Derek Hitchins - Systems World

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Derek Hitchins - Systems World

Prove SystemSolutionDerek Hitchins10/20/07 dkh©2004 1


Design RatchetingOffspringOffspringfighter Fightergeneration GenerationMany-on-manycombatsimulationSelectedfighterStable?Currentfighter"Seed"threat"Seed"fighterEnemythreatStable?SelectedenemythreatMany-on-manycombatsimulationOffspringOffspringFighter threatgeneration Generation• Far left shows cumulative selection ofe.g., Blue fighter design, using enemy(Red) fighter threat as a dynamicreference• When Blue fighter design has reachoptimum, Blue fighter becomes seedfor Red fighter cumulative selection• Process can occur over several stages,with each design leapfrogging itspredecessor• Obvious dangers of creating nonfeasibledesigns can be anticipated– Insert physical/technological limits intooffspring generation processes10/20/07 dkh©2004 2


Using Non-linear Dynamic Simulation• It is possible to update the basic systems engineeringparadigm– To create hundreds, or even thousands of options coveringdifferent:—• vehicle arrangements: how many, what functions…• operational parameters… power, capacity, sensitivity, range,frequency, etc., etc.…• Support & logistics…• …weapons performance, etc., etc.,– To search through the resulting massive n-dimensionalsolution space efficiently and…– To find the optimum (e.g. most cost effective) solution ofall the possible configurations– To “prove” your solution is the right one.10/20/07 dkh©2004 3


Using Non-linear Dynamic Simulation• The key is to use genetic algorithmic methods• Establish pseudo-genes to code for parameters insolution system– i.e., re-create the system solution from a set of genes,• e.g., Gene A codes for “radar transmitter power”– Gene A can take on a range of values that express as a range oftransmitter powers• e.g., Gene B codes for “number of weapons type X”– Gene B can take on a range of values corresponding to thenumber of X missiles carried, with an upper limit set by capacity• In each case, as the genes code for more or less, there is aconsequent cost assessment– E.g., more missiles carried = more cost– E.g. greater missile Ph = less missile firings…10/20/07 dkh©2004 4


Using Non-linear Dynamic Simulation• Design search starts by randomly generating a set ofgene values• These vary the initial parameters in the Blue Model• These values determine a putative system design– number of vehicles, weapons, ranges, missile Ph, etc.• This system design solution is sent into combatagainst an unaltered, but still dynamic and interactive,Red force.• The outcome of the conflict is recorded as e.g., thevarious forms of effectiveness provided by thatparticular set of genes10/20/07 dkh©2004 5


Using Non-linear Dynamic Simulation• Process is repeated for a significant numberof random gene patterns• Results from, say, 500 runs are comparedand the “best” solution is recorded• The corresponding gene values are set intothe design as “radar transmitter power,”“number of missiles,” etc.• This represents the first level of improveddesign10/20/07 dkh©2004 6


Using Non-linear Dynamic Simulation• The process is repeated, only now the extent bywhich the genes may change from the nominalvalue may be reduced– The intent is to refine the “hill-climbing” process• After a relatively few cycles the process is unableto improve Blue effectiveness– Typically, 15 ≤ cycles ≤ 30• The whole exercise may be repeated usingdifferent terrain and different Red opposition, untila firm, provable solution is established for allreasonable situations10/20/07 dkh©2004 7


Using Non-linear Dynamic SimulationRandomPatternGeneratorGenesOperationalEffectivenessMeasurementGenesGenesDynamicReferenceGenes• Genes “code for” differentparameter values:Genes• Some combinations produce lesseffective Blue, some more effective.• Record genes leading to better Blue,repeat the runs, watch Blue’sEffectiveness gradually grow and grow– Tx power, Rx sensitivity, DTDMAcapacity, number of vehicles in a • Red is held as a dynamic reference.10/20/07 set.dkh©2004 8


Typical Non-Linear Dynamic Simulation• Following program employs STELLA– Could use any non-linear dynamic tool• Such programs look at function andbehaviour, but…• …lack spatial dimension• Full solution requires bespoke tool withterrain cover, line-of-sight, obstacles, tracks,etc.OptimizeLF202010/20/07 dkh©2004 9


Differing Effectiveness ViewpointsRun 3 economizes onBattle Damage Repair,has fast intelligence andplenty of weapons…Run 3 does not perform well w.r.t.Casualties:1.0 is equal Blue and Red1.53/∞ would be preferred…Blue Cost Effectiveness- Red Cost EffectivenessNeed more runs…! Insert values for, say, Run 3, and start again.Initial results indicate start point well away from optimum…10/20/07 dkh©2004 11


Counter-Intuitive Results?Cumulative SelectionWeapon Stock Blue Cost Effectivness %12080Blue Weapon Ready Stock1008060402070605040302010Blue Cost Effectiveness %01 2 3 4 5 6 7 8 9 10 11Genetic progression0Despite the increased capital cost of having more weapons,more weapons increase Blue Cost Effectiveness (!)10/20/07 dkh©2004 12


Increasing Cost EffectivenessCumulative SelectionBlue PhTx PowerBlue Weapon Ph10.90.80.70.60.50.40.30.20.101 2 3 4 5 6 7 8 9 10 11Genetic progression1.20E+081.00E+088.00E+076.00E+074.00E+072.00E+070.00E+00Blue Radar Tx Power (Watts)Blue Cost Effectiveness increases as the weapon Ph increases (less weaponsneeded for same effect) and as radar Tx power diminishes (two reasons:cheaper radar, and reduced “observables.”)10/20/07 dkh©2004 13


Non-linear Dynamic Simulation• Result is a matched set of optimalparameters—specifications— for Blue, in thesituation represented by the simulation– Great advance on conventional methods• Matched specs show each subsystem– making best contribution to overall Mission Effectiveness -however measured– While operating and interacting with other systems underoperational conditions, i.e. organismic synthesis!• Solution system parameters contribute to optimumsolution– not too little, not over the top, but…– just right for successful operations• Determines optimal support, maintenance, logistics, too10/20/07 dkh©2004 14


Proving You Have Done It…• Had we been able to create a variety of terrains…• …and given that the simulations were reasonable, then…• We would have established– a systems solution,– a system design to the first level,– a set of research targets– a matched set of specifications for subsystems, and– a test bed upon which the incredulous—and futurecontractors—could explore, challenge, and possibly improve,our conclusions• …and everything can be tracked back to the initialarticle, the TRIAD, and so on…it works!10/20/07 dkh©2004 15


Applicability of Method• Method used with great success in a varietyof walks of life• Essentially, nothing about the method that iscontext or technology dependent• Used for Famine Relief, Reconstruction ofAfghanistan, Global socio-economicforecasting, and many, many more…Afghanistan Peace Operations10/20/07 dkh©2004 16


Rigorous Soft MethodcombinesGPSP and SEPPRigorous Soft Method• Functional• Physical• BehaviouralReferenceModelsIssueIdentify ProblemSymptomsGroup Problem Symptomsinto Problem ThemesModel Problem Themes(Ideal World)Identify differences betweenReal and Ideal WorldGenerate optionsto resolve IssueGenerate criteriafor a good solutionAddress all problemcomponentsto avoid (Forrester’s)counterintuitive responseUse tools andmethodsto accommodatecomplexityQ.E.D.Using geneticalgorithms,hundreds/thousands ofoptions may begenerated and comparedVerifyPreferredOption(s)PotentialImprovementsEnsure solutioncompleteness—if any solutionexistsLogical, consistent, butnot necessarilyculturally acceptable

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