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wip: wdwr
This commit is contained in:
parent
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1001
Programming/WDWR/projekt/R/scenarios.txt
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1001
Programming/WDWR/projekt/R/scenarios.txt
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File diff suppressed because it is too large
Load Diff
18
Programming/WDWR/projekt/R/t-student.R
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18
Programming/WDWR/projekt/R/t-student.R
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@ -0,0 +1,18 @@
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library(MASS) # mvrnorm()
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mu <- c(9, 8, 7, 6); # expected values
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sigma <- matrix(c(16,-2,-1,-3,-2,9,-4,-1,-1,-4,4,1,-3,-1,1,1), 4); # covariance matrix
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nu <- 4; # degrees of freedom
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n <- 10000; # number of scenarios
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y <- t(t(mvrnorm(n, rep(0, length(mu)), sigma) * sqrt(nu / rchisq(n, nu))) + mu)
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y <- y[y[,1]>=5,]
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y <- y[y[,2]>=5,]
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y <- y[y[,3]>=5,]
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y <- y[y[,4]>=5,]
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y <- y[y[,1]<=12,]
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y <- y[y[,2]<=12,]
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y <- y[y[,3]<=12,]
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y <- y[y[,4]<=12,]
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y <- y[1:1000,]
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write.table(y, "scenarios.txt", sep=" ", eol="]\n[", row.names = FALSE, col.names=FALSE)
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@ -1,101 +0,0 @@
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1 9.76311690917431 7.44135196072098 8.03080769529443 5.91262278007238
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2 9.07190145652961 7.65985278468609 7.81364884192831 6.23563982924631
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3 7.30586779807710 10.46831565453596 6.17860514098604 5.62316983488756
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4 6.06518115356950 6.82247048657865 6.70439394704260 5.97874895226848
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5 9.96818163646533 5.92140003729828 8.95647683901296 5.92957172143291
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6 8.63538473131973 10.78452347210609 5.33874254384498 5.71561846706637
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7 10.78779012148249 8.38993971621506 6.95119513298764 6.15728792196850
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8 9.47255420321130 5.24835077098752 9.00959525502073 6.84285948274497
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9 11.70926888809931 6.88871099028457 5.11505870322385 5.79141681866100
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10 9.64287670970948 6.99374980548690 6.93364646280594 6.63473958816273
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11 9.74417642254796 9.27515630991583 5.79381433859841 6.33880079977867
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12 9.84932031583696 8.13369675991747 9.56326564416240 5.47164097940741
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13 10.84180657665224 10.44173212698402 5.75796156005717 5.17082844375342
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14 6.42546815399896 11.41163561400132 6.98718847694160 6.19182014565093
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15 6.65343747480972 5.96491367509542 8.43404931472053 6.81514933519179
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16 8.41058204665803 9.81581254769240 6.22528708513955 5.45922484668070
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17 11.07466468261044 8.63685491994975 8.32064327582552 6.00532812753081
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18 5.66007803163167 10.59708873150259 6.40117913595970 6.61684806440770
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19 8.11097377453294 11.39989719535908 7.19879715792936 5.81867289870583
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20 10.18544068664910 8.00246586468051 7.78414367307899 6.39143329793770
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21 8.37417778106186 8.66901423662847 6.63403929668899 5.62622698569807
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22 5.71457990443940 8.10569739243788 6.93419305872531 6.80590758355993
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23 10.90693358349595 7.14177718551252 5.91236679453024 5.76232274027454
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24 8.56484398609648 6.22762158139036 8.11135450623580 5.62211012798539
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25 10.96985518991559 6.68271088560539 6.35539102212538 5.78652050813314
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26 7.62410553749944 5.47750296350388 8.10356514381925 6.80674322754784
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27 9.17309223739274 7.35075102155314 9.39642670821667 6.08239889804591
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28 11.69619190069919 5.81353083316989 6.71866562538033 5.83460977495301
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29 8.70647218713260 8.54952873879837 6.01177366865153 5.80247711748981
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30 10.19801770668396 8.40706649849486 7.78289804784438 5.59571260544491
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31 10.18823792131140 10.30622317535783 6.01007216642801 5.10730031536544
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32 8.89257975110205 9.05255653431256 7.04806430135429 5.72317637496039
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33 5.55169965201331 6.46995754365869 6.73752334538523 8.11225243525766
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34 7.38041303280808 6.03431805441005 6.54177632374026 6.26813757511362
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35 9.53600312164939 9.82870887668159 6.37298666598656 6.29512946813344
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36 9.62084627760301 8.38877821804972 9.60663262550341 6.14199642708027
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37 8.53371147142969 9.96028980160928 5.48461964749732 5.58700748945751
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38 11.43883601853922 7.43794182684747 8.19299665196631 6.53522296979568
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39 8.94531533108458 7.72770669562578 5.86612093622741 6.14249195015319
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40 8.56163972282782 8.78267109358917 8.09620140224837 5.84072057986787
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41 11.10581874422519 6.41650146716222 7.94880945361463 5.92181030289048
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42 8.79709510873987 6.96326671422654 6.81226688976441 6.08034755239137
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43 8.36353994459570 8.65114541354765 5.20396052285658 5.64107960525657
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44 10.72631955621739 10.40656722635996 6.40361955726036 5.00588106915542
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45 7.92377449146379 7.57337326655799 7.28981137176010 5.86937295871601
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46 6.44898752031030 9.84455679079283 7.56743321024807 5.61123930238106
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47 6.59142079861976 8.25490687097256 7.18086136653190 6.36808055523271
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48 7.47434811723050 7.57534400043646 7.40042557613225 5.77766925416132
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49 10.37922558429701 7.51649541751660 7.76007217627671 5.71165340725927
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50 11.22955957441164 9.11763834237000 5.91162025004763 5.22909656515264
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51 9.93626606347435 10.36742543458079 5.50414492338928 5.09387877291345
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52 7.46956075160300 8.85209992891748 5.66758603774144 6.84847007082318
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53 10.94818425908296 11.21395252595940 5.02869416739786 5.11896599852232
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54 8.59930902434361 10.23728557433725 6.43052152866663 5.55350545843695
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55 7.18223178367374 8.39628843993716 6.78631274384210 6.40826198282814
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56 10.50650695671218 7.14292577351122 6.97271222602888 5.67492382261040
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57 9.43266157452829 8.59707825452923 7.44232496740155 6.33561282221721
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58 11.45217548324165 6.23806997675535 7.62498135882166 5.84032703043151
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59 11.36423152742698 7.73909888403765 5.83487677225264 5.30267540082782
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60 7.40126925234506 7.78983701703254 7.26783254076869 6.35068014074993
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61 9.72974571794505 8.14279615037054 6.43258828020491 5.60905301174744
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62 9.71958329969389 8.25791366588767 6.81451687462449 5.84590714949131
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63 11.78371126050813 7.89117263445312 8.21483466145989 5.60333890798316
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64 6.43302225580614 8.07865860135554 5.44469004007024 6.25543936966105
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65 11.32524672426065 6.23439997420789 9.09984135190236 5.92522151214137
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66 6.11523920037522 9.46855519033448 8.99139954457499 7.26985178631296
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67 10.75986956011578 8.89269588219645 6.16473844457922 5.01830225504361
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68 7.44261078629228 5.59781415962693 8.47555931122058 6.71081007297257
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69 6.13170779809489 7.22655838486716 7.72995785629984 6.90573736168416
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70 9.43027479825457 11.48319339258313 7.38708836041081 5.82689865183140
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71 5.29171692626969 8.09437078836054 6.39563010794279 6.62912879511093
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72 5.09893294234523 7.74584955672930 8.88018284918212 7.01212790493029
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73 6.20578320069247 6.80617867447727 8.18103843191598 6.83958319443877
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74 6.30258685494809 7.43138054831968 6.54063201012806 6.49666135387873
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75 5.99078754250444 9.43740619647565 6.33367827869915 6.32580787466866
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76 6.09773092392164 8.81422152842116 6.40425733606985 6.00528108344304
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77 9.77514087744415 8.44932475630892 7.94420117667674 5.69585236521884
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78 9.52359733569984 5.16479502907556 8.95174376770244 6.78672071859107
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79 10.87512003895627 7.94035821507262 7.57773074672391 5.84630709056388
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80 7.03228696898568 6.60340093608582 6.44468568443036 6.19149860676074
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81 9.65992578732530 8.56527229906357 6.09872442954240 5.20440692261623
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82 6.30983535489480 9.30561393417605 6.54859829930319 6.52106231396463
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83 5.82137666724151 5.35827290855938 8.49191286969823 6.71371780289945
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84 11.37494716877100 5.81213960381298 8.05853844161706 5.69806024484299
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85 8.15267985634545 10.97224605386566 5.12446664947324 5.15795103491506
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86 11.73086416587584 5.65124348656024 6.77078823854425 5.90352878508404
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87 6.72960532844368 8.10521896869132 11.60799014602290 5.36046426627923
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88 5.25102730028672 6.84949245261905 8.49279919952933 6.29483292662839
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89 7.90449528978985 7.68346606583354 8.26036057308542 5.78474731288598
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90 8.64103203208981 6.57844735571956 8.12280488732531 7.36240059132464
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91 8.46655660484256 8.36699115351209 6.70419093097301 6.03113661709298
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92 8.50248558815979 6.35585412244623 5.95963894605528 5.55306703237114
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93 8.11096815930533 6.26958795090084 6.89847746026576 6.79423199764579
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94 11.05399477820485 6.64260318869504 8.29929372751188 6.03566117360477
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95 9.56800557695653 8.79263357467837 8.58788350167046 6.06774627920175
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96 11.90421443450454 6.78214769706352 9.14111391052618 5.69759901474202
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97 7.57911702883690 7.65716973584061 7.10622237301328 5.97627526735100
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98 5.12673628692985 11.33334016193997 7.83474212553888 5.91330252723919
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99 8.25983441252135 10.48661063799430 6.24054516528815 5.59523265380788
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100 6.37952025900229 7.91998372816597 10.27190171308190 6.82383475873556
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Binary file not shown.
