{ Analytica Model Multi_D_Demo, encoding="UTF-8" } SoftwareVersion 5.4.6 { System Variables with non-default values: } SampleSize := 2000 TypeChecking := 1 Checking := 1 SaveOptions := 2 SaveValues := 0 {!-50299|DiagramColor Model: 65535,65535,65535} {!-50299|DiagramColor Module: 65535,65535,65535} {!-50299|DiagramColor LinkModule: 65535,65535,65535} {!-50299|DiagramColor Library: 65535,65535,65535} {!-50299|DiagramColor LinkLibrary: 65535,65535,65535} {!-50299|DiagramColor Form: 65535,65535,65535} NodeInfo FormNode: 1,0,0,,0,0,,,,0,,,0 {!-50299|NodeColor Text: 62258,62258,62258} AskAttribute Value,Variable,Yes Model Multi_D_Demo Title: Multi-D Demo Author: TR Date: Do, Sep 24, 2020 1:43 PM DefaultSize: 72,32 DiagState: 2,0,0,935,588,17,,4 WindState: 2,98,82,720,350 FontStyle: Century Gothic,16 FileInfo: 0,Model Multi_D_Demo,2,2,0,0,\\Mac\Home\Documents\Dropbox syconomic\Dropbox\4. syST3M\Multi-D Demo.ana Chance Anzahl_Reparaturen Title: Anzahl Reparaturen Definition: Uniform( min: Param_Reparaturen[ Param_Reparaturen = 'Minimum' ],~ max: Param_Reparaturen[ Param_Reparaturen = 'Maximum' ],~ integer: True ) NodeLocation: 280,288,1 NodeSize: 72,32 ValueState: 2,84,90,599,498,0,SAMP Variable Param_Reparaturen Title: Parameter Reparaturen Definition: Table(Self)(188,211) IndexVals: ['Minimum','Maximum'] NodeLocation: 104,288,1 NodeSize: 72,32 Aliases: FormNode Fo1399102333 Att_PrevIndexValue: ['Minimum','Maximum'] Index Reparatur Title: Reparatur Definition: 1 .. Param_Reparaturen[ Param_Reparaturen = 'Maximum' ] NodeLocation: 104,488,1 NodeSize: 72,32 Chance Dauer_Reparatur Title: Dauer Reparatur Units: min Definition: Pert( min: Parameter_Dauer[ Parameter_Dauer = 'Minimum' ],~ mode: Parameter_Dauer[ Parameter_Dauer = 'Modalwert' ],~ max: Parameter_Dauer[ Parameter_Dauer = 'Maximum' ],~ over: Reparatur ) NodeLocation: 280,384,1 NodeSize: 72,32 ValueState: 2,358,169,656,463,,PDFP GraphSetup: Att_AreaFill Graph_Pdf_Valdim:1~ Att_ContLineStyle Graph_Pdf_Valdim:6~ Att_GraphValueRange Dauer_Reparatur:|1:1,,1 ReformVal: [Undefined,Null,Undefined,Undefined,1] Att_XRole: -1 Att_ColorRole: Null {!40200|Att_GraphSetupSlices: [3,1,DensityIndex,1]} Variable Parameter_Dauer Title: Parameter Dauer Units: min Definition: Table(Self)(3,32,14) IndexVals: ['Minimum','Maximum','Modalwert'] NodeLocation: 104,384,1 NodeSize: 72,32 DefnState: 2,589,104,416,303,0,DFNM Aliases: FormNode Fo862231421 Att_PrevIndexValue: ['Minimum','Maximum','Modalwert'] {!40404|FreePassObjectCount gYjCmvR1Yx52CF1pUGt3QYSZPSsU2Wz8WTWCqd9ewT66F$3phLgH8MMNJCwMtXtLbWjEufM5$7LTJTJH1S9HNen$mWK3qj$Lcu$hYSGhzGXXeoXID0BessztviExhO_SciXHHMBOt79IEI7hQCoOaixpkmPGV0IMXVbS2pdxQce_vwoYRR1ESUWXYE1i6jAVTanMDrFm4Wjfmw11voW6nDYF18C33avz86JqQBfGiF84IDxdBgNMWcXTOEyWsHsep0sphFl3b$JNDAFlDm5iZnyqlmQsP47SYQPB5mAlQ_Qt9JYYRiFZfss_tVIBzdzFGCOfhcyKgp9GHRYYQJ1dIkC0TXJf1QpDf5Y0V_V1Z7hGsT5kN2kQ7sbL6ugUJ8_skdXROKIHGHIKNRWciqy6GSer3IXo4Nf_Jf0OnBc1UxPuOvR_Y7iIvYBrWCvdM5rdPC0rhYPIB50yvtsstux_17DKSblv5HUjyCTl2Lg0Mk6VvKmDhAfAhDmKvV6jM$gL1kSBxiUG4ujZQIB50xurqppqsvz17DLTcmx7KXl_EWn4Oj3Pm9YyNpHlEjEmIsQ0cDrU7oUAucL5seRF3vldVOIE96432346AEJPWdmv4FRes5LcuAUp8UrDc0RsJmEiDjFoLvV5jL_eJ$iQ9udPB$peUKB4zupljhggghknrw06} Library