Uebung1_Aufgabe3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Übung 1, Aufgabe 3 (Erweiterung des Grundmodells)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Einordnung in Übung 1:**\n",
    "\n",
    "- Aufgabe 1: Grundmodell der Produktionsprogrammplanung (PPP)\n",
    "- Aufgabe 2: Grundmodell mit Zielkonflikt\n",
    "- **Aufgabe 3: Erweiterung um mehrere Perioden und Lagerbestände**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Aufgabenstellung (Klausur WS 2010/11)** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Das Produktprogramm eines Unternehmens umfasst die beiden Endprodukte A und B.\n",
    "- In der Fertigung von A und B wird jeweils ein Zwischenprodukt C benötigt, das in Eigenfertigung hergestellt wird.\n",
    "- Die Produktion eines Endproduktes kann inklusive der Fertigung der notwendigen Zwischenprodukte in einer Periode abgeschlossen werden.\n",
    "\n",
    "- Für die kommenden fünf Perioden liegen die folgenden Bedarfe vor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
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OeFf+EfsdXtvtn2j+076e83eVt8vzf4cZOcevGfQUPZ+j/T/ghzL8V+t/0OG0nVNZ1/wz4K8PxazeWcurWUtxfaij7rho48fKrtkhiWHzdRitK3u9S8AeIp9IuNXv9d06TSZ9QgbUXEtxE8RG5S4ALK24Y9MVo/8ACudnhHRNNtNZntNU0NCLPVIIgCpIwcxkkFSOqk9utW/D/gmWxv7jVPE2szeIdVuLc2rXE0CQxpCTkoka8Lnue+KctW7ef62+7QSasr+X/BOA8TaDrk3wivvE154q1G5u7+wSe6sZWU2YR8HbHHjKEA8NnnHPWrfjbxLfTeJovD0S+K1sbbT4p5T4Wtw1w7uTjc5+4oC9upJ9K1r34UX91od1oA8ZXy6E0e20sHtkb7PzkBpAQ8ijsuR2znFbuv8Agq6v7+31Xw9r0+havDbC1a5jgSZJowcgPG3Bxzg54zQ7dNrjT01evf7il8MdY1fULbUbPVrTXI4LSRPsVxrlp5FxLGy8hscMVIPzDkgjNZvifw99u+OHhyf+19Vt99pNL5dvc7EXyih2gY4V8/MO+B0rs/DeiXei2kv9qa1eaze3DB5ri4IVcgYxHGvyxr7D8Sap+IvC11q3iLR9a0zV20y600uj/wCjLMs8TlS6EEjaTtHzDkU/tp/1tYlbM6aikzRmpELRSZozQAtFJmjNAC0UmaM0ALRSZozQAtFJmjNAC0UmaM0ALRSZozQAtFJmjNAC0UmaM0ALRSZozQAtFJmjNAC0UmaM0ALRSZozQAtFJmjNAC0UmaM0ALRSZozQAtFJmjNAC0UmaM0ALRSZozQAtFJmjNAC0UmaM0ALRSZozQAtFJmjNAC0UmaM0ALRSZozQAtFJmjNAC0UmaM0ALRSZozQAtFJmjNAC0UmaM0ALRSZozQAtFJmjNADT/x92v8A12/9lNdKv3RXNH/j7tf+u3/sprpV+6KRtT2MTWv+Py1/3j/6Car1Y1oM17aiNC7bjhQRk/KfWq/lXX/PpJ/30n/xVMzqfEFFHlXX/PpJ/wB9J/8AFUeVdf8APpJ/30n/AMVQQFFHlXX/AD6Sf99J/wDFUeVdf8+kn/fSf/FUAFFHlXX/AD6Sf99J/wDFUeVdf8+kn/fSf/FUAFFHlXX/AD6Sf99J/wDFUeVdf8+kn/fSf/