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Load Diff
@ -83,7 +83,7 @@ var storage {m in MONTHS, p in PRODUCTS} =
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startingStorage[p] + sum {m2 in MONTHS: ord(m2) <= ord(m)} stored[m2, p];
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# Czas pracy narzedzi w danym miesi<EFBFBD>cu
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var availableToolTime {t in TOOLS} =
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param availableToolTime {t in TOOLS} =
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toolCount[t]*workHoursPerMonth;
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var usedToolTime {m in MONTHS, t in TOOLS} =
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sum {p in PRODUCTS} produced[m,p]*toolTimePerUnit[t,p];
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101
Programming/WDWR/projekt/old_code/data100.txt
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101
Programming/WDWR/projekt/old_code/data100.txt
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@ -0,0 +1,101 @@
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1 8.38640970547137 6.65206459832990 9.25340899939379 6.12210936270431
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2 11.47174561833503 6.88159003640127 9.35722657222265 5.55953923603233
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3 9.35744107333980 7.76050018607139 7.52248263888872 6.66070883253532
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4 11.79462832089452 8.84904849612396 9.54663595886239 5.86135113067846
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5 10.60333708223467 6.98611327352300 6.65687557067754 5.79843895114186
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6 9.99227622682918 8.40138468818306 7.29067133904427 5.54635515729128
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7 6.85386681919369 9.61502157413213 6.72665279049544 6.17021595187704
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8 7.22585612837712 5.98643566600382 7.66228380776991 7.24401202983452
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9 6.02120294740299 8.82748462758594 7.54667264901398 6.57719185154700
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10 7.81781171233356 8.62835915477718 6.69726720537751 6.37457957246354
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11 7.41500923685725 7.43485281864021 7.99043379062193 6.48482328886511
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12 8.03069531050207 6.25690178404796 6.09093011988645 5.67595896061141
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13 8.99392642494794 8.28778594511316 6.13973684710108 5.83600264194769
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14 6.24245696460346 6.39232539486031 10.84375535721419 6.36547052046049
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15 9.01023319607629 11.68740944894835 5.69616732483115 6.19037506643131
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16 9.65669838710216 7.92589484304334 6.60602391314122 6.09008609139752
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17 8.01383159007860 8.87589828833432 6.18744116565941 6.46341139819787
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18 6.41168277703518 11.65174704526084 5.49260854548204 5.76322499127593
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19 11.86071617832331 5.58525736824701 7.17211512854709 5.06082016193722
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20 6.16988486968262 11.78961088001470 6.84648432837472 6.13006129755091
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21 10.50307551032826 8.35431195348345 5.24642726566315 6.32424486960449
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22 10.20706145000350 8.50839048007318 8.52712330116200 5.50646744442542
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23 6.67901786642918 5.58637733835291 8.63780188181508 9.79688443744826
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24 5.83844381890308 8.44063550564077 11.22412896684246 6.13391763721968
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25 5.02725511916500 10.03499520640502 7.80231695276266 6.54152914586194
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26 7.91401589299405 9.43722696656540 5.35523258143446 5.59975014714696
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27 7.98004967073562 8.17745136624177 6.32951390326732 5.63731883625499
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28 7.98467051395001 11.57545107974183 6.83123357010320 6.27858288227011
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29 6.34805208941412 7.25317285835048 7.58786865181772 6.59504070525953
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30 7.08336994763901 8.92594639512021 5.92035329828115 5.98401448315642
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31 9.06644162062782 8.98837754871222 6.19341005514600 6.00305466546488
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32 8.48729551927738 9.17269835317290 5.36419157013662 5.59485038217624
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33 8.66648920402347 6.93300043060390 8.38462196805040 6.03127556347140
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34 11.83627063752078 8.90221524236904 7.55194888744446 5.19695360377865
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35 5.09649338467916 6.60060145222713 9.36785231110391 7.06996018633803
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36 10.14756579853665 8.47018085753289 7.61659609253176 5.72166332448810
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37 10.88873903559452 9.06214456213366 7.61335949698699 5.47398480579050
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38 8.66435312312922 5.81229049155926 8.55479425754780 6.47574711245415
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39 7.77804978645783 8.38793000591525 6.76635383352524 6.66232938066654
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40 5.96600192497750 10.92926350511743 5.40151489712682 6.59120257044565
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41 5.59690630619186 9.91317237753740 5.07563394246842 5.64387291599931
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42 6.74012145870261 9.04595375599635 9.06481634407877 6.91232814922274
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43 6.03915855388625 7.71614169885073 8.28544678648988 6.36744151062543
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44 10.68083156161457 5.35912754560182 8.45534166067838 5.98217635572583
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45 7.86523236391356 10.61707772747047 6.51071080752149 5.80556023866074
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46 10.59881295279743 8.70314595132368 6.93415201276310 6.13852414071696
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47 7.06365029787463 9.07856555481288 7.53503836286952 6.52970502515429
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48 9.66573092179861 9.51712670813631 7.56413274760444 5.43826427090938
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49 10.55908768529984 6.53006759211473 5.23363222181494 6.69026800097002
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50 8.37783980089781 11.34206626209622 8.52375903867853 5.00311515741636
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51 8.60678360655386 6.63301566152829 7.46356953969518 6.93623756199315
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52 8.10058227419441 9.67607064120733 5.07417893078212 5.92823677000808
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53 11.86328450036617 8.18571575346197 7.46703693228165 5.34308211339548
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54 10.91964716957687 6.32937436794021 6.97739368373887 5.31821065018457
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55 10.69583569884245 6.60565970760443 8.29033521579438 5.88004180161757
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56 7.99079208388755 10.25400562851183 8.83683368944479 5.80492792288328
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57 6.23273822505752 9.36429957708976 6.87922116365394 6.78874486297183
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58 5.88386384170986 8.59117274674032 7.72631503884364 6.09387234225471
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59 5.40661550602730 7.28181911598468 6.79417546649187 6.21515229248254
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60 8.26547024374017 8.25847144246441 7.78496725231724 5.98659129929383
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61 7.19259795212615 7.40286958820430 5.78243225576006 6.64588580256380
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62 6.21344556592916 8.87805667457996 8.92385174527858 6.65881574936494
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63 8.18272681675361 7.51144843783451 7.00028011235120 6.37742187397881
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64 9.33166132930610 7.14827316983078 8.19261176835498 5.48345528030911
|
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65 5.67795830603908 5.93268424509028 7.52055184914644 6.20004184094185
|
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66 6.61224775512171 11.07008594498179 5.59809093557888 5.76022429445898
|
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67 9.18526157242251 10.33093428932847 6.20248135198736 5.69069389194741
|
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68 10.35460673197286 6.31819949708236 7.26007034644003 5.98527710153962
|
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69 11.41775692269698 5.38885381828595 8.60474576946179 6.01560597252629
|
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70 7.51574587595340 10.20758422859382 7.95010579784885 5.84405786774390
|
||||
71 6.67147508169887 9.99676503954841 7.88196193877892 6.36471569850198
|
||||
72 8.81493026032693 9.55217236694784 6.88640408825999 5.85335196560115
|
||||
73 10.34100833805265 6.19309416422451 6.54276954947828 5.85229534744097
