21.1 Scilab Brownov model 468 PRILOGA. By: Dejan Dragan [80] // brown.m =========================== function brown(d,alfa) fakt = 5;
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1 Poglavje 21 PRILOGA
2 468 PRILOGA 21.1 Scilab By: Dejan Dragan [80] Brownov model // brown.m =========================== function brown(d,alfa) fakt = 5; N = length(d); t = [1:1:N]; // izhodi prediktor-filtra (projekcija prihodnosti za en korak naprej - napoved kolicine za cas t+1 ob casu t): // (relevantno je k = 2:N) p = []; p(1) = d(1) p(2) = d(1) for k=3:n p(k) = alfad(k-1) + (1-alfa)p(k-1) ; disp( p= ) disp(p) p_pred = alfad(n) + (1-alfa)p(N) disp( p_pred= ) disp(p_pred) // Narisemo zahtevano kolicino d in njeno napoved p: scf(0) plot(t,d, b ); set(gca(), auto_clear, off ) plot(t,d, bo ) plot([t(2:n) N+1],[p(2:N) p_pred], r ); plot([t(2:n) N+1],[p(2:N) p_pred], ro )
3 21.1 Scilab 469 title( zahtev.kolic.d (b) ob casu t in njena napoved p (r), narejena ob casu t-1 ) // pogresek (kar se dejansko zgodi ob casu t minus kar smo napovedali ob casu t-1): e = d(2:n)-p(2:n) ; disp( e= ) disp(e) scf(1) plot(t,[0 e], k ) plot(t,[0 e], ko ) title( pogresek napovedi kolicine ) mtlb_axis([0 N -max(d)/fakt max(d)/fakt]) // izracun kriterij. funkcije MAD ea = 0; for i = 1:length(e) ea = ea + abs(e(i)); MAD_N = ea/(n-1) disp( MAD_N= ) disp(mad_n) function Holtov model // holt.m =========================== // (pri variab. linear. tru) function holt(d,alfa,beta,stlet) N = length(d); t = [1:1:N];
4 470 PRILOGA tao = [1:1:stlet]; // vektor premika projekcije v prihodnost od trenutka N naprej //alfa = 0.3; //beta = 0.3; // konstanti holt prediktor filtra [ah,bh] = holt_rekurzija(d,alfa,beta) // vrne ah(k) in bh(k) - AD1:prvo ju "naucis" preko k=1:n, kaka sta optimalna disp( ah= ) disp(ah) disp( bh= ) disp(bh) scf(0) set(gca(), auto_clear, off ) subplot(211) plot(t,ah, b ) plot(t,ah, bo ), title( holtov parameter ah(k) ) subplot(212) plot(t,bh, r ) plot(t,bh, ro ), title( holtov parameter bh(k) ) scf(1) p = ah(n) + bh(n)tao // p(n+tao) = ah(n) + bh(n)tao, tao = 1,2,...; AD2: nato ah(n) in bh(n) uporabis za predikc. pri t>n disp( p= ) disp(p) // Narisemo zahtevano kolicino d in njeno napoved p: plot(t,d, b ); plot(t,d, bo ) plot(n+tao,p, r );plot(n+tao,p, ro ) title( Zahtevane kolicine (k = 1:N) in predikcija (k>n) ) function
5 21.1 Scilab Regresijski model // regresija.m =========================== function regresija(d,stlet) N = length(d); t = [1:1:N]; t_pred = [N+1:1:N+stlet] scf(0) set(gca(), auto_clear, off ) plot(t,d, b ) plot(t,d, bo ) title( Povprasevanje ) xlabel( t ) t_s = sum(t)/n d_s = sum(d)/n disp( t_s= ) disp(t_s) disp( d_s= ) disp(d_s) mean_t_d = td /N mean_t_t = tt /N disp( mean_t_d= ) disp(mean_t_d) disp( mean_t_t= ) disp(mean_t_t) clen1 = mean_t_d clen2 = t_sd_s clen3 = mean_t_t clen4 = t_st_s disp( clen1= ) disp(clen1) disp( clen2= ) disp(clen2) disp( clen3= ) disp(clen3)