FUAFFHlXX/AD6Sf99J/wDFUeVdf8+kn/fSf/FUAFFHlXX/AD6Sf99J/wDFUeVdf8+kn/fSf/FUAFFHlXX/AD6Sf99J/wDFUeVdf8+kn/fSf/FUAFFHlXX/AD6Sf99J/wDFU0i4Eyx/ZJMspYfMnbHv70AOoo8q6/59JP8AvpP/AIqjyrr/AJ9JP++k/wDiqACijyrr/n0k/wC+k/8AiqPKuv8An0k/76T/AOKoAKKPKuv+fST/AL6T/wCKo8q6/wCfST/vpP8A4qgAoo8q6/59JP8AvpP/AIqjyrr/AJ9JP++k/wDiqACijyrr/n0k/wC+k/8AiqPKuv8An0k/76T/AOKoAKKPKuv+fST/AL6T/wCKo8q6/wCfST/vpP8A4qgAoo8q6/59JP8AvpP/AIqjyrr/AJ9JP++k/wDiqACijyrr/n0k/wC+k/8AiqaBcGZo/skmVUMfmTvn39qAHUUeVdf8+kn/AH0n/wAVR5V1/wA+kn/fSf8AxVABRR5V1/z6Sf8AfSf/ABVHlXX/AD6Sf99J/wDFUAFFHlXX/PpJ/wB9J/8AFUeVdf8APpJ/30n/AMVQAUUeVdf8+kn/AH0n/wAVR5V1/wA+kn/fSf8AxVABRR5V1/z6Sf8AfSf/ABVHlXX/AD6Sf99J/wDFUAFFHlXX/PpJ/wB9J/8AFUeVdf8APpJ/30n/AMVQAUUeVdf8+kn/AH0n/wAVR5V1/wA+kn/fSf8AxVABRR5V1/z6Sf8AfSf/ABVNjFxKpZbSTAZl4ZOxI9fagB1FHlXX/PpJ/wB9J/8AFUeVdf8APpJ/30n/AMVQAUUeVdf8+kn/AH0n/wAVR5V1/wA+kn/fSf8AxVABRR5V1/z6Sf8AfSf/ABVHlXX/AD6Sf99J/wDFUAFFHlXX/PpJ/wB9J/8AFUeVdf8APpJ/30n/AMVQAUUeVdf8+kn/AH0n/wAVR5V1/wA+kn/fSf8AxVABRR5V1/z6Sf8AfSf/ABVHlXX/AD6Sf99J/wDFUAFFHlXX/PpJ/wB9J/8AFUeVdf8APpJ/30n/AMVQAUUeVdf8+kn/AH0n/wAVTYhcSwpItpIQ6hhhk7/jQA6ijyrr/n0k/wC+k/8AiqPKuv8An0k/76T/AOKoAKKPKuv+fST/AL6T/wCKo8q6/wCfST/vpP8A4qgAoo8q6/59JP8AvpP/AIqjyrr/AJ9JP++k/wDiqACijyrr/n0k/wC+k/8AiqPKuv8An0k/76T/AOKoAKKPKuv+fST/AL6T/wCKo8q6/wCfST/vpP8A4qgAoo8q6/59JP8AvpP/AIqjyrr/AJ9JP++k/wDiqACijyrr/n0k/wC+k/8AiqPKuv8An0k/76T/AOKoAKKbILiJQzWkmCyryydyB6+9O8q6/wCfST/vpP8A4qgAoo8q6/59JP8AvpP/AIqjyrr/AJ9JP++k/wDiqACijyrr/n0k/wC+k/8AiqPKuv8An0k/76T/AOKoAKKPKuv+fST/AL6T/wCKo8q6/wCfST/vpP8A4qgAoo8q6/59JP8AvpP/AIqjyrr/AJ9JP++k/wDiqACijyrr/n0k/wC+k/8AiqPKuv8An0k/76T/AOKoAKKPKuv+fST/AL6T/wCKo8q6/wCfST/vpP8A4qgAoo8q6/59JP8AvpP/AIqjyrr/AJ9JP++k/wDiqAGf8vdr/wBdv/ZTXTL90VzLLMl1aGS3dB5wGSynsfQ10y/dFI3pbGVqA/4m1l/vN/6A1WKr6j/yFbL/AHm/9Aap6ZM/iFopKKCBaKSigBaKSigBaKSigBaKSigBaKSigBaKSigBarv/AMhKH/rjJ/NKnqB/+QlD/wBcZP5pQBYrhrT4jJq3xGsdB0i0aTTZoJ3fUJI2VZXj2/LFnG4Ddy2COw6Gu4rhdaAX41+FFUAAabfAAdv9XQviS9fyY/sv+up3dFJRQIWikooAWikooAWikooAWikooAWikooAWq6f8hKb/rjH/N6nqBP+QlN/1xj/AJvQBT8R6pdaNoM95p2mzapdrtSG0hBzI7MFGSAdqjOS3YAmuVsvEvjPRdb0638d2ehC01a6+y20mkzSl4ZCrMokEgwwO0jKnr2rstU1Sy0XS59R1W5S2tLdC8ssh4Uf57d65nTLO+8Wa9aeItYt5LLTrEs+lWMoxKzMpUzyj+E7SQqdgSTzwCO/9f1/Xa4/s/1v/X9bHZUUlFAhaKSigBaKSigBaKSigBaKSigBaKSigBar2X/Hu3/XaX/0NqnqCy/492/