Distribution_variati Title: Distribution Variations Description: This library contains various functions for defining standard distributions using different sets of parameters. Author: Lonnie Chrisman and Fred Brunton~ Lumina Decision Systems Date: Wed, Oct 20, 2004 12:18 AM DefaultSize: 48,24 NodeLocation: 280,488,1 NodeSize: 72,32 NodeInfo: 1,1,1,1,1,1,0,0,0,0,,,,0 DiagState: 2,16,25,615,562,17 WindState: 2,393,94,476,224 FontStyle: Arial, 15 Att_PreLoadScript: {!40404|FreePassObjectCount gYjCmvR1Yx52CF1pUGt3QYSZPSsU2Wz8WTWCqd9ewT66F$3phLgH8MMNJCwMtXtLbWjEufM5$7LTJTJH1S9HNen$mWK3qj$Lcu$hYSGhzGXXeoXID0BessztviExhO_SciXHHMBOt79IEI7hQCoOaixpkmPGV0IMXVbS2pdxQce_vwoYRR1ESUWXYE1i6jAVTanMDrFm4Wjfmw11voW6nDYF18C33avz86JqQBfGiF84IDxdBgNMWcXTOEyWsHsep0sphFl3b$JNDAFlDm5iZnyqlmQsP47SYQPB5mAlQ_Qt9JYYRiFZfss_tVIBzdzFGCOfhcyKgp9GHRYYQJ1dIkC0TXJf1QpDf5Y0V_V1Z7hGsT5kN2kQ7sbL6ugUJ8_skdXROKIHGHIKNRWciqy6GSer3IXo4Nf_Jf0OnBc1UxPuOvR_Y7iIvYBrWCvdM5rdPC0rhYPIB50yvtsstux_17DKSblv5HUjyCTl2Lg0Mk6VvKmDhAfAhDmKvV6jM$gL1kSBxiUG4ujZQIB50xurqppqsvz17DLTcmx7KXl_EWn4Oj3Pm9YyNpHlEjEmIsQ0cDrU7oUAucL5seRF3vldVOIE96432346AEJPWdmv4FRes5LcuAUp8UrDc0RsJmEiDjFoLvV5jL_eJ$iQ9udPB$peUKB4zupljhggghknrw06} {!40400|Att_clearTypeFonts: -1} Function Smooth_fractile(fract : ascending[I] ; ~ F : positive ascending[I] ;~ I : index = common ;~ over : ... optional atomic;~ singleSampleMethod: optional atomic numeric hidden) Title: Smooth Fractile Description: Given a set of fractiles, this returns a smooth distribution with tails having the indicated fractiles. The fractiles to use must be specified in F, each value being between 0 and 1, and the fractile values must be in fract. ~ ~ For example, to specify a distribution having a P10, P50 and P90 of 7, 13, and 15, set:~ F := [0.1, 0.5, 0.9]~ fract := Table(F) ( 7, 13, 15 )~ and call Smooth_Fractile(fract,F) Definition: {The "seed" distribution has the dimensions of fract except for F.}~ var u:=if IsNotSpecified(singleSampleMethod) ~ Then normal(0,1,over:slice(fract,I,1))~ Else random(normal(0,1,over:slice(fract,I,1)), ~ method:singleSampleMethod);~ index pwr := 0..size(F)-1;~ var N := cumnormalinv(f,0,1)^pwr;~ var a := sum(Transpose(Invert(N,I,pwr),I,pwr)* fract,I);~ sum(a*u^pwr,pwr) NodeLocation: 80,184,1 NodeSize: 48,24 WindState: 2,365,39,514,582 Function Beta_m_sd(m : numeric, sd : positive ; lower,upper:optional numeric; over : ... optional atomic; singleSampleMethod : optional atomic numeric hidden ) Title: beta_m_sd(m,sd) Description: A beta distribution parameterized by the theoretical mean and std.dev. for the resulting distribution.~ ~ Based on Method of Moments.