6 472 PRILOGA disp( clen4= ) disp(clen4) a = (clen1 - clen2)/(clen3 - clen4) b = d_s - at_s disp( a= ) disp(a) disp( b= ) disp(b) d_o = at+b // model disp( d_o= ) disp(d_o) scf(1) plot(t,d, b ) plot(t,d, bo ) plot(t,d_o, r ) plot(t,d_o, ro ) title( Povprasevanje d(modro)in model d_m(rdece), predikcija+-2std (crno) ) xlabel( t ) e = d - d_o disp( e= ) disp(e) e_sr = sum(e)/n disp( e_sr= ) disp(e_sr) VAR = (e-e_sr)(e-e_sr) /(N-1) disp( VAR= ) disp(var) stde = sqrt(var) disp( stde= ) disp(stde) d_o_pred = at_pred + b d_o_pred_zg = d_o_pred + 2stde d_o_pred_sp = d_o_pred - 2stde
7 21.1 Scilab 473 disp( d_o_pred= ) disp(d_o_pred) disp( d_o_pred_zg= ) disp(d_o_pred_zg) disp( d_o_pred_sp= ) disp(d_o_pred_sp) plot(t_pred,d_o_pred, ko, linewidth,2) plot(t_pred,d_o_pred_zg, ko, linewidth,2) plot(t_pred,d_o_pred_sp, ko, linewidth,2) function Funkcija napovedovanja (Forecast) // forecast-main =========================== function forecast-main() clear clc dch = input( Povprasevanje rocno (1), default(2), nakljucno (3) ) if dch == 1 d = input( povprasevanje d=? npr. [ ] ) if dch == 2 dd = input( primer1 (1), primer2 (2), primer3 (3), primer4 (4) ) if dd == 1 d = [ ] disp( Daj regresijo ) if dd == 2 d = [ ] disp( Daj regresijo ) if dd == 3 d = [ ] disp( Daj Brown ) d = [ ] disp( Daj Holt )
8 474 PRILOGA tt = input( Koliko vzorcev za d ) d(1) = input( d(1)= ) hh = input( konstanten tr(1)/linearen (2) ) srvr = d(1) stres = d(1)/20 d_rand = grand(1,tt, unf,srvr-stres,srvr+stres) if hh == 1 d = d_rand strm = d(1)/10 konst = d(1) d = strm[1:1:tt] + konst + d_rand if dch == 3 if hh == 1 ch = 2; disp( Delamo Browna ) ch = input( regresija, mnk(1)/holt(3) ) ch = input( regresija, mnk(1)/brown(2)/holt(3) ) if ch == 1 stlet = input( Za koliko let predikcija = ) if ch == 2 alfa = input( alfa = ) stlet = input( Za koliko let predikcija = ) alfa = input( alfa = ) beta = input( beta = ) disp( d= ) disp(d) if ch == 1 regresija(d,stlet) if ch == 2 brown(d,alfa) holt(d,alfa,beta,stlet) function
9 21.2 GPSS World GPSS World By: Borut Jereb [74] Model MODEL =========================== Time is in minutes Initialization GENERATE,,,1 SAVEVALUE TrafficLight,Green ENTER StoRim,StartNoRim ENTER StoTire,StartNoTire ENTER StoScrew,StartNoScrew Rim section begin Input to warehouse :: rims GENERATE InRimTimeMean,InRimTimeRange QUEUE QueueInWarehouse,1 TEST GE R$StoRim,InCapVehRim TEST E X$TrafficLight,Green SAVEVALUE TrafficLight,Red ENTER StoRim,InCapVehRim ADVANCE InVehManipulRim,InVehManipulRimRange SAVEVALUE TrafficLight,Green DEPART QueueInWarehouse,1
10 476 PRILOGA Otput from warehouse :: rims GENERATE OutRimTimeMean,OutRimTimeRange TABULATE TableRim TEST GE S$StoRim,OutCapVehRim,RimStorageEmpty LEAVE StoRim,OutCapVehRim RimStorageEmpty Rim section Tire section begin Input to warehouse :: tire GENERATE InTireTimeMean,InTireTimeRange QUEUE QueueInWarehouse,1 TEST GE R$StoTire,InCapVehTire TEST E X$TrafficLight,Green SAVEVALUE TrafficLight,Red ENTER StoTire,InCapVehTire ADVANCE InVehManipulTire,InVehManipulTireRange SAVEVALUE TrafficLight,Green DEPART QueueInWarehouse,1 Otput from warehouse :: tire GENERATE OutTireTimeMean,OutTireTimeRange TABULATE TableTire TEST GE S$StoTire,OutCapVehTire,TireStorageEmpty LEAVE StoTire,OutCapVehTire TireStorageEmpty Tire section