67Sf+htQBj+LNV12wgtbXwrpaX2pXshjSW53i2tgFJLysoyB0AA5JPFUfCPiTXLvWr/w94vsrKDV7GGO4M2nO7W88blgCu/5lIKkYP1rc1PV9PtLy00u9vGtrnU98drgMC7BckBsYDY5GTzjjOK4jwlpknhD4mXPh+11K51mC80/7bcXWoMst1DIrhVV5QAWVgxwG6YOKI72G/hv/AFuek0UlFAhaKSigBaKSigBaKSigBaKSigBaKSigBar6f/yDbb/rin8hU9QWH/INtv8Arin8hQBX13WbfQNGn1C6DOIwFjiTl5pCcKijuzEgD61lfD3xJeeLfBVprOpW0VrczySq0MWSqbZGUDJJycKMnufSuT8T+Lrmz+IpGo+EPFOp2GkoDZNpmmGaGSZ1+aUsSASoO0AZwSx9MT/AvWP7S+Hwt/7Pv7T7LczfvLqDYk2+V2+Q5+bGcH0PFEdU2VJWS/roelUUlFBItFJRQAtFJRQAtFJRQAtFJRQAtFJRQBBe/wDHuv8A12i/9DWrFV73/j3X/rtH/wChrU9AHBa5rnjy98aX2keBovD32XT7eF7iXVxPuMkm47V8s9lAPI711+h/2x/Ytv8A8JL9h/tPB8/+z9/k5ycbd/zdMde9cRN4A8D+LvEeu3OoT32p3cd4hureW8mhS0lWNQpVEKfw4IY7u+DWv8Mr26vPB3+lTy3MdveXNtbXEzbmmgSVljYt/FwMZ74zRH4fxKl3+X9fcdfRSUUEi0UlFAC0UlFAC0UlFAC0UlFAC0UlFAFW+/1ln/18D/0Fq2F+6Kx73/W2f/Xcf+gtWwv3RSNqexlaj/yFLP8A3m/9Aaps1BqX/IUs/wDeb/0BqmoM5/ELmjNJRTIFzRmkooAXNGaSigBc0ZpKKAFzRmkooAXNGaSigBc0ZpKKAFzUDn/iZQ/9cpP5pU1V3/5CMP8A1yk/mlAizmqM+jWFzrdrq80G6+s43igl3sNivjcMZwc7R1FXaKBi5ozSUUALmjNJRQAuaM0lFAC5ozSUUALmjNJRQAuaM0lFAC5qBD/xMpv+uUf83qaq6f8AIRm/65R/zegRU8ReHNK8WaM+la/bG6spGVniErx7ipyOUIPX3rmNM+C3gDRtVttS03QfJu7WQSwyfbJ22sDkHBcg/iK7qihaO6HdtWFzRmkooAXNGaSigBc0ZpKKAFzRmkooAXNGaSigBc0ZpKKAFzUFkf3Df9dZP/Q2qaq9n/qG/wCusn/obUCK2v8Ah7SfFGlPpuv2MV9aOcmOTPB9QRgqfcEGq3hnwf4f8HWb2vhrTIbCOQ5kKEsz+m52JY4yep4raoo2HdsXNGaSigBc0ZpKKAFzRmkooAXNGaSigBc0ZpKKAFzRmkooAXNQWB/4ltt/1yT+Qqaq9h/yDrb/AK5L/IUCLPWqOj6NYaBpiafpEH2e1jZmWPezYLMWbliTySTV2igYuaM0lFAC5ozSUUALmjNJRQAuaM0lFAC5ozSUUALmjNJRQBDen9wv/XWP/wBDWp81WvP9Qv8A11j/APQ1qxQI5nxF8N/CPizU4tQ8QaHb3d3FjE25kLAdA20jePZs10VrbQWNpFa2cMcFvCoSOKNQqoo6AAdBUlFGysNtvcXNGaSigBc0ZpKKAFzRmkooAXNGaSigBc0ZpKKAFzRmkooArXv+ts/+u4/9BatlfuisW8/11n/13H/oLVtL90UjansYmtO8d7atGVDBjgsMj7pqv9su/wC/B/36P/xVT65/x9Wv+8f/AEE1TpmVT4iX7Zd/34P+/R/+Ko+2Xf8Afg/79H/4qvJ9AtfH3iq0utStvH/9nQi+uII7b+xoJdipIyj5jgngelb2geM3tLDU4fGd3bpcaVqK2D3kMTLHMXClG287Sd4B7D1xQtf6/ruS1Y7r7Zd/34P+/R/+Ko+2Xf8Afg/79H/4quYt/Hvhe68P3GtxaxB/ZltKYZLlwyLvH8IyBu6jGM57VP4b8ZeH/F0Ekvh3VIr0Rf6xQGR09yrAMB74oFrudB9su/78H/fo/wDxVH2y7/vwf9+j/wDFV5v4v+KOiQ6Frlp4b1+3OuWNuzoqruwwIztLDYxHOQM12EGu2J1S20iW4/4mU1p9qEWxvmjBALZxt6npnNC1V0Nprf8Arb/M2Ptl3/fg/wC/R/8AiqPtl3/fg/79H/4quN8Y+Kks/C3ib+xLzZquj2vmP+6z5LMu5D8w2tx9fetKDxJY29vpVvqN3/p99bCVY0jZ2YBAWchQdq+