// Numbas version: finer_feedback_settings {"name": "Productiefunctie: kostenminimalisatie bij gegeven output", "extensions": ["jsxgraph"], "custom_part_types": [], "resources": [], "navigation": {"allowregen": true, "showfrontpage": false, "preventleave": false, "typeendtoleave": false}, "question_groups": [{"pickingStrategy": "all-ordered", "questions": [{"name": "Productiefunctie: kostenminimalisatie bij gegeven output", "tags": [], "metadata": {"description": "
Minimaliseer de kosten bij een gegeven outputvolume
", "licence": "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International"}, "statement": "Stap 1: Formuleer het minimalisatieprobleem.
\\begin{eqnarray}
\\min_{A,K} C(A,K) &=& w_A \\cdot A + w_K \\cdot K\\\\
&=& \\var{wA} \\cdot A + \\var{wK} \\cdot K\\\\
\\mbox{als} && \\phi(A,K) = q \\\\
\\mbox{d.w.z. als} && \\var{c} \\cdot A^{\\var{alpha}}\\cdot K^{\\var{beta}} = \\var{geg}
\\end{eqnarray}
Stap 2: Bepaal de Lagrangefunctie.
\\begin{eqnarray}
L(A, K, \\lambda) &=& C(A, K)+\\lambda (q - \\phi(A,K)) \\\\
&=& \\var{wA} \\cdot A + \\var{wK} \\cdot K + \\lambda (\\var{geg} - \\var{c} \\cdot A^{\\var{alpha}}\\cdot K^{\\var{beta}})
\\end{eqnarray}
Stap 3: Schrijf de eerste orde voorwaarden neer om de kritische punten van deze Lagrangefunctie te berekenen.
\\begin{eqnarray}
\\mbox{(1) }\\quad \\frac{\\partial {{L}}}{\\partial A} & = & \\var{wA} - \\lambda \\cdot \\var{c} \\cdot \\var{alpha} \\cdot A^{\\var{alpha-1}}\\cdot K^{\\var{beta}} = 0\\\\
\\mbox{(2) }\\quad \\frac{\\partial {{L}}}{\\partial K} & = & \\var{wK} - \\lambda \\cdot \\var{c} \\cdot \\var{beta} \\cdot A^{\\var{alpha}}\\cdot K^{\\var{beta-1}}= 0 \\\\
\\mbox{(3) }\\quad \\frac{\\partial {{L}}}{\\partial \\lambda} & = & \\var{geg} - \\var{c} \\cdot A^{\\var{alpha}}\\cdot K^{\\var{beta}} = 0
\\end{eqnarray}
Stap 4: Los dit stelsel vergelijkingen op naar $A$, $K$, en $\\lambda$.
Het oplossen van $\\lambda$ uit de vergelijkingen (1) en (2) levert na gelijkstelling:
\\begin{eqnarray}
\\frac{\\var{wA}}{\\var{c} \\cdot \\var{alpha} \\cdot A^{\\var{alpha-1}}\\cdot K^{\\var{beta}}} & = & \\frac{\\var{wK}}{\\var{c} \\cdot \\var{beta} \\cdot A^{\\var{alpha}}\\cdot K^{\\var{beta-1}}}\\\\
K & = & \\frac{\\var{wA*beta/gcd(wA*beta,wK*alpha)}}{\\var{wK*alpha/gcd(wA*beta,wK*alpha)}} \\cdot A
\\end{eqnarray}
Substitutie hiervan in (3) geeft de optimale waarde van $A$:
\\[ A^* = \\var{Aopl} ,\\]
waardoor
\\[ K^* = \\var{Kopl} .\\]
De minimale kosten bedragen dan \\[ \\var{wA} \\cdot \\var{Aopl} + \\var{wK} \\cdot \\var{Kopl} = \\var{wA*Aopl+wK*Kopl} \\]
\n{toonfiguur()}
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", "templateType": "anything", "can_override": false}, "wA": {"name": "wA", "group": "Ungrouped variables", "definition": "random(2..10)", "description": "wA