11 21.2 GPSS World 477 Screw section begin Input to warehouse :: screw GENERATE InScrewTimeMean,InScrewTimeRange QUEUE QueueInWarehouse,1 TEST GE R$StoScrew,InCapVehScrew TEST E X$TrafficLight,Green SAVEVALUE TrafficLight,Red ENTER StoScrew,InCapVehScrew ADVANCE InVehManipulScrew,InVehManipulScrewRange SAVEVALUE TrafficLight,Green DEPART QueueInWarehouse,1 Otput from warehouse :: screw GENERATE OutScrewTimeMean,OutScrewTimeRange TABULATE TableScrew TEST GE S$StoScrew,OutCapVehScrew,ScrewStorageEmpty LEAVE StoScrew,OutCapVehScrew ScrewStorageEmpty Screw section Simulation duration GENERATE,,SimDur,1 ;Duration in minutes 1 START 1
12 478 PRILOGA Vhodni podatki VHODNI PODATKI =========================== Time is in minutes By: Borut Jereb StoRim StoTire StoScrew STORAGE 800 ; Warehouse storage capacity for rims STORAGE 800 ; Warehouse storage capacity for tires STORAGE 2600 ; Warehouse storage capacity for screws ; Frequency distribution table for rims, tires and screws TableRim TABLE S$StoRim,19,19,100 TableTire TABLE S$StoTire,19,19,100 TableScrew TABLE S$StoScrew,79,79,100 ; Initial number of items in the warehouse (at the start time of simulation) StartNoRim EQU 200 StartNoTire EQU 200 StartNoScrew EQU 800 ; Mean arrival time for rims, tires and screws InRimTimeMean EQU 180 InTireTimeMean EQU 360 InScrewTimeMean EQU 170 ; Arrival_time = (InXXTimeMean-InXXTimeRange..InXXTimeMean+InXXTimeRange) ; XX = {Rim, Tire, Screw} InRimTimeRange EQU 10 InTireTimeRange EQU 20 InScrewTimeRange EQU 10 ; Capacity of one input vehicle carrying rims, tires and screws InCapVehRim EQU 390 InCapVehTire EQU 800 InCapVehScrew EQU 1450 ; Vehicle upload manipulation time for rims, tires and screws InVehManipulRim EQU 6 InVehManipulTire EQU 6 InVehManipulScrew EQU 6 ; Mean depart time for rims, tires and screws OutRimTimeMean EQU 8 OutTireTimeMean EQU 8 OutScrewTimeMean EQU 8
13 21.2 GPSS World 479 ; Departure_time = (OutXXTimeMean-OutXXTimeRange..OutXXTimeMean+OutXXTimeRange) ; XX = {Rim, Tire, Screw} OutRimTimeRange EQU 1 OutTireTimeRange EQU 1 OutScrewTimeRange EQU 1 ; Capacity of one input vehicle carrying rims, tires and screws OutCapVehRim EQU 20 OutCapVehTire EQU 20 OutCapVehScrew EQU 80 ; unloading manipulation time for rims, tires and screws InVehManipulRim EQU 6 InVehManipulTire EQU 6 InVehManipulScrew EQU 6 ; Unloading_time = unloading manipulation time for rims, tires and screws InVehManipulRimRange EQU 0 InVehManipulTireRange EQU 0 InVehManipulScrewRange EQU 0 Green EQU 1 ; Boolean value for green is 1 Red EQU 0 ; Boolean value for red is 0 SimDur EQU 7200 ; Simulation duration in minutes
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