5wO1ArP8Ar5f5nQfbLv8Avwf9+j/8VR9su/78H/fo/wDxVcJ8P/iLY+OLrV4ba4jeS0um8lEidc2/ARyWHUnPHX2rS8catf6dokVtocixatqVwlnZuyhhG7clyCCCFUMfwoeiXn+o7atdjqftl3/fg/79H/4qj7Zd/wB+D/v0f/iq5/whrT6/4XtL24AW7AMN0g/gmQlZB/30D+FbdNqzsSS/bLv+/B/36P8A8VR9su/78H/fo/8AxVRUUgJftl3/AH4P+/R/+KqJru6+2Rtuh3CNgP3Zx1X/AGqKib/j6j/3G/mtAFr7Zd/34P8Av0f/AIqj7Zd/34P+/R/+KqKuEt/GGr3/AMStN01bCWw0e4trh0NygEtyybfm29UUbuM4J5OMYoWrsPpc9A+2Xf8Afg/79H/4qj7Zd/34P+/R/wDiq5LxJbeLtT1OOx8OahDodksPmS6k9uly7yZwI1jZuBgZJI7jHesey8bapa+Add1DUhbXl/pFzJaRXEC7YbxwVCMBnj5mAIB6g4o3X9eg+XY9F+2Xf9+D/v0f/iqPtl3/AH4P+/R/+KrzZLnxn4T1bSrjxJ4gtdY0/VLxLSaBbFYDaO4OzYwOWG7AO7nFeiU7aXES/bLv+/B/36P/AMVR9su/78H/AH6P/wAVUVFIRL9su/78H/fo/wDxVH2y7/vwf9+j/wDFVFRQBL9su/78H/fo/wDxVH2y7/vwf9+j/wDFVFRQBL9su/78H/fo/wDxVH2y7/vwf9+j/wDFVFRQBL9su/78H/fo/wDxVRLd3X2yRt0O4xqD+7OOrf7VFRL/AMfUn+4v82oAtfbLv+/B/wB+j/8AFUfbLv8Avwf9+j/8VVS8uksbGa6kSR0hQuyxIXdgBnAUck+wrkPCfibWta8b63Z6tZnT7a2treS2s32mRA5fLOR/EcD5ckDjvmhauw7aXO6+2Xf9+D/v0f8A4qj7Zd/34P8Av0f/AIquA12PxzPeahqNnrdl4f06wY/Z7ee1ScXiqoJeR92YwTkADBA60l94s1nUvCXhr+x1i07V/ERVVeaPzFth5Zd3C98BeAfUZoWqv6fj/X+Y7a7/ANI9A+2Xf9+D/v0f/iqPtl3/AH4P+/R/+KrhPDt94i0fxh/wjfinVoNZFxZtd2l8lqtu5KuFdGRSR/EpBHvXbU+lxPR2Jftl3/fg/wC/R/8AiqPtl3/fg/79H/4qoqKQiX7Zd/34P+/R/wDiqPtl3/fg/wC/R/8AiqiooAl+2Xf9+D/v0f8A4qj7Zd/34P8Av0f/AIqoqKAJftl3/fg/79H/AOKo+2Xf9+D/AL9H/wCKqKigCX7Zd/34P+/R/wDiqit7u6WIhWhx5jnmM/3j/tUVFb/6s/77/wDoRoAtfbLv+/B/36P/AMVR9su/78H/AH6P/wAVXN+KF8SXC2tl4Wkhs2uGb7RqUyLKLVAONsZI3MTjHYYOazfDd9rum+KpPDXiPVY9bk+xC9jvksxbsB5hUo6qSvpgjHQ0LV2G1pc7b7Zd/wB+D/v0f/iqPtl3/fg/79H/AOKrz6Xxhq9z8R9E0yCwlsdHuWuVaW5QLJdNGmcqp5VAcYPBb6dbfii+1/UPE1r4a8LanDpErWjXl1fPbCdo0DhVVUY4yTu69hR28x211O2+2Xf9+D/v0f8A4qj7Zd/34P8Av0f/AIquP8Gatq8t9q+g+JLmG81DSZIwLuGIRi4ikTcrlASFbIYEDiurpkkv2y7/AL8H/fo//FUfbLv+/B/36P8A8VUVFICX7Zd/34P+/R/+Ko+2Xf8Afg/79H/4qoqKAJftl3/fg/79H/4qj7Zd/wB+D/v0f/iqiooAl+2Xf9+D/v0f/iqPtl3/AH4P+/R/+KqKigCX7Zd/34P+/R/+KqK1u7pbOFVaHaI1AzGSen+9RUVt/wAesX+4P5UAWvtl3/fg/wC/R/8AiqPtl3/fg/79H/4quR8R23i/VNVWy8O6jDoVikIkfUXtkuZJJCf9WsbNgAAZJI7jHSs+x8UalY+H/FMes3EV3e+HVYfbYoPKWf8AciRSUyQG5wQDjp60ujZai20kd99su/78H/fo/wDxVH2y7/vwf9+j/wDFV5p4Th8T3Oq2x1D4mWepyQKsl9pMOnW4dcrnYzKdy8nrgdKsXc/i7xR4k1WLwzr9voljpE62wD2K3DXUuxXbcWPyqNwHy89apqxKV+p6H9su/wC/B/36P/xVH2y7/vwf9+j/APFVzngrXbjxF4Vt76+jjjvFaSC5