~ Reference:~ Morgan, M.G., and Henrion, M., "Uncertainty", 1990, p. 97 Definition: if IsNotSpecified(lower) then lower:=0;~ if IsNotSpecified(upper) then upper:=1;~ var u := (m-lower) / (upper-lower);~ var v := (sd / (upper-lower))^2;~ var a := (u^2 - u^3 - v * u) / v;~ var b := (u * (1-u)^2 - v * (1-u)) / v;~ if IsNotSpecified(singleSampleMethod) then~ beta(a,b,lower,upper)~ else~ beta(a,b,lower,upper, singleSampleMethod:singleSampleMethod) NodeLocation: 320,120,1 NodeSize: 96,20 WindState: 2,57,102,524,409 Function Lognormal_m_sd(mean, stddev; over: ... optional atomic; singleSampleMethod : optional atomic numeric hidden ) Title: LogNormal_m_sd(m,sd) Description: This function is no longer need since the built-in log normal function has been enhanced. The definition has been updated accordingly. It is included here only for backwards compatibility.~ ~ This function works well when the ratio of mean/stddev >= 1. Otherwise the sample stddev may vary considerably from the desired.~ ~ Definition: if (isNotSpecified(singleSampleMethod)) then~ lognormal(Mean:mean,Stddev:stddev)~ else ~ lognormal(Mean:mean,Stddev:stddev, singleSampleMethod:singleSampleMethod)~ NodeLocation: 120,56,1 NodeSize: 92,20 WindState: 2,125,102,784,377 Function Pert(min,mode,max; over : ... optional atomic; singleSampleMethod : optional atomic numeric hidden ) Title: Pert(min,mode,max) Description: A Pert-distribution, which is a beta distribution defined by a given min, mode, and max. Definition: var mean := (min + 4*mode + max ) / 6;~ var a :=6* (mean - min) / (max-min);~ var b := 6*(max - mean) / (max-min);~ if (isNotSpecified(singleSampleMethod)) then~ beta(a,b,min,max)~ else ~ beta(a,b,min,max, singleSampleMethod:singleSampleMethod) NodeLocation: 320,56,1 NodeSize: 96,20 WindState: 2,23,9,784,377 Function Gamma_m_sd(m,sd : positive; over : ... optional atomic; singleSampleMethod : optional atomic numeric hidden ) Title: Gamma_m_sd(m,sd) Description: The gamma distribution, parameterized by the theoretical mean and std.dev. of the target distribution.~ ~ Uses Method of Moments. Reference:~ Morgan, M.G., and Henrion, M., "Uncertainty", 1990, p. 93. Definition: var a := (m/sd)^2;~ var b := sd^2/m;~ if (isNotSpecified(singleSampleMethod)) then~ gamma(a,b)~ else ~ gamma(a,b, singleSampleMethod:singleSampleMethod) NodeLocation: 120,120,1 NodeSize: 92,20 WindState: 2,93,30,784,377 Function Warp_dist(dist : Samp ; ~ fracts : ascending[F] ; ~ F : positive ascending IndexType;~ over : ... optional atomic,~ singleSampleMethod : hidden optional atomic numeric ) Title: Warp Dist Description: Applies a smooth warping funtion to a given sample so as to obtain the listed fractiles, while maintaining the approximate shape of the distribution. For example, if you have p10, p50 and p90 percentiles and you want a "Normal-like" distribution, you could use:~ ~ index F:=[10%,50%,90%];~ var pctiles := Array(F,[5,10,20]);~ Warp_dist(Normal(0,1),pctiles,F)~ ~ The resulting distribution will not be a Normal (you can't necessarily obtain a normal with any three fractiles, since Normal has only 2 free parameters), but it will be basically bell-shaped -- skewed a bit to the left to obtain the given fractiles.