|
||||
74 8.21225622897127 9.19420261352311 5.50976361832561 6.50474402181068
|
||||
75 5.21712163609094 8.99345997972815 8.90935395647960 6.95465457249704
|
||||
76 7.19263314339272 7.75122339336118 6.55400244396229 6.17420524444947
|
||||
77 7.47042045387234 8.57992190152031 8.81054618695823 5.89474805758430
|
||||
78 9.40144483135410 8.12162393352070 6.87945218422346 5.32293412183378
|
||||
79 10.43568612418940 7.56903040482999 8.11467631685016 6.15185553754121
|
||||
80 6.89788205797093 10.49090726456854 5.60573364312945 5.90016825245131
|
||||
81 8.40187075462482 6.63200111204084 8.80002231974220 6.52807896556919
|
||||
82 11.09530562833293 7.17846988472875 6.17825717640593 5.15314774961649
|
||||
83 5.30169200307174 10.00677222950092 7.02250570949688 6.58898550622257
|
||||
84 7.12941565748076 10.79383482977027 6.94724538603335 5.63368869104514
|
||||
85 10.51315181327761 7.97803822244174 6.23221470948432 5.12188223474996
|
||||
86 9.60719281025592 8.06562427926403 6.72890591184924 5.37227501894104
|
||||
87 7.36932186905072 6.38892407121165 9.39023244605481 6.77826088317697
|
||||
88 6.18127154860016 9.06836518560135 7.99958754457252 6.45060931728576
|
||||
89 6.57666298108503 10.99205738028462 7.73908984591217 5.72724756133371
|
||||
90 6.32937368664344 9.62668784336028 5.84276743584105 6.21380406480346
|
||||
91 6.05330532218822 8.22361956350043 6.47166304886069 5.62673428466093
|
||||
92 9.79501831949422 6.36857961118238 7.54098230329907 6.39432823108518
|
||||
93 9.91808445954114 9.13469023862793 7.98132008822906 5.00940577737874
|
||||
94 10.68937894068579 9.95804209382347 5.31316321040921 5.01886724569659
|
||||
95 10.93776822739210 9.06480582511433 5.07546190641304 5.32852345019194
|
||||
96 8.91747138855106 8.89253508752477 7.04055074088613 5.94737846911610
|
||||
97 9.44216658550990 5.79280042118289 8.05260652410974 6.70053612755559
|
||||
98 8.06891980451287 11.30085871569205 6.46414921073781 5.58982387487253
|
||||
99 7.33190851491788 9.58145942349845 5.25495825638714 5.61862630847008
|
||||
100 9.66308325670658 10.43672680962318 5.88883182273180 5.27629211394324
|
||||
|
||||
53
Programming/WDWR/projekt/wdwr/result-minAvgProfit.txt
Normal file
53
Programming/WDWR/projekt/wdwr/result-minAvgProfit.txt
Normal file
@ -0,0 +1,53 @@
|
||||
i;minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4
|
||||
1;-600;-600;0;0;0;0;0;0;0;0;0;0;0;0;0;50;50;50;50;50;50;50;50;50;50;50;50
|
||||
2;-67.59000000000003;-67.19889931518434;9.26551;1;0;0;0;0;1;0;0;10;8;6;34;40;50;44;16;40;42;44;16;50;50;50;50
|
||||
3;464.82;465.107;19.0047;0;0;0;1;0;0;0;0;23;20;14;64;27;50;36;0;27;30;36;0;50;50;50;50
|
||||
4;997.23;997.357;29.4848;0;0;0;43;0;0;0;0;36;30;23;57;15;50;27;0;14;20;27;0;50;50;50;50
|
||||
5;1529.64;1530.25;39.9669;0;0;0;85;0;0;0;0;47;42;31;52;3;50;19;0;3;8;19;0;50;50;50;50
|
||||
6;2062.05;2062.08;50.8849;3;0;0;120;7;2;0;0;50;51;42;51;0;50;8;0;0;0;8;0;50;50;50;50
|
||||
7;2594.46;2594.52;62.1001;0;0;0;162;24;13;0;0;50;51;49;50;0;50;1;0;0;0;1;0;50;50;50;50
|
||||
8;3126.87;3127.18;73.5215;36;0;2;246;2;24;0;0;50;51;50;73;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
9;3659.28;3659.63;85.1089;44;0;8;248;8;32;1;0;50;53;50;116;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
10;4191.69;4192.2;96.6978;15;0;17;244;51;0;0;0;50;97;50;160;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
11;4724.1;4724.19;108.275;79;0;0;77;0;57;24;0;50;52;52;374;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
12;5256.51;5256.76;119.859;0;0;0;188;93;71;34;0;50;50;50;239;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
13;5788.92;5789.33;131.455;64;0;42;109;43;81;0;0;50;52;50;436;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
14;6321.33;6321.9;143.053;2;0;49;192;119;95;1;0;50;50;50;365;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
15;6853.74;6853.79;154.626;3;0;57;187;132;101;1;0;50;55;50;442;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
16;7386.15;7386.47;166.79;3;0;114;250;147;120;0;0;50;56;51;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
17;7918.56;7918.79;180.122;165;0;109;190;0;145;2;0;50;53;51;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
18;8450.97;8450.98;194.31;95;0;117;250;85;172;50;0;50;50;56;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
19;8983.38;8983.46;209.32;201;0;146;190;1;191;51;0;50;52;54;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
20;9515.79;9515.8;225.023;288;0;148;250;1;212;81;0;50;50;50;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
21;10048.2;10048.3;241.223;1;0;150;190;236;302;0;0;50;51;160;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
22;10580.6;10580.7;257.65;257;0;109;250;1;0;0;0;50;304;184;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
23;11113;11113.1;274.336;1;0;149;250;274;349;162;0;50;56;50;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
24;11645.4;11645.6;291.421;272;0;150;190;85;1;188;0;50;346;50;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
25;12177.8;12177.9;308.783;270;0;111;190;36;270;0;0;50;99;301;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
26;12710.2;12710.4;326.256;0;0;0;250;324;271;409;0;50;183;80;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
27;13242.7;13242.9;343.752;270;0;148;190;135;0;275;0;50;411;52;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
28;13775.1;13775.2;361.295;0;0;3;250;362;350;0;0;50;160;560;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
29;14307.5;14307.7;378.849;271;0;146;190;176;11;327;0;50;439;52;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
30;14839.9;14840;396.41;347;0;110;190;131;270;482;0;50;266;51;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
31;15372.3;15372.7;414.073;0;0;5;190;417;349;5;0;50;225;530;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
32;15904.7;15904.9;431.837;270;0;110;190;162;270;400;0;50;246;60;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
33;16437.1;16437.2;449.621;270;0;0;190;239;350;72;0;50;269;649;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
34;16969.5;16969.6;467.424;350;0;5;250;190;1;399;0;50;560;227;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
35;17501.9;17502;485.233;349;0;1;250;204;23;129;0;50;691;530;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
36;18034.3;18034.4;503.08;0;0;148;250;610;0;51;0;50;748;530;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
37;18566.8;18566.9;520.949;1;0;150;190;502;0;401;0;50;776;288;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
38;19099.2;19099.2;538.82;1;0;1;250;520;1;499;0;50;803;344;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
39;19631.6;19631.6;556.705;1;0;150;190;672;349;500;0;50;406;261;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
40;20164;20164;574.59;1;0;36;250;693;9;497;0;50;690;363;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
41;20696.4;20696.4;592.504;347;0;2;190;305;34;290;0;50;850;648;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
42;21228.8;21228.8;610.459;271;0;1;190;383;59;499;0;50;850;444;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
43;21761.2;21761.3;628.438;350;0;150;190;338;349;500;0;50;500;362;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
44;22293.6;22293.7;646.432;348;0;148;190;358;296;401;0;50;690;471;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
45;22826;22826.1;664.435;350;0;0;250;374;347;400;0;50;543;527;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
46;23358.4;23358.5;682.428;270;0;149;190;570;145;313;0;50;848;530;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
47;23890.9;23891;700.439;271;0;150;250;591;270;339;0;50;719;650;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
48;24423.3;24423.3;718.467;323;0;150;250;561;258;366;0;50;761;650;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
49;24955.7;24955.8;736.497;350;0;150;250;446;272;498;0;50;850;524;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
50;25488.1;25488.3;755.133;185;0;110;190;700;307;400;0;50;848;530;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
51;26020.5;26020.6;774.872;221;0;150;250;696;263;400;0;50;690;530;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
52;26552.9;26553.9;796.113;270;0;110;190;700;270;500;0;50;690;650;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
@ -0,0 +1,99 @@
|
||||
minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4
|
||||
10580.7;10580.8;257.666;336;0;149;190;0;1;134;0;50;301;51;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
avgProfit:
|
||||
10431.3
|
||||
10701.3
|
||||
10289.3
|
||||
10069.9
|
||||
10850.6
|
||||
10484.8
|
||||
10350.1
|
||||
10326.3
|
||||
10453.9
|
||||
10467.4
|
||||
10049.4
|
||||
10603.5
|
||||
11024.6
|
||||
11070.8
|
||||
10234.5
|
||||
10268
|
||||
10798.3
|
||||
9880.5
|
||||
10836.7
|
||||
11107.6
|
||||
10320.1
|
||||
11855.6
|
||||
9764.31
|
||||
11123.6
|
||||
10475
|
||||
11498.3
|
||||
10815
|
||||
10594.3
|
||||
9900.24
|
||||
10777.9
|
||||
minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4
|
||||
20164;20164;574.598;346;0;0;250;289;9;401;0;50;689;583;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
avgProfit:
|
||||
20154.7
|
||||
20455.9
|
||||
20073
|
||||
18945.5
|
||||
20449.9
|
||||
19784.6
|
||||
19458
|
||||
19834.6
|
||||
20212.1
|
||||
20071.6
|
||||
18646.7
|
||||
20721.6
|
||||
20994.7
|
||||
21696
|
||||
19350
|
||||
19462
|
||||
20773.9
|
||||
18405.2
|
||||
20631.2
|
||||
20984
|
||||
18959.4
|
||||
22145.5
|
||||
18483.8
|
||||
21647.1
|
||||
20124.6
|
||||
22142.5
|
||||
20811.8
|
||||
20397.9
|
||||
18593.3
|
||||
20510
|
||||
minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4
|
||||
26553.9;26553.9;796.113;270;0;150;190;700;270;400;0;50;850;530;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
avgProfit:
|
||||
26799.8
|
||||
27035.6
|
||||
26275.1
|
||||
24812.5
|
||||
26806
|
||||
25731.5
|
||||
25709.3
|
||||
26040
|
||||
26796.5
|
||||
26717.3
|
||||
24063.1
|
||||
27351.1
|
||||
27509.7
|
||||
28774
|
||||
25328.3
|
||||
25695.7
|
||||
27530.2
|
||||
24353
|
||||
26997.2
|
||||
27534.5
|
||||