SL7oljco2PbK5H1reoasxEv2y7/vwf8Afo//ABVH2y7/AL8H/fo//FVFRSAl+2Xf9+D/AL9H/wCKo+2Xf9+D/v0f/iqiooAl+2Xf9+D/AL9H/wCKo+2Xf9+D/v0f/iqiooAl+2Xf9+D/AL9H/wCKo+2Xf9+D/v0f/iqiooALi7umiAZoceYh4jP94f7VS/bLv+/B/wB+j/8AFVVuP9WP99P/AEIVLQBL9su/78H/AH6P/wAVR9su/wC/B/36P/xVef8AxH8YavoGk3CeH7CXzYRG09/KgEMCs4UBc/fc56AYHU9gdrxbrd5pWnWcGleR/aOpXK2tu1xny4yQWZ2A6hVVjjvwKOlyrM6b7Zd/34P+/R/+Ko+2Xf8Afg/79H/4quC8H3viH+2JILzxFp/izSmRi2oWiQxNaTAj90yIxyCM89RjmsZtT8dapol34x0nXLODT4fOkttGexVlnijZhlpSdwZgueOOlD01Y1Ft2PVvtl3/AH4P+/R/+Ko+2Xf9+D/v0f8A4qs/StQj1bR7PUIOI7qBJlHoGUH+tW6bTTsyE7q5L9su/wC/B/36P/xVH2y7/vwf9+j/APFVFRSAl+2Xf9+D/v0f/iqPtl3/AH4P+/R/+KqKigCX7Zd/34P+/R/+Ko+2Xf8Afg/79H/4qoqKAJftl3/fg/79H/4qj7Zd/wB+D/v0f/iqiooAVp7iW6tVkaLHnA/KhHY+9dOv3RXLL/x+Wv8A11/9lNdSv3RSOmlsYeu/8fVr/vH/ANBNU6ua7/x9Wv8AvH/0E1SpmVT4jyDwX4C/t7Sb2/8A+Er8T6bv1O7H2bTtR8mFcTMMhdpwT3rR8f8AhnS/D/wzi0zT4G+zyarbPMZXMjzM0y7mdjyxNej2tna2MJisraG2jLs5SGMICzHJOB3J5Jou7G01CEQ39rDcxBg4SaMOoYHIOD3B5BprS3lb8LEOTbfz/E4rx/La6f4j8JahrOxNEtbuQTtIuY4ZTGRE7dgAcjJ4BIrJ1i/tPEvxEjl8E3dve3MOi3cd5dWbh0+cAQozrwTuBIGcivTp4IbqB4LmJJopBteORQysPQg9ag07StP0i2+z6TYW1jBnPlW0Kxrn1woAqbXVn5/irDUrWa/rW545rmv+Epv2fxpENxaG+hs44xp4x9ohnQjcWT7y4YEljx710+p6jYaF8WdB1DWb2CxtZ9ClgSa5kEabw6NjceM4NdtL4e0Wee5nn0iwklu02XEj2yFpl9HJHzDjoamvtK07VLVbbU7C1vIFIIiuIVkUEdDggir5m3zef5poLpLl6f8ADf5HkOp6nbavY/FW8sXEts9pbiKUfdkAhI3A9wex6GtvwJHP4Z8TrZ+IZ0vLjXrSKex1IptZtiDdbYycBR8ygdQSetegto2lulyj6baMt2oS5UwKRMoGAH4+YAcAGpJdOsp1txPZ28gtXD24eJT5LAYBXI+UgcZFJafghSldW/rp/kcd8Nru2N54qsvtEX2tNduZHg3jzFQkYYr1APrWd4iPijX/AIlK/g4aUyeHYdkh1Uy+UZ5lydoj6sqY69N59a9Bi0vT4NRmv4LG2ivJ1Cy3KQqJJAOgZgMkfWpILS2tWma2t4oWnfzJTGgUyPgDc2OpwByfSkunkv0sPm1k+/66nn3gdtd8PeONS0jxYNNSXWgdStv7NL+T5i4WVRvGdx+ViOe5r0iq8tla3FzBcT20Ms9uSYZXjBaIkYO0nkZHHFT0+i/r+tCW7u4tFJRSELUTf8fUf+4381qSom/4+k/3G/mtAE1cXrB/4vH4Z/7B97/OOuzqB7O1kvIruS2he5hVkjmaMF0VuoDdQDgZ+lC0af8AWzQ+jOc8Va34Xlnm8MeMHW1tby2EvmXkwggnXdyiybgdwIBI44I61wclstx4B8W2fh+eS98OabcQS6YwkMoxHsklRHOSygjA5PcZr1vUdK0/V7b7Pq1ha30Oc+VcwrIufXDAip4beG2t1gt4Y4oUG1Y41Cqo9ABxQtP68yuZKx594l8T6J4sk8OaZ4e1W11C5n1S3uWjtpQ7RRRnezMAcrjGOcda9GrPsdB0jS7mW40zSrKznnOZZbe3SNpD/tEAE9T1q/T6W+f5f5EsWikopCFopKKAFopKKAFopKKAFqJf+PqT/cX+bVJUS/8AH0/+4v8ANqAJq4vRP+SxeKP+vCy/9qV2dQJZWsV5Ldx20KXMyqssyxgO4XoC3UgZOM9M01uO+jX9bnEeJtU8BeJmuNM8ZXMdhPpNySkV7efZZAwXiWPa4LKQeDz9M1hQ6hcWujeBfEniW5YW9rezxPdz8YgkR1hkkOOMgJknH3ua9MvdD0nUrqG51HS7K7uIDmKWe3R3j/3SRkfhVq4toLu2e3uoY54JF2vFIgZWHoQeCKS0RXMtv61OIttW0/xN8W7KfQr2C/ttL0yYXE9tIJI1eV02ruBwThGOPau9qnp2lafpFv8AZ9JsLWxhzny7aFY1z64UAVbp9Ev63uS9XcWikopCFopKKAFopKKAFopKKAFqKD/Vn/ff/wBCNSVFB/qz/vv/AOhGgDB8ZeNLDwfZ25umia7vZPKtYpZliRm7l3bhEHc/kCeKp+DLnRZr25uE8R6ZrWvXih7p7S5R9iL0RFBJEa5/EnJ5Nb2p+H9G1to21nSbHUDECIzd2yS7M9cbgcdBTNO8MaBo9ybnSND02wnKlDLa2kcTFT2yoBxwKF5lNq1kc/4m/wCSqeC/929/9FCodV1DT/Dfxbh1HWryGytNR0g28dxcOEjEkcu7aWPAJD559K7OWytZrqG6mtoZLi33eTK8YLx7hhtp6jI6460zUNMsNWtTbarZW97bkgmK5iWRCR3wwIo2t5X/ABv/AJhdPR/1rc5PwdeW+t+OPFGt6bMtxp8n2a0huIzlJWjVi5U9CAXAyK7eoLW0trC1S2sbeK2gjGEihQIqj0AHAqamJ6u4tFJRSELRSUUALRSUUALRSUUALUVt/wAesX+4P5VJUVt/x6xf7g/lQByfjHx5aaFqMOhW17Y2uq3UfmCe/nWKC1jzjzGLEbj1wg5OOw5qmD4Ph+HmrQi8XxLZAedq0ljcrLNMzEFpHMbDHTOARhVwOmK6fUPCvh7Vrs3Wq6Fpl9cEBTNc2ccjkDoMsCan0zQtI0VZF0fSrLT1lx5gtbdIg+OmdoGetHRl3StY851EeCjd6Avw8GiHWP7RgKf2V5fmiEH975mznbsznd/OtLQfEGi+E/EfizT9f1S106WTUTfRC6mEfmxyRocrn73IYYGTxXa2Gh6Tpc80+maXZ2cs5zLJb26RtIevzEAZ/Gk1DQ9J1aWKXVdLsr2SE5ie5t0kMZ/2SwOPwp3tt/V7f5BdPR/1v/mYHwxR/wDhCxdurKt/eXN5GGBB8uSVmQ4PTKkH8a6+mqoVQqgAAYAA6UtD8id3di0UlFIQtFJRQAtFJRQAtFJRQBHP/qx/vp/6EKlqGf8A1Y/30/8AQhUtAHGfFr/kmepf78H/AKOSqPxUsLW5tfDt5qwJ0mz1JDf9cLEyMpLY/hyQD7Gu6u7O11C1a2v7aG5gfG6KaMOrYORkHjqAaldEkjKSKrIwwVYZBHpij/O/5FqX6/iebWEfhyT4laWvw/8A7LEH2K4GqNpWzyth2iMP5fy7t2cZ5xmqWieLdD8P/CW50S+1O2h1XT4bizaxeQec8gZ1UCPqd2QRj1r0zTtI03R4Wh0jT7WxiZtzJawLEpPqQoHNRy6Do8+qJqU+lWMl+gwl09shlX6ORkdfWhpNcvT+v8xqSTuQ+E7GXS/Buj2NwCJreyijcHswQAj8616SiqlLmbZklZWFopKKkYtFJRQAtFJRQAtFJRQAL/x+Wv8A11/9lNdSv3RXLJ/x+2v/AF1/9lNdSv3RSOmj8Jh67/x9W3+8f/QTVHNaGsRrLfWqPnaWOcEj+E9xUP8AZ9v6Sf8Af1/8aZlV+Iq5ozVr+z7f0k/7+v8A40f2fb+kn/f1/wDGgyKuaM1a/s+39JP+/r/40f2fb+kn/f1/8aAKuaM1a/s+39JP+/r/AONH9n2/pJ/39f8AxoAq5ozVr+z7f0k/7+v/AI0f2fb+kn/f1/8AGgCrmjNWv7Pt/ST/AL+v/jR/Z9v6Sf8Af1/8aAKuaM1a/s+39JP+/r/40f2fb+kn/f1/8aAKuaM1a/s+39JP+/r/AONH9n2/pJ/39f8AxoAq5qNj/pSf7jfzWr39n2/pJ/39f/GomsYBexrh8GNyf3req+/vQBFmjNWv7Pt/ST/v6/8AjR/Z9v6Sf9/X/wAaAKuaM1a/s+39JP8Av6/+NH9n2/pJ/wB/X/xoAq5ozVr+z7f0k/7+v/jR/Z9v6Sf9/X/xoAq5ozVr+z7f0k/7+v8A40f2fb+kn/f1/wDGgCrmjNWv7Pt/ST/v6/8AjR/Z9v6Sf9/X/wAaAKuaM1a/s+39JP8Av6/+NH9n2/pJ/wB/X/xoAq5ozVr+z7f0k/7+v/jR/Z9v6Sf9/X/xoAq5qNT/AKU/+4v82q9/Z