Lineare Optimierung - integer Simplex- Zuschnittproblem

cutting-stock problem - simplex integer programm
An order for [br]30 pieces of length 2, [br]10 pieces of length 3[br]and [br]20 pieces of length 4 [br]is to be cut from stock lengths 5, 6, and 9 with[br]costs respectively of 6, 7, and 10[br][br]D,E,F,G,H,I,J Zuschnitt-Muster, D1:J1 standard längen auf Lager (Cutting pattern, stock length)[br][br]Spreadsheet calculation table [math]\longrightarrow[/math] [url=https://www.geogebra.org/m/BpqJ28eP#material/fP8cnZbb]Simplex linear integer prgramm[/url][math]\nearrow[/math][br][list=1][*]X Input tableau transfer from Spreadsheet[/*][*]make Start Tableau for Dual Simplex program (dual simplex minimize programm)[br]doing simplex steps until cost row (last) all elements positiv[/*][*]make minimum number col x5 integer [br]delete col x5 in input tableau (1.) and subtract units produced x5 in row 1[br]input cost 3x6=18 in Z[br][/*][*]make Dual Simplex Tableau [br]doing simplex steps until cost row (last) all elements positiv[/*][*]Puuh, remaining cutting pattern are integers [/*][*]transfer to spreadsheet row 6[br][/*][/list]Zuschnitt[br][list][*]x1=1 - 5 m Standard - 1 Einheit verwendet [/*][*]x4=11 [/*][*]x5=3 - 6 m Standard - 14 Einheiten[/*][*]x6=9 - 9 m Standard - 9 Einheiten[/*][*]Kosten 173+3 x 7=194 für 24 Einheiten[/*][/list][br][url=http://www.dma.ufv.br/maxima/index.php]wxMaxima[/url][math]\nearrow[/math][br][i]load("simplex");[br]minimize_lp([br]x1*6+x2*6+x3*7+x4*7+x5*7+x6*10+x7*10,[[br]x1+x4+3*x5+x6 >= 30, [br]x1+2*x3+x6 >= 10,[br]x2+x4+x6+2*x7 >= 20,[br]x5=6[br]]), nonegative_lp=true, numer;[/i][br][182.0,[x7=4.0,x2=0,x3=0,x6=10,x5=6,x4=2,x1=0]] Kosten[br][br][i]minimize_lp([br]x1*0+x2+x3*0+x4*0+x5*0+x6*0+x7*1,[[br]x1+x4+3*x5+x6 >= 30, [br]x1+2*x3+x6 >= 10,[br]x2+x4+x6+2*x7 >= 20,[br]x5=3[br]]), nonegative_lp=true, numer;[/i][br][0.0,[x7=0,x2=0,x3=0,x6=9.0,x5=3,x4=11.0,x1=1.0]] Verschnitt[br][i]x1*6+x2*6+x3*7+x4*7+x5*7+x6*9+x7*9, x7=0,x2=0,x3=0,x6=9.0,x5=3,x4=11.0,x1=1.0; [/i][br]185.0 Kosten[br][br][url=https://docs.google.com/spreadsheets/d/1gKxW9G7O550szURpzJLdSpxtVAfJFrt6kjItLmlyh_g/edit?gid=121650166#gid=121650166]goolge spreadsheet: Grundlagen Simplex-Algorithmus macro:simplex.js[math]\nearrow[/math][br][img]data:image/png;base64,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[/img][/url]

Information: Lineare Optimierung - integer Simplex- Zuschnittproblem