24894.9
|
||||
28956
|
||||
24512.9
|
||||
28704.1
|
||||
26404.1
|
||||
29275.3
|
||||
27718.4
|
||||
27043.9
|
||||
24179.5
|
||||
27068.2
|
||||
@ -0,0 +1,99 @@
|
||||
minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4
|
||||
25488.3;25488.3;755.133;268;0;110;190;477;270;500;0;50;654;530;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
avgProfit:
|
||||
25609.4
|
||||
25873.7
|
||||
25413.2
|
||||
23878.9
|
||||
25764.6
|
||||
24872.7
|
||||
24604.1
|
||||
25059.6
|
||||
25695.3
|
||||
25506.2
|
||||
23307.3
|
||||
26302.3
|
||||
26478.7
|
||||
27585.3
|
||||
24356.9
|
||||
24579.8
|
||||
26361.5
|
||||
23240.8
|
||||
26012.6
|
||||
26449.8
|
||||
23820.7
|
||||
27877.6
|
||||
23386.8
|
||||
27485.6
|
||||
25413.4
|
||||
28068.6
|
||||
26454.6
|
||||
25893.9
|
||||
23353.4
|
||||
25942
|
||||
minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4
|
||||
26020.6;26020.8;774.88;218;0;110;190;700;266;400;0;50;846;530;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
avgProfit:
|
||||
26186
|
||||
26404.1
|
||||
25894.4
|
||||
24381.3
|
||||
26283.5
|
||||
25336.7
|
||||
25180.9
|
||||
25542.5
|
||||
26294.8
|
||||
26116.1
|
||||
23699.7
|
||||
26835.4
|
||||
26989.9
|
||||
28161.5
|
||||
24813.8
|
||||
25087.9
|
||||
26951.7
|
||||
23788.7
|
||||
26512.6
|
||||
26979.8
|
||||
24345.2
|
||||
28472.6
|
||||
23907.3
|
||||
28056.3
|
||||
25890.8
|
||||
28668.9
|
||||
27075.7
|
||||
26478.2
|
||||
23787.7
|
||||
26501.1
|
||||
minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4
|
||||
26553.9;26553.9;796.113;270;0;150;190;700;270;400;0;50;850;530;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
avgProfit:
|
||||
26799.8
|
||||
27035.6
|
||||
26275.1
|
||||
24812.5
|
||||
26806
|
||||
25731.5
|
||||
25709.3
|
||||
26040
|
||||
26796.5
|
||||
26717.3
|
||||
24063.1
|
||||
27351.1
|
||||
27509.7
|
||||
28774
|
||||
25328.3
|
||||
25695.7
|
||||
27530.2
|
||||
24353
|
||||
26997.2
|
||||
27534.5
|
||||
24894.9
|
||||
28956
|
||||
24512.9
|
||||
28704.1
|
||||
26404.1
|
||||
29275.3
|
||||
27718.4
|
||||
27043.9
|
||||
24179.5
|
||||
27068.2
|
||||
@ -0,0 +1,99 @@
|
||||
minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4
|
||||
10580.7;10580.8;257.666;336;0;149;190;0;1;134;0;50;301;51;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
Risk:
|
||||
184.458
|
||||
195.09
|
||||
207.678
|
||||
278.26
|
||||
220.823
|
||||
181.042
|
||||
195.281
|
||||
199.255
|
||||
182.194
|
||||
181.295
|
||||
285.773
|
||||
185.306
|
||||
272.997
|
||||
288.405
|
||||
223.407
|
||||
213.341
|
||||
208.693
|
||||
354.01
|
||||
217.093
|
||||
301.892
|
||||
200.499
|
||||
637.424
|
||||
408.232
|
||||
308.314
|
||||
181.042
|
||||
470.693
|
||||
212.039
|
||||
184.694
|
||||
345.455
|
||||
205.291
|
||||
minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4
|
||||
20164;20164;574.598;346;0;0;250;289;9;401;0;50;689;583;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
Risk:
|
||||
403.784
|
||||
414.043
|
||||
408.225
|
||||
664.379
|
||||
413.447
|
||||
448.303
|
||||
513.763
|
||||
439.966
|
||||
403.784
|
||||
408.366
|
||||
773.926
|
||||
459.531
|
||||
537.056
|
||||
795.835
|
||||
542.569
|
||||
512.817
|
||||
471.741
|
||||
879.425
|
||||
441.46
|
||||
533.521
|
||||
659.742
|
||||
990.73
|
||||
842.758
|
||||
776.278
|
||||
404.787
|
||||
989.35
|
||||
481.833
|
||||
409.978
|
||||
795.275
|
||||
421.261
|
||||
minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4
|
||||
26553.9;26553.9;796.113;270;0;150;190;700;270;400;0;50;850;530;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
Risk:
|
||||
559.839
|
||||
576.633
|
||||
596.264
|
||||
942.092
|
||||
560.045
|
||||
679.026
|
||||
683.47
|
||||
627.607
|
||||
559.839
|
||||
562.478
|
||||
1245.41
|
||||
638.37
|
||||
675.366
|
||||
1132.8
|
||||
784.609
|
||||
686.634
|
||||
680.842
|
||||
1115.92
|
||||
572.793
|
||||
682.114
|
||||
914.647
|
||||
1211.67
|
||||
1051.95
|
||||
1104.87
|
||||
583.36
|
||||
1360.71
|
||||
743.425
|
||||
577.734
|
||||
1191.09
|
||||
581.791
|
||||
@ -0,0 +1,99 @@
|
||||
minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4
|
||||
25488.3;25488.3;755.133;268;0;110;190;477;270;500;0;50;654;530;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
Risk:
|
||||
530.713
|
||||
540.297
|
||||
540.363
|
||||
880.892
|
||||
533.022
|
||||
618.647
|
||||
672.371
|
||||
587.509
|
||||
530.713
|
||||
534.152
|
||||
1092.72
|
||||
618.438
|
||||
665.276
|
||||
1074.34
|
||||
737.478
|
||||
678.05
|
||||
632.259
|
||||
1123.76
|
||||
560.502
|
||||
655.794
|
||||
900.277
|
||||
1201.02
|
||||
1059.4
|
||||
1034.46
|
||||
540.34
|
||||
1290.13
|
||||
657.239
|
||||
542.315
|
||||
1072.76
|
||||
548.737
|
||||
minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4
|
||||
26020.6;26020.8;774.88;218;0;110;190;700;266;400;0;50;846;530;450;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
Risk:
|
||||
545.117
|
||||
552.781
|
||||
562.228
|
||||
899.047
|
||||
545.117
|
||||
643.324
|
||||
674.499
|
||||
609.029
|
||||
545.491
|
||||
547.446
|
||||
1160.56
|
||||
629.71
|
||||
667.372
|
||||
1097.62
|
||||
769.274
|
||||
696.182
|
||||
656.859
|
||||
1119.07
|
||||
565.151
|
||||
664.345
|
||||
911.073
|
||||
1232.42
|
||||
1071.62
|
||||
1055.54
|
||||
562.586
|
||||
1324.02
|
||||
695.983
|
||||
560.188
|
||||
1119.5
|
||||
563.245
|
||||
minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4
|
||||
26553.9;26553.9;796.113;270;0;150;190;700;270;400;0;50;850;530;370;0;50;0;0;0;0;0;0;50;50;50;50
|
||||
Risk:
|
||||
559.839
|
||||
576.633
|
||||
596.264
|
||||
942.092
|
||||
560.045
|
||||
679.026
|
||||
683.47
|
||||
627.607
|
||||
559.839
|
||||
562.478
|
||||
1245.41
|
||||
638.37
|
||||
675.366
|
||||
1132.8
|
||||
784.609
|
||||
686.634
|
||||
680.842
|
||||
1115.92
|
||||
572.793
|
||||
682.114
|
||||
914.647
|
||||
1211.67
|
||||
1051.95
|
||||
1104.87
|
||||
583.36
|
||||
1360.71
|
||||
743.425
|
||||
577.734
|
||||
1191.09
|
||||
581.791
|
||||
1046
Programming/WDWR/projekt/wdwr/wdwr17421-1.dat
Normal file
1046
Programming/WDWR/projekt/wdwr/wdwr17421-1.dat
Normal file
File diff suppressed because it is too large
Load Diff
117
Programming/WDWR/projekt/wdwr/wdwr17421-1.mod
Normal file
117
Programming/WDWR/projekt/wdwr/wdwr17421-1.mod
Normal file
@ -0,0 +1,117 @@
|
||||
/*********************************************
|
||||
* OPL 12.5.1.0 Model
|
||||
* Author: Mateusz Percinski
|
||||
* Creation Date: May 23, 2017 at 6:34:43 PM
|
||||
*********************************************/
|
||||
|
||||
/***********PARAMETRY************************/
|
||||
//Paramatry zostaladniej opisane w pliku .dat oraz raporcie
|
||||
|
||||
// Parametry liczbowe
|
||||
int nMachType = ...;
|
||||
int nMonth = ...;
|
||||
int nProdType = ...;
|
||||
|
||||
int nHours = ...;
|
||||
int nScenarios = ...;
|
||||
|
||||
// Utworzenie wektorow indeksujacych
|
||||
{int} machines = asSet(1..nMachType);
|
||||
{int} months = asSet(1..nMonth);
|
||||
{int} products = asSet(1..nProdType);
|
||||
{int} scenarios = asSet(1..nScenarios);
|
||||
|
||||
//Parametry produkcji i sprzedazy
|
||||
int machineCount[machines] = ...;
|
||||
float prodTime[machines][products] = ...;
|
||||
int maxInMonth[months][products] = ...;
|
||||
|
||||
//Parametry magazynowania
|
||||
int storageMax[products] = ...;
|
||||
int storageCost = ...;
|
||||
int storageStart[products] = ...;
|
||||
|
||||
//Parametry do generowania scenariusze (nieuzywane w modelu CPLEX)
|
||||
int mu[products] = ...;
|
||||
int sigma[products][products] = ...;
|
||||
|
||||
//Macierz scenariuszy dochodow ze sprzedazy: typ produkty (kolumna) - scenariusz (wiersz)
|
||||
float sellProfit[scenarios][products] = ...;
|
||||
|
||||
/*********** ZMIENNE DECYZYJNE ********************************************/
|
||||
dvar int produce[months][products]; // Liczba wyprodukowanych
|
||||
dvar int sell[months][products]; // Liczba sprzedanych
|
||||
dvar int stock[months][products]; // Liczba w magazynie
|
||||
|
||||
// Czas wykorzystany na danym typie maszyna na dany typ produktu
|
||||
dvar float workTime[months][machines][products];
|
||||
|
||||
// Zmienna binarna - czy sprzedaz danego typu produktu przekaroczyla 80 procent pojemnosci rynku
|
||||
dvar boolean if80prec[months][products];
|
||||
|
||||
// Zmienna - ile pieniedzy nalezy odjac od dochodow z poszczegolnych produkow
|
||||
// w poszczegolnych miesiacach ze wzgledu na przekroczenie 80% pojmnosci rynku
|
||||
dvar float lowerProfit[scenarios][months][products];
|
||||
|
||||
/************ KRYTERIA OCENY *********************************************/
|
||||
// zysk policzony dla poszczegolnych scenariuszy
|
||||
dexpr float profit[i in scenarios] = sum(m in months, p in products)
|
||||
(sell[m][p]*sellProfit[i][p]-lowerProfit[i][m][p]- stock[m][p]*storageCost);
|
||||
// wartosc oczekiwana zysku policzona jako srednia
|
||||
dexpr float avgProfit = sum(i in scenarios)(profit[i])/nScenarios;
|
||||
|
||||
// funkcja celu
|
||||
maximize avgProfit;
|
||||
|
||||
// ************** OGRANICZENIA ******************************************/
|
||||
subject to {
|
||||
// Zmienne decyzyjne nie mniejsze niz zero
|
||||
forall(i in scenarios, m in months, mc in machines, p in products) {
|
||||
workTime[m][mc][p] >= 0;
|
||||
produce[m][p] >= 0;
|
||||
sell[m][p] >= 0;
|
||||
stock[m][p] >= 0;
|
||||
lowerProfit[i][m][p] >= 0;
|
||||
}
|
||||
// Ogranicznie czasu produkcji maszyn w miesiacu
|
||||
forall(m in months, mc in machines) {
|
||||
sum(p in products) (workTime[m][mc][p]) <= (machineCount[mc]*nHours);
|
||||
}
|
||||
// Ograniczenie definiujace wykorzystany czas pracy maszyn
|
||||
forall(m in months, p in products, mc in machines) {
|
||||
workTime[m][mc][p] == produce[m][p]*prodTime[mc][p];
|
||||
}
|
||||
// Ogranicznie maksymalnej pojemnosci rynku
|
||||
forall(m in months, p in products) {
|
||||
sell[m][p] <= maxInMonth[m][p];
|
||||
}
|
||||
// Ogranicznie ustawiajace zmienna binarna po przekroczeniu 80 procent pojemnosci rynku
|
||||