9v6Sf8Af1/8aiWxgN7IuHwI0I/et6t7+1AEWaM1a/s+39JP+/r/AONH9n2/pJ/39f8AxoAq5ozVr+z7f0k/7+v/AI0f2fb+kn/f1/8AGgCrmjNWv7Pt/ST/AL+v/jR/Z9v6Sf8Af1/8aAKuaM1a/s+39JP+/r/40f2fb+kn/f1/8aAKuaM1a/s+39JP+/r/AONH9n2/pJ/39f8AxoAq5ozVr+z7f0k/7+v/AI0f2fb+kn/f1/8AGgCrmjNWv7Pt/ST/AL+v/jR/Z9v6Sf8Af1/8aAKuajgP7s/77f8AoRq9/Z9v6Sf9/X/xqK2sYGiYkP8A6xx/rWHRiPWgCLNGatf2fb+kn/f1/wDGj+z7f0k/7+v/AI0AVc0Zq1/Z9v6Sf9/X/wAaP7Pt/ST/AL+v/jQBVzRmrX9n2/pJ/wB/X/xo/s+39JP+/r/40AVc0Zq1/Z9v6Sf9/X/xo/s+39JP+/r/AONAFXNGatf2fb+kn/f1/wDGj+z7f0k/7+v/AI0AVc0Zq1/Z9v6Sf9/X/wAaP7Pt/ST/AL+v/jQBVzRmrX9n2/pJ/wB/X/xo/s+39JP+/r/40AVc1Hbn/RYv9wfyq9/Z9v6Sf9/X/wAaitLGB7KBmD5Mak4lYdvrQBFmjNWv7Pt/ST/v6/8AjR/Z9v6Sf9/X/wAaAKuaM1a/s+39JP8Av6/+NH9n2/pJ/wB/X/xoAq5ozVr+z7f0k/7+v/jR/Z9v6Sf9/X/xoAq5ozVr+z7f0k/7+v8A40f2fb+kn/f1/wDGgCrmjNWv7Pt/ST/v6/8AjR/Z9v6Sf9/X/wAaAKuaM1a/s+39JP8Av6/+NH9n2/pJ/wB/X/xoAq5ozVr+z7f0k/7+v/jR/Z9v6Sf9/X/xoAozn92P99f/AEIVJmpbmxgWJSA/+sQf61j1YD1qX+z7f0k/7+v/AI0AVc0Zq1/Z9v6Sf9/X/wAaP7Pt/ST/AL+v/jQBVzRmrX9n2/pJ/wB/X/xo/s+39JP+/r/40AVc0Zq1/Z9v6Sf9/X/xo/s+39JP+/r/AONAFXNGatf2fb+kn/f1/wDGj+z7f0k/7+v/AI0AVc0Zq1/Z9v6Sf9/X/wAaP7Pt/ST/AL+v/jQBVzRmrX9n2/pJ/wB/X/xo/s+39JP+/r/40AVc0Zq1/Z9v6Sf9/X/xo/s+39JP+/r/AONAFVP+P21/66/+ymuqX7orm5LSKK5tGjDg+cOsjH+E+prpF+6KR1UfhMjVf+QjZ/7zf+gtRSar/wAhC0/3m/8AQTSZoMavxHgXhfwx8J9Ts9QuvGdxpSas2p3YkFzq7QPtEzbcoJFxx7V0Xh7xnH4S8GavqNt9q1jw/b60lppkjzMzNA5RW2MQS4VywHrjGe9angLwJos+g3U3iPwrYSX0mpXbF7/TkMrIZmKnLrkjGMe1aHxP0ye68FW1ppNjJMY9RtGENrCW2osyknao4AHPtVR0su9l+KCck2/V/qSXHjzUtN0SGfWPCt1barfXZttO0mK6jmkuPl3Biw+VBjJOSduO9SaD48nutUk0nxZoU3hvU0tmukiluEniliU/MUlTgkcZGMjNR+OrLUoNX0HxNpGnyao+jyyieyhYCSSKVNrFM9WBAOO/NYf2fU/iT4lj1GXRNS0DS7PTbq1jbU4xDPNLOoU4jBJVVAzk9c1N3Z28/wAtPvf+Qko2V/61/wAv8zK8d+PtQ1z4aa1MfCl9a6BeWu2z1V5o283LDaWhHzIrdic9RnFeg2/ifyvGWn+GPsmfO0o3v2nzfu7WC7duPfOc/hXnWt6p4mufhXeeDP8AhC9W/tO0sVtpblYg1rIiYG6JwSXYgAhQMjPPSui8SHUPDnjfRPFEOg6lq9omlvYXEemxCSaJiyMp2EjI4I9u9Xon5X/R2/Gw3rGz31/9t/QTx94k/tPwn8QNF+y+V/ZNig87zN3m+ZHu+7j5cdOpzWxovipriWw0XQ7D+0WtbOM6hced5cdqfLBWPODukPHy8YHJI4zxN7p+v6npvxNuLrQ723l1O0g+x2/lF3cCEjaCowzDuFJweM1ueGNBuPAOt6amlWN2+i63Ai3kKq8hsrsJ/rWzkhX6MT0IFKK6Psvvs/69bCla2nS//to/4Ua/4j1q58RjxBpUltHHqcu2WS+Wby34BtwB2QAfMODnisf4yf8ACKf8Jp4U/wCE9/5A3k3fmf6372I9v+q+brXR/D/7ZpuveKNJv9Kv7cvqk19FdvBi3mjkI2hJO7ccjtVbx9fXeifEDwvrcWhavq