~ ~ Note that if you were to provide only two fractiles, Normality would be preserved in this example. Definition: var p := if IsNotSpecified(singleSampleMethod) ~ Then if IsSampleEvalMode then dist else getfract(dist,0.5)~ Else getfract(dist,Random(method:singleSampleMethod));~ var z:=getfract(dist,F);~ Cubicinterpextrap(z,fracts,p,F) NodeLocation: 192,184,1 NodeSize: 48,24 WindState: 2,10,13,498,590 Module Dist_var__helper_fn1 Title: Dist Var. Helper Fns Author: Lonnie Date: Thu, Mar 16, 2006 1:54 PM DefaultSize: 48,24 NodeLocation: 312,184,1 NodeSize: 56,24 DiagState: 2,74,407,534,284,17 Function Triangular_10_mode_9(p10,mode,p90 : scalar; over:...atomic optional ; singleSampleMethod : hidden optional scalar ) Title: Triangular 10_Mode_90 Description: A triangular distribution defined by its p10 and p90 fractiles (p10<=p90) and mode.~ ~ This is here for legacy models. You should now use: Triangular10_mode_90 (without the first underscore). Definition: if IsNotSpecified(singleSampleMethod) then~ Triangular10_Mode_90( p10,mode,p90,over:over)~ else~ Triangular10_mode_90( p10,mode,p90,over:over, singleSampleMethod:singleSampleMethod ) NodeLocation: 128,216,1 NodeSize: 92,20 WindState: 2,418,134,680,280 Function Triangular_u_given_h(d,h) Title: Triangular u given h Description: This is a helper function for Triangular_10_mode_90. Gives base of a triangle given the dist from right corner to 10th percentile and the height. The distance is not necessarily correct, it is used in a search.~ Only valid for d>0. Definition: var a:=h/2;~ var b:=-(h*abs(d)+0.1);~ var c := h*abs(d)^2/2;~ (-b + sqrt(b^2-4*a*c)) / (2*a) NodeLocation: 392,56,1 NodeSize: 48,24 Function Triangular_area_from(d1,d2,h) Title: Triangular Area from h Description: This is a helper function for Triangular_10_mode_90.~ Gives the area of a triangle dist given the height and d1=pmode-p10, d2=pmode-p20. Definition: 1/2 * (Triangular_u_given_h(d1,h) + Triangular_u_given_h(d2,h)) * h NodeLocation: 272,56,1 NodeSize: 56,24 WindState: 2,584,482,476,224 Function Cubicinterpextrap(D,R:numeric[I];X:atomic numeric;I:IndexType) Title: CubicInterpExtrap Description: Linear extrapolation, same as linearinterp, with the extended functionality that when X < min(D,I), the first line segment in D,R is used to extrapolate to the left, and for X>max(D,I) the last line segment is extrapolated. Definition: var n=size(I);~ if n=1 then r else begin~ if Xslice(D,I,n) then~ (x-slice(d,I,n)) * (slice(R,I,n)-slice(R,I,n-1)) / ~ (slice(d,I,n)-slice(d,I,n-1)) + Slice(R,I,n)~ else ~ cubicinterp(d,r,x,I)~ end NodeLocation: 120,56,1 NodeSize: 80,24 WindState: 2,143,90,650,421 Function Triangular_10_50_90(p10,p50,p90 ; noErr : optional boolean ; over:... optional atomic; ~ singleSampleMethod : optional hidden scalar ) Title: Triangular_10_50_90 Description: This defines a trianglar distribution given percentiles p10 <= p50 <= p90.