forall(m in months, p in products) {
|
||||
sell[m][p] <= 0.8*maxInMonth[m][p] + 1000000 * if80prec[m][p];
|
||||
sell[m][p] >= 0.8*maxInMonth[m][p] * if80prec[m][p];
|
||||
}
|
||||
// Ograniczenia linearyzujace wplyw zmiennej binarnej na funkcje celu
|
||||
forall (i in scenarios,m in months, p in products) {
|
||||
lowerProfit[i][m][p] <= 1000000 * if80prec[m][p];
|
||||
lowerProfit[i][m][p] <= 0.2 * sell[m][p]*sellProfit[i][p];
|
||||
0.2 * sell[m][p]*sellProfit[i][p] - lowerProfit[i][m][p] + 1000000 * if80prec[m][p] <= 1000000;
|
||||
}
|
||||
// Ograniczenie sprzedazy oraz definicja ilosci towaru pozostajacej w magazynie
|
||||
forall(m in months, p in products) {
|
||||
if(m == 1) { //pierwszy miesiac
|
||||
sell[m][p] <= produce[m][p]+storageStart[p];
|
||||
stock[m][p]==(produce[m][p] + storageStart[p])-sell[m][p];
|
||||
}else { // kolejne miesiace
|
||||
sell[m][p] <= produce[m][p] + stock[m-1][p];
|
||||
stock[m][p]==(produce[m][p] + stock[m-1][p])-sell[m][p];
|
||||
}
|
||||
}
|
||||
// Ogranicznie maksymalnej ilosci magazynowanych produktow oraz
|
||||
// wymaganej ilosc pozostalej w magazynie na na koniec symulacji
|
||||
forall(m in months, p in products) {
|
||||
stock[m][p] <= storageMax[p];
|
||||
if(m == 3) {
|
||||
stock[m][p] >= 50;
|
||||
}
|
||||
}
|
||||
}
|
||||
77
Programming/WDWR/projekt/wdwr/wdwr17421-2.dat
Normal file
77
Programming/WDWR/projekt/wdwr/wdwr17421-2.dat
Normal file
@ -0,0 +1,77 @@
|
||||
/*********************************************
|
||||
* OPL 12.5.1.0 Data
|
||||
* Author: Mateusz Percinski
|
||||
* Creation Date: May 24, 2017 at 10:09:32 PM
|
||||
*********************************************/
|
||||
|
||||
/***************PARAMETRY********************/
|
||||
nMachType = 5; //ilosc typow maszun
|
||||
nMonth = 3; //ilosc symulowanych miesiecy
|
||||
nProdType = 4; //ilosc typow produktow
|
||||
|
||||
nHours = 384; //liczba godzin roboczych w miesiacu 2*8*24=384
|
||||
nScenarios = 30; //ilosc scenariuszy w symulacji
|
||||
|
||||
// Ilosc maszyn poszczegolnych typow
|
||||
machineCount = [4 2 3 1 1];
|
||||
|
||||
// Koszty wykorzystania typow maszyn (wiersze)
|
||||
//w wytwarzaniu poszczegolnych typow produktow (kolumny)
|
||||
prodTime = [[ 0.4 0.6 0 0]
|
||||
[ 0.2 0.1 0 0.6]
|
||||
[ 0.1 0 0.7 0]
|
||||
[ 0.06 0.04 0 0.05]
|
||||
[ 0 0.05 0.02 0]];
|
||||
|
||||
// Pojemnosc rynku - typ produktu(kolumny) w miesiacu(wiersz)
|
||||
maxInMonth = [[ 400 0 200 300]
|
||||
[ 700 400 500 0]
|
||||
[ 0 800 600 400]];
|
||||
|
||||
// Magazyn
|
||||
storageMax = [200 200 200 200]; //maksymalna pojemnosc dla typow produktow
|
||||
storageCost = 1; //koszt magazynowania zl/sztuka/miesiac
|
||||
storageStart = [50 50 50 50]; //poczatkowa ilosc w magazynach
|
||||
|
||||
/************************* SCENARIOS *************************/
|
||||
// expected values (used only in t-student.R script)
|
||||
mu = [9 8 7 6];
|
||||
|
||||
// covariance (used only in t-student.R script)
|
||||
sigma = [[ 16 -2 -1 -3]
|
||||
[ -2 9 -4 -1]
|
||||
[ -1 -4 4 1]
|
||||
[ -3 -1 1 1]];
|
||||
|
||||
// scenarios (generated by t-student.R script)
|
||||
sellProfit = [[10.7349277395552 7.72178696852481 6.91313555665251 5.3580692014906]
|
||||
[11.4029325398093 5.35289018472828 8.44560627918346 5.94437008867837]
|
||||
[5.9511305436333 9.89072813219028 8.15019579921374 5.92283032635847]
|
||||
[6.98653442934503 9.43820696539249 6.08947859890014 6.28684001183198]
|
||||
[8.97632928181137 7.73492022936159 7.69443003815675 6.65911599366542]
|
||||
[6.3718935550723 8.80949463923562 7.79090422832384 6.81174804312265]
|
||||
[8.78246371277526 9.96223625339419 5.07839427301482 6.04541265888823]
|
||||
[8.61200211939238 6.9004774184148 8.38569802461679 6.01047636955988]
|
||||
[8.04259291408092 11.6005401405856 5.51825529023523 5.52418355625024]
|
||||
[10.2568641176451 9.42484863719592 5.61768337124079 5.44832826311492]
|
||||
[6.06345343273768 6.67810135189213 8.25425855603235 7.11046580533962]
|
||||
[8.74262951486885 8.49193741618487 8.26781448661321 5.63718569432722]
|
||||
[8.96019482191789 7.48206038212727 8.64020735666332 6.61806454609454]
|
||||
[10.7060656286305 7.74073441264031 8.70806653294011 5.56273592101066]
|
||||
[9.14849596786536 5.38206186993592 8.48324089408178 6.31836607658665]
|
||||
[10.9884988766065 5.05294389126629 7.81201862380442 5.89377116896792]
|
||||
[9.85025400030063 9.20603928762331 6.76845266520436 5.77678827669309]
|
||||
[9.76699335636152 7.83363407758539 5.01347383618885 5.84594312150737]
|
||||
[8.22098260831502 7.93964476990494 8.36747212617667 6.58666346744554]
|
||||
[9.64940023484707 7.34646951028725 8.15389801271092 6.71485257935324]
|
||||
[9.56697193644308 7.24791547600423 5.82061898605385 6.6329880618574]
|
||||
[7.64858383380397 11.8956850930214 6.57566578417732 7.52978318768004]
|
||||
[10.8667504186116 5.6239431792075 6.49095275149094 5.44747350145729]
|
||||
[11.5459204391574 6.5874000517338 8.91791399632966 5.70569902009294]
|
||||
[9.00453124705504 6.25952067886102 8.93937357845343 6.11437998871486]
|
||||
[10.4316051568988 8.89165309349428 8.00183466339121 6.22792967489147]
|
||||
[11.1403947310683 8.80153331584519 6.36710780356291 5.52424084189948]
|
||||
[9.5907049066395 9.30053224873728 6.5052199777433 5.64308372528693]
|
||||
[6.47305423724122 7.66508680006143 7.42589600717661 6.52108273658984]
|
||||
[9.76726531281191 8.22194904261638 7.1081746124431 6.13833241033153]
|
||||
];
|
||||
128
Programming/WDWR/projekt/wdwr/wdwr17421-2.mod
Normal file
128
Programming/WDWR/projekt/wdwr/wdwr17421-2.mod
Normal file
@ -0,0 +1,128 @@
|
||||
/*********************************************
|
||||
* OPL 12.5.1.0 Model
|
||||
* Author: mateu
|
||||
* Creation Date: May 24, 2017 at 10:08:31 PM
|
||||
*********************************************/
|
||||
|
||||
/***********PARAMETRY************************/
|
||||
//Paramatry zostaladniej opisane w pliku .dat oraz raporcie
|
||||
|
||||
// Parametry liczbowe
|
||||
int nMachType = ...;
|
||||
int nMonth = ...;
|
||||
int nProdType = ...;
|
||||
|
||||
int nHours = ...;
|
||||
int nScenarios = ...;
|
||||
|
||||
// Utworzenie wektorow indeksujacych
|
||||
{int} machines = asSet(1..nMachType);
|
||||
{int} months = asSet(1..nMonth);
|
||||
{int} products = asSet(1..nProdType);
|
||||
{int} scenarios = asSet(1..nScenarios);
|
||||
|
||||
//Parametry produkcji i sprzedazy
|
||||
int machineCount[machines] = ...;
|
||||
float prodTime[machines][products] = ...;
|
||||
int maxInMonth[months][products] = ...;
|
||||
|
||||
//Parametry magazynowania
|
||||
int storageMax[products] = ...;
|
||||
int storageCost = ...;
|
||||
int storageStart[products] = ...;
|
||||
|
||||
//Parametry do generowania scenariusze (nieuzywane w modelu CPLEX)
|
||||
int mu[products] = ...;
|
||||
int sigma[products][products] = ...;
|
||||
|
||||
//Macierz scenariuszy dochodow ze sprzedazy: typ produkty (kolumna) - scenariusz (wiersz)
|
||||
float sellProfit[scenarios][products] = ...;
|
||||
|
||||
/*********** ZMIENNE DECYZYJNE ********************************************/
|
||||
dvar int produce[months][products]; // Liczba wyprodukowanych
|
||||
dvar int sell[months][products]; // Liczba sprzedanych
|
||||
dvar int stock[months][products]; // Liczba w magazynie
|
||||
|
||||
// Czas wykorzystany na danym typie maszyna na dany typ produktu
|
||||
dvar float workTime[months][machines][products];
|
||||
|
||||
// Zmienna binarna - czy sprzedaz danego typu produktu przekaroczyla 80 procent pojemnosci rynku
|
||||
dvar boolean if80prec[months][products];
|
||||
|
||||
// Zmienna - ile pieniedzy nalezy odjac od dochodow z poszczegolnych produkow
|
||||
// w poszczegolnych miesiacach ze wzgledu na przekroczenie 80% pojmnosci rynku
|
||||
dvar float lowerProfit[scenarios][months][products];
|
||||
|
||||
/************ KRYTERIA OCENY *********************************************/
|
||||
// ZYSK policzony dla poszczegolnych scenariuszy
|
||||
dexpr float profit[i in scenarios] = sum(m in months, p in products)
|
||||
(sell[m][p]*sellProfit[i][p]-lowerProfit[i][m][p]- stock[m][p]*storageCost);
|
||||
|
||||
// wartosc oczekiwana zysku policzona jako srednia
|
||||
dexpr float avgProfit = sum(i in scenarios)(profit[i])/nScenarios;
|
||||
|
||||
// RYZYKO zdefiniowane srednia roznica Giniego
|
||||
dexpr float giniRisk = sum (t1 in scenarios, t2 in scenarios ) (
|
||||
0.5 * abs(profit[t1] - profit[t2]) * 1/nScenarios * 1/nScenarios
|
||||
);
|
||||
|
||||
// funkcja celu
|
||||
//minimize giniRisk;
|
||||
maximize avgProfit;
|
||||
|
||||
// ************** OGRANICZENIA ******************************************/
|
||||
subject to {
|
||||
// Zmienne decyzyjne nie mniejsze niz zero
|
||||
forall(i in scenarios, m in months, mc in machines, p in products) {
|
||||
workTime[m][mc][p] >= 0;
|
||||
produce[m][p] >= 0;
|
||||
sell[m][p] >= 0;
|
||||
stock[m][p] >= 0;
|
||||
lowerProfit[i][m][p] >= 0;
|
||||
}
|
||||
// Ogranicznie czasu produkcji maszyn w miesiacu
|
||||
forall(m in months, mc in machines) {
|
||||
sum(p in products) (workTime[m][mc][p]) <= (machineCount[mc]*nHours);
|
||||
}
|
||||
// Ograniczenie definiujace wykorzystany czas pracy maszyn
|
||||
forall(m in months, p in products, mc in machines) {
|
||||
workTime[m][mc][p] == produce[m][p]*prodTime[mc][p];
|
||||
}
|
||||
// Ogranicznie maksymalnej pojemnosci rynku
|
||||
forall(m in months, p in products) {
|
||||
sell[m][p] <= maxInMonth[m][p];
|
||||
}
|
||||
// Ogranicznie ustawiajace zmienna binarna po przekroczeniu 80 procent pojemnosci rynku
|
||||
forall(m in months, p in products) {
|
||||
sell[m][p] <= 0.8*maxInMonth[m][p] + 1000000 * if80prec[m][p];
|
||||
sell[m][p] >= 0.8*maxInMonth[m][p] * if80prec[m][p];
|
||||
}
|
||||
// Ograniczenia linearyzujace wplyw zmiennej binarnej na funkcje celu
|
||||
forall (i in scenarios,m in months, p in products) {
|
||||
lowerProfit[i][m][p] <= 1000000 * if80prec[m][p];
|
||||
lowerProfit[i][m][p] <= 0.2 * sell[m][p]*sellProfit[i][p];
|
||||