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", "templateType": "anything", "can_override": false}, "geg": {"name": "geg", "group": "Ungrouped variables", "definition": "c*Aopl^(alpha)*Kopl^(beta)\n", "description": "", "templateType": "anything", "can_override": false}, "Kopl": {"name": "Kopl", "group": "Ungrouped variables", "definition": "wA*theta*beta", "description": "", "templateType": "anything", "can_override": false}}, "variablesTest": {"condition": "", "maxRuns": 100}, "ungrouped_variables": ["wA", "wK", "alpha", "beta", "theta", "Aopl", "Kopl", "c", "geg", "kostenopl"], "variable_groups": [], "functions": {"toonfiguur": {"parameters": [], "type": "html", "language": "javascript", "definition": "var wa = Numbas.jme.unwrapValue(scope.variables.wa);\nvar wk = Numbas.jme.unwrapValue(scope.variables.wk);\nvar alpha = Numbas.jme.unwrapValue(scope.variables.alpha);\nvar beta = Numbas.jme.unwrapValue(scope.variables.beta);\nvar c = Numbas.jme.unwrapValue(scope.variables.c);\nvar aopl = Numbas.jme.unwrapValue(scope.variables.aopl);\nvar kopl = Numbas.jme.unwrapValue(scope.variables.kopl);\nvar kostenopl = Numbas.jme.unwrapValue(scope.variables.kostenopl);\nvar geg = Numbas.jme.unwrapValue(scope.variables.geg);\nvar div = Numbas.extensions.jsxgraph.makeBoard('400px','400px',\n {boundingBox: [-10,1.5*kostenopl/wk,1.5*kostenopl/wa,-10],\n axis: false,\n showNavigation: false,\n grid: false\n });\nvar board = div.board;\nxaxis = board.create('axis', [[0, 0], [1,0]], \n\t {name:'A', \n\t\t\twithLabel: true, \n\t\t\tlabel: {position: 'rt', // possible values are 'lft', 'rt', 'top', 'bot'\n\t\t\t\t\t offset: [0, -10] // (in pixels)\n\t\t\t\t\t }\n\t\t\t});\n yaxis = board.create('axis', [[0, 0], [0, 1]], \n\t\t {name:'K', \n\t\t\twithLabel: true, \n\t\t\tlabel: {\n\t\t\t position: 'rt', // possible values are 'lft', 'rt', 'top', 'bot'\n\t\t\t offset: [-20, 0] // (in pixels)\n\t\t\t\t}\n\t\t\t});\nxaxis.removeAllTicks();\t\nyaxis.removeAllTicks();\nvar grafiek = board.create('functiongraph',\n [function(x){return -wa/wk*x+kostenopl/wk},0,kostenopl/wa],\n{strokeColor:\"green\",setLabelText:'niveau',visible: true, strokeWidth: 4, highlightStrokeColor: 'green'} \n ); \nvar grafiek2 = board.create('functiongraph',\n [function(x){ return Math.pow(geg/c,1/beta)*Math.pow(x,-alpha/beta)},0,2.5*aopl],\n{strokeColor:\"red\",setLabelText:'productie',visible: true, strokeWidth: 4, highlightStrokeColor: 'red'} \n ); \nvar grafiek3 = board.create('functiongraph',\n [function(x){ return -wa/wk*x+3/4*kostenopl/wk},0,3/4*kostenopl/wa],\n{strokeColor:\"green\",setLabelText:'niveau',visible: true, dash:2, highlightStrokeColor: 'green'} \n ); \nvar grafiek4 = board.create('functiongraph',\n [function(x){ return -wa/wk*x+5/4*kostenopl/wk},0,5/4*kostenopl/wa],\n{strokeColor:\"black\",setLabelText:'niveau',visible: true, dash:2, highlightStrokeColor: 'black'} \n ); \nvar punt = board.create('point', [aopl, kopl],{name:''});\nreturn div;"}}, "preamble": {"js": "", "css": ""}, "parts": [{"type": "numberentry", "useCustomName": false, "customName": "", "marks": "5", "scripts": {}, "customMarkingAlgorithm": "", "extendBaseMarkingAlgorithm": true, "unitTests": [], "showCorrectAnswer": true, "showFeedbackIcon": true, "variableReplacements": [], "variableReplacementStrategy": "originalfirst", "nextParts": [], "suggestGoingBack": false, "adaptiveMarkingPenalty": 0, "exploreObjective": null, "prompt": "