~ ~ This is a legacy name. You should now use Triangular10_50_90 instead (without the first underscore) Definition: if IsNotSpecified(singleSampleMethod) then~ Triangular10_50_90( p10,p50,p90,over:over)~ else~ Triangular10_50_90( p10,p50,p90,over:over, singleSampleMethod:singleSampleMethod ) NodeLocation: 328,216,1 NodeSize: 92,20 WindState: 2,360,32,765,465 Text Te36 Description: These are legacy names NodeLocation: 232,168,-1 NodeSize: 200,16 Close Dist_var__helper_fn1 Function Erlang(m,n; over : ... optional atomic; singleSampleMethod : optional atomic numeric hidden ) Title: Erlang(m,n) Description: The Erlang distribution is really just a variant of the Gamma distribution with another name, although it generally refers to the special case when parameter n is an integer, while the corresponding parameter A in a gamma distribution is often non-integer. ~ ~ The time of arrival of the nth event in a Poisson process with mean arrival of m follows an Erlang distribution. Definition: if (isNotSpecified(singleSampleMethod)) then~ gamma(n,m)~ else ~ gamma(n, m, singleSampleMethod:singleSampleMethod) NodeLocation: 80,264,1 NodeSize: 52,24 WindState: 2,104,36,476,378 Function Pareto(a,b; over : ... optional atomic; singleSampleMethod : optional atomic numeric hidden ) Title: Pareto(a,b) Description: The Pareto distribution. ~ The "classic" use of the Pareto distribution is to model the distribution of wealth in a society, under an assumption that a smaller percentage of the people own a larger percentage of the wealth (e.g., 20% of the population control 80% of the wealth).~ ~ The Pareto distribution is appropriate for a variety of "population" models. Examples: The size of objects in a population (e.g., grains of sand), value of assets in a collection of assets, file sizes, word frequencies, number of acquaintances of a given person, etc. Definition: if (isNotSpecified(singleSampleMethod)) then~ b * (1-uniform(0,1,over:a,b)) ^ (-1/a)~ else~ b*(1-uniform(0,1,singleSampleMethod:singleSampleMethod,~ over:a,b)) ^ (-1/a) NodeLocation: 200,264,1 NodeSize: 48,24 WindState: 2,613,82,476,448 Function Rayleigh(mode; over : ... optional atomic; singleSampleMethod : optional atomic numeric hidden ) Title: Rayleigh(mode) Description: The Rayleigh distribution results when you have two orthogonal components that are each normally distributed, such as might be the case with Wind Speed. The length of the vector itself will then have a Rayleigh distribution.~ ~ The Rayleigh is a special case of the Weibull distribution -- Weibull(2,sqrt(2)*mode). It also coincides with Chi-Squared, conditional exponential, and the Rice distributions. Definition: if (isNotSpecified(singleSampleMethod)) then~ Sqrt((-2*mode^2*Ln(Uniform(0,1,over:mode))))~ else~ Sqrt((-2*mode^2*Ln(Uniform(0,1,~ singleSampleMethod:singleSampleMethod,over:mode)))) NodeLocation: 336,264,1 NodeSize: 64,24 WindState: 2,14,25,596,399 Function Negbinomial(r,p; over : ... optional atomic; singleSampleMethod : optional atomic numeric hidden ) Title: NegBinomial(r,p) Description: The number of events that occur in a binomial process with probability p of success until the r'th success occurs. Definition: if (isNotSpecified(singleSampleMethod)) then~ Poisson(Gamma(r,(1-p)/p))~ else~ Poisson(Gamma(r,(1-p)/p,singleSampleMethod:singleSampleMethod),~ singleSampleMethod:singleSampleMethod) NodeLocation: 96,344,1 NodeSize: 68,24 WindState: 2,341,91,498,284 Function Inversegaussian(A,B : positive ; over : ... optional atomic; singleSampleMethod : optional atomic numeric hidden ) Title: InverseGaussian(A,B) Description: The inverse gaussian distribution with location parameter A and scale parameter B. Used in reliability studies. Gives the first passage time in a standard Brownian motion with postive drift.~ ~ Some books refer to this as the Wald Distribution. Others define the Wald distribution as the special case where A=1.~ ~ This algorithm due to:~ * Michael, Schucany, and Haas (1976) Definition: if (isNotSpecified(singleSampleMethod)) then~ (~ var y:=Normal(0,1,over:A,B)^2;~ var x := a + (a^2*y - a * sqrt( 4*A*B*y + (A*y)^2 ) ) / (2*B);~ if uniform(0,1) * (A+x) <= A~ then x~ else a^2/x~ )~ else~ (~ var y:=Normal(0,1,singleSampleMethod:singleSampleMethod,over:A,B)^2;~ var x := a + (a^2*y - a * sqrt( 4*A*B*y + (A*y)^2 ) ) / (2*B);~ if uniform(0,1,singleSampleMethod:singleSampleMethod,over:A,B) * (A+x) <= A~ then x~ else a^2/x~ ) NodeLocation: 240,344,1 NodeSize: 68,24 WindState: 2,399,161,583,428 Function Wald(k : positive; over : ... optional atomic; singleSampleMethod : optional atomic numeric hidden ) Title: Wald(k) Description: The Wald distribution.~ See also InverseGaussian -- some texts call that distribution the Wald distribution. Definition: if (isNotSpecified(singleSampleMethod)) then~ Inversegaussian(1,k)~ else~ Inversegaussian(1,k, singleSampleMethod: singleSampleMethod) NodeLocation: 368,344,1 NodeSize: 48,24 WindState: 2,225,16,558,314 Function Lorenzian(mode,scale; over : ... optional atomic; singleSampleMethod : optional atomic numeric hidden ) Title: Lorenzian (mode,scale) Description: The Lorenzian distribution (also known as Cauchy, Cauchy-Lorenz, Lorenz, and Breit-Wigner) is a continuous bell-shaped distribution having the indicated mode, and with the second parameter specifying the half-width at the half-maximum density. ~ ~ It has uses in physics, especially in the study of resonance and spectroscopy where it describes the shape of spectral lines that are broadened through various resonances.~ ~ The standard form, in which mode=0 and shape=1, is known as the standard Cauchy distribution.~ ~ The Lorenz distribution has some unusual mathematical properties that are uncommon among the standard distributions. Its mean, variance and higher moments are all undefined. As a result, the law of large numbers does not apply to samples generated from a Lorenz distribution.~ ~ One other property of interest: The ratio of two standard normal random variables follows a standard Cauchy distribution. Definition: if (isNotSpecified(singleSampleMethod)) then~ mode + scale*tan( uniform(-90,90,over:mode,scale) )~ else~ mode + scale*tan( uniform(-90,90,~ singleSampleMethod:singleSampleMethod,over:mode,scale)) NodeLocation: 480,264,1 NodeSize: 64,24 WindState: 2,123,22,717,448 Function Triangular10_50_90(p10,p50,p90 ; noErr : optional boolean = 0; over:... optional atomic; ~ singleSampleMethod : optional hidden scalar ) Title: Triangular10_50_90 Description: This defines a trianglar distribution given percentiles p10 <= p50 <= p90.