0.2 * sell[m][p]*sellProfit[i][p] - lowerProfit[i][m][p] + 1000000 * if80prec[m][p] <= 1000000;
|
||||
}
|
||||
// Ograniczenie sprzedazy oraz definicja ilosci towaru pozostajacej w magazynie
|
||||
forall(m in months, p in products) {
|
||||
if(m == 1) { //pierwszy miesiac
|
||||
sell[m][p] <= produce[m][p]+storageStart[p];
|
||||
stock[m][p]==(produce[m][p] + storageStart[p])-sell[m][p];
|
||||
}else { // kolejne miesiace
|
||||
sell[m][p] <= produce[m][p] + stock[m-1][p];
|
||||
stock[m][p]==(produce[m][p] + stock[m-1][p])-sell[m][p];
|
||||
}
|
||||
}
|
||||
// Ogranicznie maksymalnej ilosci magazynowanych produktow oraz
|
||||
// wymaganej ilosc pozostalej w magazynie na na koniec symulacji
|
||||
forall(m in months, p in products) {
|
||||
stock[m][p] <= storageMax[p];
|
||||
if(m == 3) {
|
||||
stock[m][p] >= 50;
|
||||
}
|
||||
}
|
||||
}
|
||||
execute {
|
||||
cplex.tilim = 600;
|
||||
writeln("avgProfit: ",avgProfit,", giniRisk: ",giniRisk);
|
||||
}
|
||||
80
Programming/WDWR/projekt/wdwr/wdwr17421-3.dat
Normal file
80
Programming/WDWR/projekt/wdwr/wdwr17421-3.dat
Normal file
@ -0,0 +1,80 @@
|
||||
/*********************************************
|
||||
* OPL 12.5.1.0 Data
|
||||
* Author: Mateusz Percinski
|
||||
* Creation Date: May 24, 2017 at 10:09:32 PM
|
||||
*********************************************/
|
||||
|
||||
/***************PARAMETRY********************/
|
||||
nMachType = 5; //ilosc typow maszun
|
||||
nMonth = 3; //ilosc symulowanych miesiecy
|
||||
nProdType = 4; //ilosc typow produktow
|
||||
|
||||
nHours = 384; //liczba godzin roboczych w miesiacu 2*8*24=384
|
||||
nScenarios = 30; //ilosc scenariuszy w symulacji
|
||||
|
||||
// Ilosc maszyn poszczegolnych typow
|
||||
machineCount = [4 2 3 1 1];
|
||||
|
||||
// Koszty wykorzystania typow maszyn (wiersze)
|
||||
//w wytwarzaniu poszczegolnych typow produktow (kolumny)
|
||||
prodTime = [[ 0.4 0.6 0 0]
|
||||
[ 0.2 0.1 0 0.6]
|
||||
[ 0.1 0 0.7 0]
|
||||
[ 0.06 0.04 0 0.05]
|
||||
[ 0 0.05 0.02 0]];
|
||||
|
||||
// Pojemnosc rynku - typ produktu(kolumny) w miesiacu(wiersz)
|
||||
maxInMonth = [[ 400 0 200 300]
|
||||
[ 700 400 500 0]
|
||||
[ 0 800 600 400]];
|
||||
|
||||
// Magazyn
|
||||
storageMax = [200 200 200 200]; //maksymalna pojemnosc dla typow produktow
|
||||
storageCost = 1; //koszt magazynowania zl/sztuka/miesiac
|
||||
storageStart = [50 50 50 50]; //poczatkowa ilosc w magazynach
|
||||
|
||||
/******************* METODA: WYMAGANY POZIOM ZYSKU ***********/
|
||||
minAvgProfit = 20000.0; //wymagany poziom zysku
|
||||
|
||||
/************************* SCENARIOS *************************/
|
||||
// expected values (used only in t-student.R script)
|
||||
mu = [9 8 7 6];
|
||||
|
||||
// covariance (used only in t-student.R script)
|
||||
sigma = [[ 16 -2 -1 -3]
|
||||
[ -2 9 -4 -1]
|
||||
[ -1 -4 4 1]
|
||||
[ -3 -1 1 1]];
|
||||
|
||||
// scenarios (generated by t-student.R script)
|
||||
sellProfit = [[10.7349277395552 7.72178696852481 6.91313555665251 5.3580692014906]
|
||||
[11.4029325398093 5.35289018472828 8.44560627918346 5.94437008867837]
|
||||
[5.9511305436333 9.89072813219028 8.15019579921374 5.92283032635847]
|
||||
[6.98653442934503 9.43820696539249 6.08947859890014 6.28684001183198]
|
||||
[8.97632928181137 7.73492022936159 7.69443003815675 6.65911599366542]
|
||||
[6.3718935550723 8.80949463923562 7.79090422832384 6.81174804312265]
|
||||
[8.78246371277526 9.96223625339419 5.07839427301482 6.04541265888823]
|
||||
[8.61200211939238 6.9004774184148 8.38569802461679 6.01047636955988]
|
||||
[8.04259291408092 11.6005401405856 5.51825529023523 5.52418355625024]
|
||||
[10.2568641176451 9.42484863719592 5.61768337124079 5.44832826311492]
|
||||
[6.06345343273768 6.67810135189213 8.25425855603235 7.11046580533962]
|
||||
[8.74262951486885 8.49193741618487 8.26781448661321 5.63718569432722]
|
||||
[8.96019482191789 7.48206038212727 8.64020735666332 6.61806454609454]
|
||||
[10.7060656286305 7.74073441264031 8.70806653294011 5.56273592101066]
|
||||
[9.14849596786536 5.38206186993592 8.48324089408178 6.31836607658665]
|
||||
[10.9884988766065 5.05294389126629 7.81201862380442 5.89377116896792]
|
||||
[9.85025400030063 9.20603928762331 6.76845266520436 5.77678827669309]
|
||||
[9.76699335636152 7.83363407758539 5.01347383618885 5.84594312150737]
|
||||
[8.22098260831502 7.93964476990494 8.36747212617667 6.58666346744554]
|
||||
[9.64940023484707 7.34646951028725 8.15389801271092 6.71485257935324]
|
||||
[9.56697193644308 7.24791547600423 5.82061898605385 6.6329880618574]
|
||||
[7.64858383380397 11.8956850930214 6.57566578417732 7.52978318768004]
|
||||
[10.8667504186116 5.6239431792075 6.49095275149094 5.44747350145729]
|
||||
[11.5459204391574 6.5874000517338 8.91791399632966 5.70569902009294]
|
||||
[9.00453124705504 6.25952067886102 8.93937357845343 6.11437998871486]
|
||||
[10.4316051568988 8.89165309349428 8.00183466339121 6.22792967489147]
|
||||
[11.1403947310683 8.80153331584519 6.36710780356291 5.52424084189948]
|
||||
[9.5907049066395 9.30053224873728 6.5052199777433 5.64308372528693]
|
||||
[6.47305423724122 7.66508680006143 7.42589600717661 6.52108273658984]
|
||||
[9.76726531281191 8.22194904261638 7.1081746124431 6.13833241033153]
|
||||
];
|
||||
164
Programming/WDWR/projekt/wdwr/wdwr17421-3.mod
Normal file
164
Programming/WDWR/projekt/wdwr/wdwr17421-3.mod
Normal file
@ -0,0 +1,164 @@
|
||||
/*********************************************
|
||||
* OPL 12.5.1.0 Model
|
||||
* Author: mateu
|
||||
* Creation Date: May 24, 2017 at 10:08:31 PM
|
||||
*********************************************/
|
||||
|
||||
/***********PARAMETRY************************/
|
||||
//Paramatry zostaladniej opisane w pliku .dat oraz raporcie
|
||||
|
||||
// Parametry liczbowe
|
||||
int nMachType = ...;
|
||||
int nMonth = ...;
|
||||
int nProdType = ...;
|
||||
|
||||
int nHours = ...;
|
||||
int nScenarios = ...;
|
||||
|
||||
// Utworzenie wektorow indeksujacych
|
||||
{int} machines = asSet(1..nMachType);
|
||||
{int} months = asSet(1..nMonth);
|
||||
{int} products = asSet(1..nProdType);
|
||||
{int} scenarios = asSet(1..nScenarios);
|
||||
|
||||
//Parametry produkcji i sprzedazy
|
||||
int machineCount[machines] = ...;
|
||||
float prodTime[machines][products] = ...;
|
||||
int maxInMonth[months][products] = ...;
|
||||
|
||||
//Parametry magazynowania
|
||||
int storageMax[products] = ...;
|
||||
int storageCost = ...;
|
||||
int storageStart[products] = ...;
|
||||
|
||||
//Parametry do generowania scenariusze (nieuzywane w modelu CPLEX)
|
||||
int mu[products] = ...;
|
||||
int sigma[products][products] = ...;
|
||||
|
||||
//Macierz scenariuszy dochodow ze sprzedazy: typ produkty (kolumna) - scenariusz (wiersz)
|
||||
float sellProfit[scenarios][products] = ...;
|
||||
|
||||
/******************* PARAMTERY METODY: WYMAGANY POZIOM ZYSKU ***********/
|
||||
float minAvgProfit = ...; //wymagany poziom zysku
|
||||
|
||||
/*********** ZMIENNE DECYZYJNE ********************************************/
|
||||
dvar int produce[months][products]; // Liczba wyprodukowanych
|
||||
dvar int sell[months][products]; // Liczba sprzedanych
|
||||
dvar int stock[months][products]; // Liczba w magazynie
|
||||
|
||||
// Czas wykorzystany na danym typie maszyna na dany typ produktu
|
||||
dvar float workTime[months][machines][products];
|
||||
|
||||
// Zmienna binarna - czy sprzedaz danego typu produktu przekaroczyla 80 procent pojemnosci rynku
|
||||
dvar boolean if80prec[months][products];
|
||||
|
||||
// Zmienna - ile pieniedzy nalezy odjac od dochodow z poszczegolnych produkow
|
||||
// w poszczegolnych miesiacach ze wzgledu na przekroczenie 80% pojmnosci rynku
|
||||
dvar float lowerProfit[scenarios][months][products];
|
||||
|
||||
/************ KRYTERIA OCENY *********************************************/
|
||||
// ZYSK policzony dla poszczegolnych scenariuszy
|
||||
dexpr float profit[i in scenarios] = sum(m in months, p in products)
|
||||
(sell[m][p]*sellProfit[i][p]-lowerProfit[i][m][p]- stock[m][p]*storageCost);
|
||||
|
||||
// wartosc oczekiwana zysku policzona jako srednia
|
||||
dexpr float avgProfit = sum(i in scenarios)(profit[i])/nScenarios;
|
||||
|
||||
// RYZYKO zdefiniowane srednia roznica Giniego
|
||||
dexpr float giniRisk = sum (t1 in scenarios, t2 in scenarios ) (
|
||||
0.5 * abs(profit[t1] - profit[t2]) * 1/nScenarios * 1/nScenarios
|
||||
);
|
||||
|
||||
// funkcja celu
|
||||
minimize giniRisk;
|
||||
//maximize avgProfit;
|
||||
|
||||
// ************** OGRANICZENIA ******************************************/
|
||||
subject to {
|
||||
// Zmienne decyzyjne nie mniejsze niz zero
|
||||
forall(i in scenarios, m in months, mc in machines, p in products) {
|
||||
workTime[m][mc][p] >= 0;
|
||||
produce[m][p] >= 0;
|
||||
sell[m][p] >= 0;
|
||||
stock[m][p] >= 0;
|
||||
lowerProfit[i][m][p] >= 0;
|
||||
}
|
||||
// Ogranicznie czasu produkcji maszyn w miesiacu
|
||||
forall(m in months, mc in machines) {
|
||||
sum(p in products) (workTime[m][mc][p]) <= (machineCount[mc]*nHours);
|
||||
}
|
||||
// Ograniczenie definiujace wykorzystany czas pracy maszyn
|
||||
forall(m in months, p in products, mc in machines) {
|
||||
workTime[m][mc][p] == produce[m][p]*prodTime[mc][p];
|
||||
}
|
||||
// Ogranicznie maksymalnej pojemnosci rynku
|
||||
forall(m in months, p in products) {
|
||||
sell[m][p] <= maxInMonth[m][p];
|
||||
}
|
||||
// Ogranicznie ustawiajace zmienna binarna po przekroczeniu 80 procent pojemnosci rynku
|
||||
forall(m in months, p in products) {
|
||||
sell[m][p] <= 0.8*maxInMonth[m][p] + 1000000 * if80prec[m][p];
|
||||
sell[m][p] >= 0.8*maxInMonth[m][p] * if80prec[m][p];
|
||||
}
|
||||
// Ograniczenia linearyzujace wplyw zmiennej binarnej na funkcje celu
|
||||
forall (i in scenarios,m in months, p in products) {
|
||||
lowerProfit[i][m][p] <= 1000000 * if80prec[m][p];
|
||||
lowerProfit[i][m][p] <= 0.2 * sell[m][p]*sellProfit[i][p];
|
||||
0.2 * sell[m][p]*sellProfit[i][p] - lowerProfit[i][m][p] + 1000000 * if80prec[m][p] <= 1000000;
|
||||
}
|
||||
