recent progress
*Ernadote, D.: An ontology mindset for system engineering. In: Systems Engineering (ISSE), 2015 IEEE International
Symposium on, pp. 454–460. IEEE (2015)
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System Engineering and Operations Research/Optimization
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In common: Decision Support frameworks
SE: structured processes, information and databases.
Questions raised in multi-objective optimization
OR: algorithms and tools for complex decision making
Opportunities to couple SE and OR.
State-of-the-art SE/OR: historic heritage of AI techniques
(sub-gradients, genetics algorithm, Constraint Programming)
Recent helpful advances in OR (generic design of meta-
heuristics, model&run solvers: LocalSolver, MIP, robust
optimization)
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Context presentation
Implementation of SE methods and specific tool stack
RETEX
Conclusion
Agenda
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When a new problem is met, the tool stack is augmented
One tool stack to tackle many issues
The stack enables data reuse across tools
PROLOG MEGA ACCESS
Calendar
Database
Logical
architecture
Deployment
architecture
Sequencing
Tool
Cost &
Resources
Optimization
Contract
Managers
MIP, IBM CPLEX
Optimized
deployment
schedule
Deployment
schedule
Robust
Optimization Cost
data
Scheduling
constraints
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A Product Lifecycle Management (PLM) tool is
needed to support SoS engineering
System engineering:
Definition of
configurations
PLM
Definition of spatial &
temporal deployment
Single system
engineering
System of systems
engineering
System engineering:
Definition of
configurations
PLM
Definition of spatial &
temporal deployment
Engineering data flow
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An ontology for the management of System of
Systems
Main
validation
effort
Main driver is capture of validated models
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An pivot ontology for enabling communication
between tools
Defined by
problem of
interest
Defined by
CM
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Communication between tools is enabled by Prolog
and a CM process
Communication between tool is a « change
management » process
Input & output formats are tool neutral (n-
uplets)
Correspondence rules* are expressed with the
Domain Specific Language of the pivot ontology
PROLOG
Tool A Tool B Knowledge
Base A Knowledge
Base B
Correspondance
rules*
Change
Request to
Base A
Change
Request to
Base A
Change
Request to
Base A
Change
Request to
Tool B
Change
Request to
Base A
Change
Request to
Tool A
Change
Control
Board
Change
Control
Board
Change
notice to
Tool B
Change
notice to
Tool B
Change
notice to
Tool B
Change
notice to
Tool A
*ISO/IEC/IEEE 42010
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Mixed Integer Programming (MIP)/ Robust optimization
PERT scheduling: a polynomial problem Minimizing total completion time (makespan)
Only precedence constraints among tasks
Implemented in MS Project
Adding resources constraints, RCPSP, a NP Hard problem.
Our objectives: Minimizing dismantling dates for SCCOA sites
Minimizing financial cost through maintenance costs of obsolete
technologies
Needs for a specific optimization problem
MIP: model&run implementation using OPL Cplex
Robust optimization extension: considering stochastic delays in project
milestones
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Context presentation
Implementation of SE methods and specific tool stack
RETEX
Conclusion
Plan
13 DEC 2016 SC-PPT-OT4001-0436-MOSS 19
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Lessons learnt using MEGA NAF
MEGA NAF imposed by the DGA for coherence of DGA
architecture modelling.
Automatic generation of chapters of analytical documents
ensures consistent documentation.
Some vigilance points were raised thanks to the NAF SoS
model to detect functional and/or temporal discontinuities.
Generation of a website with selected views and pieces of
information: clear success amongst stakeholders of SCCOA.
The website gave satisfaction for its ability to capitalize knowledge
amongst stakeholders, it was adopted for the training of
newcomers in SCCOA.
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Surprise ! Prolog (b. 1972) match the needs !
Input/output format are
tools neutral
Correspondence rules are
naturally and concisely
written in prolog → Low
maintenance
Prolog is open source,
Having satisfactory
performances with quick
developments encouraged us
to use Prolog.
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Lessons learnt using Mixed Integer Programming
IBM Cplex for the MIP model&run computations, OPL script for
data preprocessing.
MIP allows to deal with more complex models: resource
constraints, financial costs and uncertainty with robust optimization
techniques.
For this case, resolution to optimality is easy with Cplex. Other
free and less efficient MIP solvers could be used efficiently.
Generally, MIP requires some specific expertise in modeling to be
efficient, but offers optimality guarantees.
For discrete optimization problems, we recommend for non
experts to use more intuitive modeling frameworks such as
LocalSolver or Constraint Programming.
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Context presentation
Implementation of SE methods and specific tool stack
RETEX
Conclusion
Plan
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Conclusions
Methodological conclusions: NAF extensions : Calendar database, PLM tool with Prolog
Interest of Prolog for concision and maintainability
MIP interest : optimality proof, state-of-the art robust optimization techniques
Industrial conclusions for SCCOA : Return on investment of the SE methodology
Web site export: a powerful tool for communication amongst stakeholders
and for training of SCCOA newcomers.
Innovating methodology for opportunity questions.
Dismantling old telecom artifacts: planning results presented to the customers
and decision makers
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Perspectives
Methodological perspectives: Generalization of the methodology for other SoS
Excel for pivot files between SE and OR
Using more open source software (COIN-OR)
Multi-objective optimization
Constraint Programming: efficient for scheduling problems, easier for model&run
LocalSolver: large scale model&run optimization solver
Simulation coupled with SE and OR.
Industrial perspectives for SCCOA : Preparation of next increment SCCOA 5 : architecture design
Other opportunity and scheduling questions
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References
System Engineering references : D. Luzeaux, J. Ruault, J. Wippler, Complex Systems and Systems of
Systems, John Wiley & Sons (2013)
J. Estefan, et al.: Survey of model-based systems engineering (MBSE)
methodologies. Incose MBSE Focus Group 25(8) (2007
Ernadote, D.: An ontology mindset for system engineering. In: IEEE
International Symposium on Systems Engineering (ISSE), pp. 454–460. (2015)
Operations Research references : D. Bertsimas, M. Sim: The price of robustness. Operations research 52(1),
35–53 (2004)
M. Fischetti, M. Monaci, Light robustness. Lecture Notes in Computer
Science 5868, 61–84 (2009)
J. Schulz, Hybrid solving techniques for project scheduling problems. Ph.D.
thesis (2013)
N. Dupin, Modélisation et résolution de grands problèmes stochastiques
combinatoires: application à la gestion de production d’électricité. Ph.D. thesis,
Univ. Lille 1 (2015)
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Appendix: MIP model