~ ~ There are a couple downsides of defining a triangular distribution using percentiles, rather than using Min-Mode-Max as is done with the built-in Triangular distribution function. First, the percentiles do not uniquely specify the triangular distribution. When p10 and p90 are on opposite sides of the mode, there are often two possible triangular distributions with the indicated fractiles, and when p10 and p90 are on the same side of the mode, there is a fully unconstrained degree of freedom, leading to an infinite number of triangular distributions matching the fractiles. In these cases, this function will select one of the possible consistent distributions.~ ~ In addition to non-uniqueness, there are some combinations of p10<=p50<=p90 that have no triangular distribution with the indicated percentiles. This may occur when p50-p10 << p90-p50 or p50-p10 >> p90-p50 (where << means **much** less than). In this case, an error results. You can suppress this error by setting the noErr parameter to false, in which case a triangular distribution not precisely matching the indicated p10, p50 and p90 percentiles is returned. In many cases, the non-precise distribution will be close to the percentiles, but in some cases it may be substantially different. Definition: if (p10=p50 and p50=p90) then Triangular(p10,p50,p90)~ else begin~ ~ var flip := p50-p101e-6 and iter<100) (~ a := (y90-y10) / (ln(x90-c) - ln(x10-c) );~ var da := (y90-y10) * (x10-x90) / ( (c-x10)*(c-x90)*(ln(x10-c)-ln(x90-c))^2 );~ b := exp( -(y10 - a*ln(x10-c))/a );~ var db := exp(-y10/a) * (y10*(x10-c)*da/a^2 -1);~ var y50 := ((x50-c)/b)^a ;~ var u50 := (x50-c)/b;~ var dy50 := u50^a * (da * ln(u50) + (a*(x50-c)*db + b) / (b * (c-x50)));~ var cnext := Min([x10,c-(y50-ln(2))/dy50]);~ cNext := if cNext>=x10 then 0.9*cNext+0.1*c else cNext;~ c := cNext;~ if prevy50<>null then err := abs(prevy50-y50);~ prevy50 := y50;~ iter := iter+1~ );~ ~ if IsNotSpecified(singleSampleMethod) then ::Weibull(a,b)+c~ else ::Weibull(a,b,singleSampleMethod:singleSampleMethod)+c NodeLocation: 80,496,1 NodeSize: 48,24 WindState: 2,179,45,1073,754 Close Distribution_variati Variable Ergebnis_Gesamtdauer Title: Ergebnis Gesamtdauer Units: min Definition: Table(Version)(Anzahl_mal_Dauer,Sum( Dauer_je_Reparatur, Reparatur )) NodeLocation: 640,384,1 NodeSize: 72,32 ValueState: 2,351,22,877,634,,CDFP Aliases: FormNode Fo1347722109 GraphSetup: Att_AreaFill Graph_KDE_Valdim:1~ Att_ContLineStyle Graph_KDE_Valdim:6~ Att_AreaFill Graph_Pdf_Valdim:1~ Att_CatLineStyle Graph_Pdf_Valdim:1~ Att_ContLineStyle Graph_Pdf_Valdim:6~ Att_GraphValueRange Ergebnis_Gesamtdauer:|1:1,0,,,,,10 NumberFormat: 2,I,4,2,1,0,4,0,$,0,"ABBREV",0,,,0,0,15 {!40200|Att_GraphSetupSlices: [3,1,DensityIndex,1]} Variable Dauer_je_Reparatur Title: Dauer je Reparatur Units: min Definition: Dauer_Reparatur * ( Reparatur <= Anzahl_Reparaturen ) NodeLocation: 456,384,1 NodeSize: 72,32 ValueState: 2,198,15,994,684,,SAMP Constant Prod_Zeit_pro_MA_d Title: Produktive Zeit pro Mitarbeiter pro Tag Units: min Definition: 360 NodeLocation: 824,288,1 NodeSize: 72,40 Aliases: FormNode Fo1935973245 Variable Sicherheitsniveau Title: Sicherheitsniveau Definition: 95% NodeLocation: 456,488,1 NodeSize: 80,32 Aliases: FormNode Fo350526333 NumberFormat: 2,%,4,2,0,0,4,0,$,0,"ABBREV",0,,,0,0,15 Variable Perzentilwert_Gesamt Title: Perzentilwert Gesamtdauer Units: min Definition: GetFract( Ergebnis_Gesamtdauer, Sicherheitsniveau ) NodeLocation: 640,488,1 NodeSize: 72,32 ValueState: 2,228,234,416,303,,MIDM NumberFormat: 2,I,4,2,1,0,4,0,$,0,"ABBREV",0,,,0,0,15 Objective Max__Anzahl_Mitarbei Title: Max. Anzahl Mitarbeiter Definition: Ceil( Perzentilwert_Gesamt / Prod_Zeit_pro_MA_d ) NodeLocation: 824,488,1 NodeSize: 72,32 Aliases: FormNode Fo971283325 Objective Anzahl_Mitarbeiter Title: Anzahl Mitarbeiter Definition: Ceil( Ergebnis_Gesamtdauer / Prod_Zeit_pro_MA_d ) NodeLocation: 824,384,1 NodeSize: 72,32 ValueState: 2,282,51,554,604,,PDFP Aliases: FormNode Fo2045025149 ReformVal: [Undefined,Version,Undefined,Undefined,1] {!40600|Att_ClusterIndex: Version} Index Version Title: Version Definition: ['Falsch','Richtig'] NodeLocation: 640,280,1 NodeSize: 72,32 Att_PrevIndexValue: ['Falsch','Richtig'] Variable Anzahl_mal_Dauer Title: Anzahl mal Dauer Units: min Description: Das ist die falsche Modellierung. Definition: Anzahl_Reparaturen * Dauer_Reparatur[ @Reparatur = 1 ] NodeLocation: 456,288,1 NodeSize: 72,32 ValueState: 2,312,69,759,516,,CDFP NodeColor: 65535,19661,19661 GraphSetup: Att_AreaFill Graph_Pdf_Valdim:1 NumberFormat: 2,I,4,2,1,0,4,0,$,0,"ABBREV",0,,,0,0,15 FormNode Fo1399102333 Title: Parameter Reparaturen Definition: 0 NodeLocation: 168,88,1 NodeSize: 120,16 Original: Param_Reparaturen FormNode Fo862231421 Title: Parameter Dauer Definition: 0 NodeLocation: 168,128,1 NodeSize: 120,16 Original: Parameter_Dauer FormNode Fo1935973245 Title: Produktive Zeit pro Mitarbeiter pro Tag Definition: 0 NodeLocation: 300,168,1 NodeSize: 252,16 Original: Prod_Zeit_pro_MA_d FormNode Fo350526333 Title: Sicherheits-niveau Definition: 0 NodeLocation: 420,128,1 NodeSize: 132,16 Original: Sicherheitsniveau FormNode Fo971283325 Title: Max. Anzahl Mitarbeiter Definition: 1 NodeLocation: 736,168,1 NodeSize: 144,16 Original: Max__Anzahl_Mitarbei FormNode Fo2045025149 Title: Anzahl Mitarbeiter Definition: 1 NodeLocation: 736,128,1 NodeSize: 144,16 Original: Anzahl_Mitarbeiter Text Te1004837757 Title: Inputs NodeLocation: 300,116,-1 NodeSize: 268,84 NodeInfo: 1,,,,,1 Text Te605330301 Title: Outputs NodeLocation: 740,116,-1 NodeSize: 156,84 NodeInfo: 1,,,,,1 FormNode Fo1347722109 Title: Ergebnis Gesamtdauer Definition: 1 NodeLocation: 736,88,1 NodeSize: 144,16 Original: Ergebnis_Gesamtdauer Close Multi_D_Demo