// Ograniczenie sprzedazy oraz definicja ilosci towaru pozostajacej w magazynie
|
||||
forall(m in months, p in products) {
|
||||
if(m == 1) { //pierwszy miesiac
|
||||
sell[m][p] <= produce[m][p]+storageStart[p];
|
||||
stock[m][p]==(produce[m][p] + storageStart[p])-sell[m][p];
|
||||
}else { // kolejne miesiace
|
||||
sell[m][p] <= produce[m][p] + stock[m-1][p];
|
||||
stock[m][p]==(produce[m][p] + stock[m-1][p])-sell[m][p];
|
||||
}
|
||||
}
|
||||
// Ogranicznie maksymalnej ilosci magazynowanych produktow oraz
|
||||
// wymaganej ilosc pozostalej w magazynie na na koniec symulacji
|
||||
forall(m in months, p in products) {
|
||||
stock[m][p] <= storageMax[p];
|
||||
if(m == 3) {
|
||||
stock[m][p] >= 50;
|
||||
}
|
||||
}
|
||||
/******************OGRANICZENIE: WYMAGANY POZIOM ZYSKU ****************************/
|
||||
avgProfit>=minAvgProfit;
|
||||
|
||||
}// Koniec ogranicznen
|
||||
|
||||
main {
|
||||
var file = new IloOplOutputFile("result-minAvgProfit.txt");
|
||||
//var fileAP = new IloOplOutputFile("avgProfitScenario.txt");
|
||||
//var fileMR = new IloOplOutputFile("maxRiskScenario.txt");
|
||||
|
||||
var mod = thisOplModel;
|
||||
var def = mod.modelDefinition;
|
||||
var data = mod.dataElements;
|
||||
var maxAvgProfit = 26553;
|
||||
var i = 1;
|
||||
|
||||
file.writeln("i;minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4");
|
||||
|
||||
data.minAvgProfit = -600.0;
|
||||
|
||||
while (data.minAvgProfit <= maxAvgProfit) {
|
||||
mod = new IloOplModel (def, cplex);
|
||||
mod.addDataSource(data);
|
||||
mod.generate();
|
||||
cplex.tilim = 300;
|
||||
cplex.solve();
|
||||
file.writeln(i,";",data.minAvgProfit,";",mod.avgProfit,";",mod.giniRisk,";",mod.produce[1][1],";",mod.produce[1][2],";",mod.produce[1][3],";",mod.produce[1][4], ";",mod.produce[2][1],";",mod.produce[2][2],";",mod.produce[2][3],";",mod.produce[2][4],";",mod.produce[3][1],";",mod.produce[3][2], ";",mod.produce[3][3],";",mod.produce[3][4],";",mod.stock[1][1],";",mod.stock[1][2],";",mod.stock[1][3],";",mod.stock[1][4], ";",mod.stock[2][1],";",mod.stock[2][2],";",mod.stock[2][3],";",mod.stock[2][4],";",mod.stock[3][1],";",mod.stock[3][2], ";",mod.stock[3][3],";",mod.stock[3][4]);
|
||||
writeln(i," minAvgProfit: ",data.minAvgProfit," avgProfit: ",mod.avgProfit,", giniRisk: ",mod.giniRisk);
|
||||
mod.end();
|
||||
data.minAvgProfit = data.minAvgProfit + 532.41;
|
||||
i = i+1;
|
||||
};
|
||||
|
||||
|
||||
file.close();
|
||||
//fileAP.close();
|
||||
//fileMR.close();
|
||||
}
|
||||
80
Programming/WDWR/projekt/wdwr/wdwr17421-4.dat
Normal file
80
Programming/WDWR/projekt/wdwr/wdwr17421-4.dat
Normal file
@ -0,0 +1,80 @@
|
||||
/*********************************************
|
||||
* OPL 12.5.1.0 Data
|
||||
* Author: Mateusz Percinski
|
||||
* Creation Date: May 24, 2017 at 10:09:32 PM
|
||||
*********************************************/
|
||||
|
||||
/***************PARAMETRY********************/
|
||||
nMachType = 5; //ilosc typow maszun
|
||||
nMonth = 3; //ilosc symulowanych miesiecy
|
||||
nProdType = 4; //ilosc typow produktow
|
||||
|
||||
nHours = 384; //liczba godzin roboczych w miesiacu 2*8*24=384
|
||||
nScenarios = 30; //ilosc scenariuszy w symulacji
|
||||
|
||||
// Ilosc maszyn poszczegolnych typow
|
||||
machineCount = [4 2 3 1 1];
|
||||
|
||||
// Koszty wykorzystania typow maszyn (wiersze)
|
||||
//w wytwarzaniu poszczegolnych typow produktow (kolumny)
|
||||
prodTime = [[ 0.4 0.6 0 0]
|
||||
[ 0.2 0.1 0 0.6]
|
||||
[ 0.1 0 0.7 0]
|
||||
[ 0.06 0.04 0 0.05]
|
||||
[ 0 0.05 0.02 0]];
|
||||
|
||||
// Pojemnosc rynku - typ produktu(kolumny) w miesiacu(wiersz)
|
||||
maxInMonth = [[ 400 0 200 300]
|
||||
[ 700 400 500 0]
|
||||
[ 0 800 600 400]];
|
||||
|
||||
// Magazyn
|
||||
storageMax = [200 200 200 200]; //maksymalna pojemnosc dla typow produktow
|
||||
storageCost = 1; //koszt magazynowania zl/sztuka/miesiac
|
||||
storageStart = [50 50 50 50]; //poczatkowa ilosc w magazynach
|
||||
|
||||
/******************* METODA: WYMAGANY POZIOM ZYSKU ***********/
|
||||
minAvgProfit = 20000.0; //wymagany poziom zysku
|
||||
|
||||
/************************* SCENARIOS *************************/
|
||||
// expected values (used only in t-student.R script)
|
||||
mu = [9 8 7 6];
|
||||
|
||||
// covariance (used only in t-student.R script)
|
||||
sigma = [[ 16 -2 -1 -3]
|
||||
[ -2 9 -4 -1]
|
||||
[ -1 -4 4 1]
|
||||
[ -3 -1 1 1]];
|
||||
|
||||
// scenarios (generated by t-student.R script)
|
||||
sellProfit = [[10.7349277395552 7.72178696852481 6.91313555665251 5.3580692014906]
|
||||
[11.4029325398093 5.35289018472828 8.44560627918346 5.94437008867837]
|
||||
[5.9511305436333 9.89072813219028 8.15019579921374 5.92283032635847]
|
||||
[6.98653442934503 9.43820696539249 6.08947859890014 6.28684001183198]
|
||||
[8.97632928181137 7.73492022936159 7.69443003815675 6.65911599366542]
|
||||
[6.3718935550723 8.80949463923562 7.79090422832384 6.81174804312265]
|
||||
[8.78246371277526 9.96223625339419 5.07839427301482 6.04541265888823]
|
||||
[8.61200211939238 6.9004774184148 8.38569802461679 6.01047636955988]
|
||||
[8.04259291408092 11.6005401405856 5.51825529023523 5.52418355625024]
|
||||
[10.2568641176451 9.42484863719592 5.61768337124079 5.44832826311492]
|
||||
[6.06345343273768 6.67810135189213 8.25425855603235 7.11046580533962]
|
||||
[8.74262951486885 8.49193741618487 8.26781448661321 5.63718569432722]
|
||||
[8.96019482191789 7.48206038212727 8.64020735666332 6.61806454609454]
|
||||
[10.7060656286305 7.74073441264031 8.70806653294011 5.56273592101066]
|
||||
[9.14849596786536 5.38206186993592 8.48324089408178 6.31836607658665]
|
||||
[10.9884988766065 5.05294389126629 7.81201862380442 5.89377116896792]
|
||||
[9.85025400030063 9.20603928762331 6.76845266520436 5.77678827669309]
|
||||
[9.76699335636152 7.83363407758539 5.01347383618885 5.84594312150737]
|
||||
[8.22098260831502 7.93964476990494 8.36747212617667 6.58666346744554]
|
||||
[9.64940023484707 7.34646951028725 8.15389801271092 6.71485257935324]
|
||||
[9.56697193644308 7.24791547600423 5.82061898605385 6.6329880618574]
|
||||
[7.64858383380397 11.8956850930214 6.57566578417732 7.52978318768004]
|
||||
[10.8667504186116 5.6239431792075 6.49095275149094 5.44747350145729]
|
||||
[11.5459204391574 6.5874000517338 8.91791399632966 5.70569902009294]
|
||||
[9.00453124705504 6.25952067886102 8.93937357845343 6.11437998871486]
|
||||
[10.4316051568988 8.89165309349428 8.00183466339121 6.22792967489147]
|
||||
[11.1403947310683 8.80153331584519 6.36710780356291 5.52424084189948]
|
||||
[9.5907049066395 9.30053224873728 6.5052199777433 5.64308372528693]
|
||||
[6.47305423724122 7.66508680006143 7.42589600717661 6.52108273658984]
|
||||
[9.76726531281191 8.22194904261638 7.1081746124431 6.13833241033153]
|
||||
];
|
||||
248
Programming/WDWR/projekt/wdwr/wdwr17421-4.mod
Normal file
248
Programming/WDWR/projekt/wdwr/wdwr17421-4.mod
Normal file
@ -0,0 +1,248 @@
|
||||
/*********************************************
|
||||
* OPL 12.5.1.0 Model
|
||||
* Author: mateu
|
||||
* Creation Date: May 24, 2017 at 10:08:31 PM
|
||||
*********************************************/
|
||||
|
||||
/***********PARAMETRY************************/
|
||||
//Paramatry zostaladniej opisane w pliku .dat oraz raporcie
|
||||
|
||||
// Parametry liczbowe
|
||||
int nMachType = ...;
|
||||
int nMonth = ...;
|
||||
int nProdType = ...;
|
||||
|
||||
int nHours = ...;
|
||||
int nScenarios = ...;
|
||||
|
||||
// Utworzenie wektorow indeksujacych
|
||||
{int} machines = asSet(1..nMachType);
|
||||
{int} months = asSet(1..nMonth);
|
||||
{int} products = asSet(1..nProdType);
|
||||
{int} scenarios = asSet(1..nScenarios);
|
||||
|
||||
//Parametry produkcji i sprzedazy
|
||||
int machineCount[machines] = ...;
|
||||
float prodTime[machines][products] = ...;
|
||||
int maxInMonth[months][products] = ...;
|
||||
|
||||
//Parametry magazynowania
|
||||
int storageMax[products] = ...;
|
||||
int storageCost = ...;
|
||||
int storageStart[products] = ...;
|
||||
|
||||
//Parametry do generowania scenariusze (nieuzywane w modelu CPLEX)
|
||||
int mu[products] = ...;
|
||||
int sigma[products][products] = ...;
|
||||
|
||||
//Macierz scenariuszy dochodow ze sprzedazy: typ produkty (kolumna) - scenariusz (wiersz)
|
||||
float sellProfit[scenarios][products] = ...;
|
||||
|
||||
/******************* PARAMTERY METODY: WYMAGANY POZIOM ZYSKU ***********/
|
||||
float minAvgProfit = ...; //wymagany poziom zysku
|
||||
|
||||
/*********** ZMIENNE DECYZYJNE ********************************************/
|
||||
dvar int produce[months][products]; // Liczba wyprodukowanych
|
||||
dvar int sell[months][products]; // Liczba sprzedanych
|
||||
dvar int stock[months][products]; // Liczba w magazynie
|
||||
|
||||
// Czas wykorzystany na danym typie maszyna na dany typ produktu
|
||||
dvar float workTime[months][machines][products];
|
||||
|
||||
// Zmienna binarna - czy sprzedaz danego typu produktu przekaroczyla 80 procent pojemnosci rynku
|
||||
dvar boolean if80prec[months][products];
|
||||
|
||||
// Zmienna - ile pieniedzy nalezy odjac od dochodow z poszczegolnych produkow
|
||||
// w poszczegolnych miesiacach ze wzgledu na przekroczenie 80% pojmnosci rynku
|
||||
dvar float lowerProfit[scenarios][months][products];
|
||||
|
||||
/************ KRYTERIA OCENY *********************************************/
|
||||
// ZYSK policzony dla poszczegolnych scenariuszy
|
||||
dexpr float profit[i in scenarios] = sum(m in months, p in products)
|
||||
(sell[m][p]*sellProfit[i][p]-lowerProfit[i][m][p]- stock[m][p]*storageCost);
|
||||
|
||||
// wartosc oczekiwana zysku policzona jako srednia
|
||||
dexpr float avgProfit = sum(i in scenarios)(profit[i])/nScenarios;
|
||||
|
||||
// DODATKOWE OBLICZNIE RYZYKA DLA KAZDEGO SCENARIUSZA - POTRZEBNE DO ANALIZY FSD
|
||||
dexpr float risk[i in scenarios] = sum (t in scenarios) (
|
||||
0.5 * abs(profit[i] - profit[t]) * 1/nScenarios
|
||||
);
|
||||
|
||||
// RYZYKO zdefiniowane srednia roznica Giniego
|
||||
