System-of-Systems Engineering - Liophant Simulation

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Discrete Event Simulation• Discrete Event Simulation(DES) uses numerical analysisto analyze systems where thestate variable(s) changes onlyat discrete points in time• DES Paradigms– Activity Oriented– Event Oriented– Process Oriented• DES models have theadvantages of a relatively fastrun time, flexibility, andmodularity• Useful for modeling queuesand logisticsCases with both a low time to execute killchain and high probability of successCases with a low time to execute kill chainCases with high probability of successCases not on the Pareto FrontierDeveloping Methods and Techniques for System of Systems EngineeringDr. Dimitri Mavris, dimitri.mavris@ae.gatech.edu36
Discrete Event Simulations• Discrete event problems embody the following concepts[Fishman 2001]:– Work - items, jobs, or customers seeking service– Resources - provider of service– Routing - collection of required services– Buffers - waiting area for work awaiting service– Scheduling - pattern of resource availability– Sequencing - order resources provide service (e.g. first come firstserve)– Performance - overall system measure• DES has many types of applications and describes a broadclass of simulations• Queuing models are able to describe systems with resourceallocation and sequences of operations [Zimmermann 2008]Developing Methods and Techniques for System of Systems EngineeringDr. Dimitri Mavris, dimitri.mavris@ae.gatech.edu37
Page 1 and 2: Developing Methods and Techniques f
Page 3 and 4: The Aerospace Systems Design Labora
Page 5 and 6: Motivation- Emphasis on “Early-Ph
Page 7 and 8: What is a system?“A set of functi
Page 9 and 10: System of Systems• “A set or ar
Page 11 and 12: What is SoSE?• System of Systems
Page 13 and 14: The Evolution of New Ideas“All tr
Page 15 and 16: What is needed for this New Paradig
Page 17 and 18: An Architecture-based Approach to S
Page 19 and 20: The System Alternative Space• The
Page 21 and 22: The Architecture-based Technology E
Page 23 and 24: Extension to Systems-of-Systems Ana
Page 25 and 26: Fundamental Properties of a Complex
Page 27 and 28: Enablers for Complex SoS• Design
Page 29 and 30: Design of Experiments• Design of
Page 31 and 32: Design of Computer Simulation• Co
Page 33 and 34: Agent-Based Behavioral Modeling•
Page 35: System Dynamics Modeling• System
Page 41 and 42: Surrogate ModelsWhy do we use surro
Page 43 and 44: Challenges to Surrogate Modeling fo
Page 45 and 46: Fitting Modal ResponsesDeveloping M
Page 51 and 52: Pareto Optimal Solutions in Many Di
Page 53 and 54: Interactive Electronic Design Revie
Page 55 and 56: Collaboration and Integration in So
Page 57 and 58: Concluding Remarks• System of Sys
Page 59 and 60: Selecting a DesignDeveloping Method
Page 61 and 62: A New Paradigm Shift: Simulation Ba
Page 63 and 64: Which are simple? Which arecomplica
Page 65 and 66: What is a Markov Chain• Markov Ch
Page 67 and 68: Markov Chains• The transition pro

System-of-Systems Engineering - Liophant Simulation

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