dexpr float giniRisk = sum (t1 in scenarios, t2 in scenarios ) (
|
||||
0.5 * abs(profit[t1] - profit[t2]) * 1/nScenarios * 1/nScenarios
|
||||
);
|
||||
|
||||
// funkcja celu
|
||||
minimize giniRisk;
|
||||
//maximize avgProfit;
|
||||
|
||||
// ************** OGRANICZENIA ******************************************/
|
||||
subject to {
|
||||
// Zmienne decyzyjne nie mniejsze niz zero
|
||||
forall(i in scenarios, m in months, mc in machines, p in products) {
|
||||
workTime[m][mc][p] >= 0;
|
||||
produce[m][p] >= 0;
|
||||
sell[m][p] >= 0;
|
||||
stock[m][p] >= 0;
|
||||
lowerProfit[i][m][p] >= 0;
|
||||
}
|
||||
// Ogranicznie czasu produkcji maszyn w miesiacu
|
||||
forall(m in months, mc in machines) {
|
||||
sum(p in products) (workTime[m][mc][p]) <= (machineCount[mc]*nHours);
|
||||
}
|
||||
// Ograniczenie definiujace wykorzystany czas pracy maszyn
|
||||
forall(m in months, p in products, mc in machines) {
|
||||
workTime[m][mc][p] == produce[m][p]*prodTime[mc][p];
|
||||
}
|
||||
// Ogranicznie maksymalnej pojemnosci rynku
|
||||
forall(m in months, p in products) {
|
||||
sell[m][p] <= maxInMonth[m][p];
|
||||
}
|
||||
// Ogranicznie ustawiajace zmienna binarna po przekroczeniu 80 procent pojemnosci rynku
|
||||
forall(m in months, p in products) {
|
||||
sell[m][p] <= 0.8*maxInMonth[m][p] + 1000000 * if80prec[m][p];
|
||||
sell[m][p] >= 0.8*maxInMonth[m][p] * if80prec[m][p];
|
||||
}
|
||||
// Ograniczenia linearyzujace wplyw zmiennej binarnej na funkcje celu
|
||||
forall (i in scenarios,m in months, p in products) {
|
||||
lowerProfit[i][m][p] <= 1000000 * if80prec[m][p];
|
||||
lowerProfit[i][m][p] <= 0.2 * sell[m][p]*sellProfit[i][p];
|
||||
0.2 * sell[m][p]*sellProfit[i][p] - lowerProfit[i][m][p] + 1000000 * if80prec[m][p] <= 1000000;
|
||||
}
|
||||
// Ograniczenie sprzedazy oraz definicja ilosci towaru pozostajacej w magazynie
|
||||
forall(m in months, p in products) {
|
||||
if(m == 1) { //pierwszy miesiac
|
||||
sell[m][p] <= produce[m][p]+storageStart[p];
|
||||
stock[m][p]==(produce[m][p] + storageStart[p])-sell[m][p];
|
||||
}else { // kolejne miesiace
|
||||
sell[m][p] <= produce[m][p] + stock[m-1][p];
|
||||
stock[m][p]==(produce[m][p] + stock[m-1][p])-sell[m][p];
|
||||
}
|
||||
}
|
||||
// Ogranicznie maksymalnej ilosci magazynowanych produktow oraz
|
||||
// wymaganej ilosc pozostalej w magazynie na na koniec symulacji
|
||||
forall(m in months, p in products) {
|
||||
stock[m][p] <= storageMax[p];
|
||||
if(m == 3) {
|
||||
stock[m][p] >= 50;
|
||||
}
|
||||
}
|
||||
/******************OGRANICZENIE: WYMAGANY POZIOM ZYSKU ****************************/
|
||||
avgProfit>=minAvgProfit;
|
||||
|
||||
}// Koniec ogranicznen
|
||||
|
||||
main {
|
||||
//var fileProfit = new IloOplOutputFile("results-minAvgProfit-FSD-profit.txt");
|
||||
//var fileRisk = new IloOplOutputFile("results-minAvgProfit-FSD-risk.txt");
|
||||
var fileProfit = new IloOplOutputFile("results-minAvgProfit-FSD-profit(2).txt");
|
||||
var fileRisk = new IloOplOutputFile("results-minAvgProfit-FSD-risk(2).txt");
|
||||
|
||||
var mod = thisOplModel;
|
||||
var def = mod.modelDefinition;
|
||||
var data = mod.dataElements;
|
||||
var maxAvgProfit = 26553;
|
||||
var i = 1;
|
||||
/**************** Pierwsze wybrane rozwiazanie ************************************/
|
||||
fileProfit.writeln("minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4");
|
||||
fileRisk.writeln("minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4");
|
||||
|
||||
//data.minAvgProfit = 25488.3;
|
||||
data.minAvgProfit = 10580.7;
|
||||
|
||||
mod = new IloOplModel (def, cplex);
|
||||
mod.addDataSource(data);
|
||||
mod.generate();
|
||||
|
||||
writeln("Pierwsze wybrane rozwiazanie: ");
|
||||
cplex.solve();
|
||||
|
||||
writeln(i," minAvgProfit: ",data.minAvgProfit," avgProfit: ",mod.avgProfit,", giniRisk: ",mod.giniRisk);
|
||||
fileProfit.writeln(data.minAvgProfit,";",mod.avgProfit,";",mod.giniRisk,";",mod.produce[1][1],";",mod.produce[1][2],";",mod.produce[1][3],";",mod.produce[1][4], ";",mod.produce[2][1],";",mod.produce[2][2],";",mod.produce[2][3],";",mod.produce[2][4],";",mod.produce[3][1],";",mod.produce[3][2], ";",mod.produce[3][3],";",mod.produce[3][4],";",mod.stock[1][1],";",mod.stock[1][2],";",mod.stock[1][3],";",mod.stock[1][4], ";",mod.stock[2][1],";",mod.stock[2][2],";",mod.stock[2][3],";",mod.stock[2][4],";",mod.stock[3][1],";",mod.stock[3][2], ";",mod.stock[3][3],";",mod.stock[3][4]);
|
||||
fileRisk.writeln(data.minAvgProfit,";",mod.avgProfit,";",mod.giniRisk,";",mod.produce[1][1],";",mod.produce[1][2],";",mod.produce[1][3],";",mod.produce[1][4], ";",mod.produce[2][1],";",mod.produce[2][2],";",mod.produce[2][3],";",mod.produce[2][4],";",mod.produce[3][1],";",mod.produce[3][2], ";",mod.produce[3][3],";",mod.produce[3][4],";",mod.stock[1][1],";",mod.stock[1][2],";",mod.stock[1][3],";",mod.stock[1][4], ";",mod.stock[2][1],";",mod.stock[2][2],";",mod.stock[2][3],";",mod.stock[2][4],";",mod.stock[3][1],";",mod.stock[3][2], ";",mod.stock[3][3],";",mod.stock[3][4]);
|
||||
|
||||
i = 1;
|
||||
fileProfit.writeln("avgProfit: ");
|
||||
while (i<=data.nScenarios) {
|
||||
fileProfit.writeln(mod.profit[i]);
|
||||
i++;
|
||||
};
|
||||
|
||||
i = 1;
|
||||
fileRisk.writeln("Risk: ");
|
||||
while (i<=data.nScenarios) {
|
||||
fileRisk.writeln(mod.risk[i]);
|
||||
i++;
|
||||
};
|
||||
|
||||
/**************** Drugie wybrane rozwiazanie ************************************/
|
||||
fileProfit.writeln("minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4");
|
||||
fileRisk.writeln("minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4");
|
||||
|
||||
//data.minAvgProfit = 26020.6;
|
||||
data.minAvgProfit = 20164;
|
||||
|
||||
|
||||
mod = new IloOplModel (def, cplex);
|
||||
mod.addDataSource(data);
|
||||
mod.generate();
|
||||
|
||||
writeln("Drugie wybrane rozwiazanie: ");
|
||||
cplex.solve();
|
||||
|
||||
writeln(i," minAvgProfit: ",data.minAvgProfit," avgProfit: ",mod.avgProfit,", giniRisk: ",mod.giniRisk);
|
||||
fileProfit.writeln(data.minAvgProfit,";",mod.avgProfit,";",mod.giniRisk,";",mod.produce[1][1],";",mod.produce[1][2],";",mod.produce[1][3],";",mod.produce[1][4], ";",mod.produce[2][1],";",mod.produce[2][2],";",mod.produce[2][3],";",mod.produce[2][4],";",mod.produce[3][1],";",mod.produce[3][2], ";",mod.produce[3][3],";",mod.produce[3][4],";",mod.stock[1][1],";",mod.stock[1][2],";",mod.stock[1][3],";",mod.stock[1][4], ";",mod.stock[2][1],";",mod.stock[2][2],";",mod.stock[2][3],";",mod.stock[2][4],";",mod.stock[3][1],";",mod.stock[3][2], ";",mod.stock[3][3],";",mod.stock[3][4]);
|
||||
fileRisk.writeln(data.minAvgProfit,";",mod.avgProfit,";",mod.giniRisk,";",mod.produce[1][1],";",mod.produce[1][2],";",mod.produce[1][3],";",mod.produce[1][4], ";",mod.produce[2][1],";",mod.produce[2][2],";",mod.produce[2][3],";",mod.produce[2][4],";",mod.produce[3][1],";",mod.produce[3][2], ";",mod.produce[3][3],";",mod.produce[3][4],";",mod.stock[1][1],";",mod.stock[1][2],";",mod.stock[1][3],";",mod.stock[1][4], ";",mod.stock[2][1],";",mod.stock[2][2],";",mod.stock[2][3],";",mod.stock[2][4],";",mod.stock[3][1],";",mod.stock[3][2], ";",mod.stock[3][3],";",mod.stock[3][4]);
|
||||
|
||||
i = 1;
|
||||
fileProfit.writeln("avgProfit: ");
|
||||
while (i<=data.nScenarios) {
|
||||
fileProfit.writeln(mod.profit[i]);
|
||||
i++;
|
||||
};
|
||||
|
||||
i = 1;
|
||||
fileRisk.writeln("Risk: ");
|
||||
while (i<=data.nScenarios) {
|
||||
fileRisk.writeln(mod.risk[i]);
|
||||
i++;
|
||||
};
|
||||
|
||||
/**************** Trzecie wybrane rozwiazanie ************************************/
|
||||
fileProfit.writeln("minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4");
|
||||
fileRisk.writeln("minAvgProfit;avgProfit;giniRisk;m1_prod_P1;m1_prod_P2;m1_prod_P3;m1_prod_P4;m2_prod_P1;m2_prod_P2;m2_prod_P3;m2_prod_P4;m3_prod_P1;m3_prod_P2;m3_prod_P3;m3_prod_P4;m1_stock_P1;m1_stock_P2;m1_stock_P3;m1_stock_P4;m2_stock_P1;m2_stock_P2;m2_stock_P3;m2_stock_P4;m3_stock_P1;m3_stock_P2;m3_stock_P3;m3_stock_P4");
|
||||
|
||||
//data.minAvgProfit = 26553.9;
|
||||
data.minAvgProfit = 26553.9;
|
||||
|
||||
mod = new IloOplModel (def, cplex);
|
||||
mod.addDataSource(data);
|
||||
mod.generate();
|
||||
|
||||
writeln("Trzecie wybrane rozwiazanie: ");
|
||||
cplex.solve();
|
||||
|
||||
writeln(i," minAvgProfit: ",data.minAvgProfit," avgProfit: ",mod.avgProfit,", giniRisk: ",mod.giniRisk);
|
||||
fileProfit.writeln(data.minAvgProfit,";",mod.avgProfit,";",mod.giniRisk,";",mod.produce[1][1],";",mod.produce[1][2],";",mod.produce[1][3],";",mod.produce[1][4], ";",mod.produce[2][1],";",mod.produce[2][2],";",mod.produce[2][3],";",mod.produce[2][4],";",mod.produce[3][1],";",mod.produce[3][2], ";",mod.produce[3][3],";",mod.produce[3][4],";",mod.stock[1][1],";",mod.stock[1][2],";",mod.stock[1][3],";",mod.stock[1][4], ";",mod.stock[2][1],";",mod.stock[2][2],";",mod.stock[2][3],";",mod.stock[2][4],";",mod.stock[3][1],";",mod.stock[3][2], ";",mod.stock[3][3],";",mod.stock[3][4]);
|
||||
fileRisk.writeln(data.minAvgProfit,";",mod.avgProfit,";",mod.giniRisk,";",mod.produce[1][1],";",mod.produce[1][2],";",mod.produce[1][3],";",mod.produce[1][4], ";",mod.produce[2][1],";",mod.produce[2][2],";",mod.produce[2][3],";",mod.produce[2][4],";",mod.produce[3][1],";",mod.produce[3][2], ";",mod.produce[3][3],";",mod.produce[3][4],";",mod.stock[1][1],";",mod.stock[1][2],";",mod.stock[1][3],";",mod.stock[1][4], ";",mod.stock[2][1],";",mod.stock[2][2],";",mod.stock[2][3],";",mod.stock[2][4],";",mod.stock[3][1],";",mod.stock[3][2], ";",mod.stock[3][3],";",mod.stock[3][4]);
|
||||
|
||||
i = 1;
|
||||
fileProfit.writeln("avgProfit: ");
|
||||
while (i<=data.nScenarios) {
|
||||
fileProfit.writeln(mod.profit[i]);
|
||||
i++;
|
||||
};
|
||||
|
||||
i = 1;
|
||||
fileRisk.writeln("Risk: ");
|
||||
while (i<=data.nScenarios) {
|
||||
fileRisk.writeln(mod.risk[i]);
|
||||
i++;
|
||||
};
|
||||
|
||||
|
||||
fileProfit.close();
|
||||
fileRisk.close();
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user