9tawXccy6VZmd0LhAuRwB0PU9qWjUPRfkUnapK3mc/8P5fBx+IFpF8JricactvK2rQtLOITkARkLMcl9w6gYxnNezV5lFqOoeO/GmgX1n4X1nQ7fSJ5Jri91a3Fu7qYyvlIuSWDFgT24r0vNU9kZy+IdRTc0ZqSR1QP/x/xf8AXJ/5rUuahc/6fF/1yf8AmtAFjNeZLHryfGnQZvEF9E/n2N4YbK2B8q2UbB1PLsc8tx2AHr6XmuP1eyupfi54cvI7aZ7WGwvElnWMlEZjHtBboCcHAPXFEfiXz/JlJ6P+upz3xUk8Pp4j0wfES5lXwrJayKsMcsoV7oMpBdYvnYbc4PIBznGaz7DWZdN+DviC80G/mOlS3f2bQppJWeSGFykY5JLABmbAbkACuj8QzX3hfx9L4mbQtT12xuNPjtEXTIlmmtXV2J/dkglW3Dkf3eayz4Z1bxH4Z8YXo0yXTH1qWK50/Trrarq8SqQ7gcKzsoyM8YGaI25Xf5/f+dtvL7zS+q+X5fh5jdR8F6T8ONR0LW/DJuraefVIbTUS93JILxJcqTIGbGQxDAgda9WrzCXWNW8f6po2mHwprOjQ6ffx3l/c6lCIo/3WSEjIJ8zLAcjjHNem5qvs69/wsv1uZS3V+3+Y6im5ozUiHUU3NGaAHUU3NGaAHUU3NGaAHVAn/H/L/wBck/m1S5qFD/p8v/XJP5tQBT8Q+HNK8V6O+la/a/a7KRlZovMdMlTkcqQevvXmWh/DLwiPioJPDmkCztPDm155hcyyeddMMrH87EAIuGOO7AdjXr0j7I2YAttBOAMk1yXwysLqz8FR3Gp20ltf6hcz3tzHMhV1eSRiAwPIIXaOfSiOjuv6f9foVd8lv6/r/M858RReEPEHxC8Q3XifwxrniJrW5isYzpltO6WqJGCzO0bKPvM3GSeOlev+FV0aPwrp6eGdv9kiEC1CszYT0yxJz9ea52+8ba5oWsXOn6n4Q1bUxJOfsF1o9uJIXiIG0Sln/dsCcEnjjIrV8B6Ld6B4Qt7PUcC6eSW4ljVsiJpJGfYCODjdjI9Kcfh+4qo7v+tjpKKbmjNIzHUU3NGaAHUU3NGaAHUU3NGaAHUU3NGaAHVBaf6lv+usn/oZqXNQ2h/ct/11k/8AQzQBV1xdYk07yvD0lrDdyMFM90pZYVwcuFH3iOMDIHqa5H4WfbYfBOqL57X17Hqt6oluGwZnEhALEDjJHYcV32a4/wCGtldWGg6lHfW01s76xeSKs0ZQsjSkqwB6gjkHvQt36fqim/dXqvyZk+FIdYg+MOsr4g1BL26fSIJNsKFIoAZX+RAecDHU8k5PsK0PhHR/iR4p8R33iYXN0NNv/sNhGl1JEtsEjQl1CkfMWYnJz0Fb9lZXSfGTVb1raZbSTR7eNJzGRGziRyVDdCQCOPesZ9V1LwD4n12JfCusaxZ6vdi9tJ9LgEqq7RqrpJyNnzLncexo0sr9n9/N/lcu75nbfT8tfxN34ZX11eeCYotQuJLq4sLmeyeeVtzyCKVkUse52gZrrq5j4f6ReaL4Pgi1SMRX1xNLd3EQbd5byyM5TPfG7H4V0uaqW+pm7Xdth1FNzRmpEOopuaM0AOopuaM0AOopuaM0AOqCy/48Lf8A65L/ACqXNQ2R/wBAt/8Arkv8qAPLfiHqukal42k8P+I7fVtTsLWwSdNG0pZGkvZXZuSIyCVRUB5IGW+lRW+pw6H8Idefw3ql+kQnW1tLO+ieO50lpCieUxYlvlLbh6Z71v8AiAXvhTx/J4sttDv9asrzT1s7mPTYxLcQujkqwjJBZSGwcdMZrMl8Mat4n8O+L9UfT5rC61t4ZrCwuyokTyFXYXAJCszL0zwMZojbl1+f3/5G19V8rfd+Gu4zUfBek/DjUdC1vwybq2nn1SG01EvdySC8SXKkyBmxkMQwIHWvVq8wl1jVvH+qaNph8Kazo0On38d5f3OpQiKP91khIyCfMywHI4xzXpuar7Ovf8LL9bmUt1ft/mOopuaM1Ih1FNzRmgB1FNzRmgB1FNzRmgB1FNzRmgCK7/1K/wDXWP8A9DFT1Xuz+5X/AK6x/wDoYqbNAHnnxU1O8vfDms6Notw9ubXTpLrUbmPrGgUlIgf7zkc+ig+oqPX2a98G+CvD4uJreLWpLeC4eCQo5hWEyMoYcjO0D6E1keJvh74ztfDviV9N8bSXEF8txcSabHokTyXG4H935m4uTjCgjoAMDtWjNoPiGw8A+Fr6Xztd1XQ7mK7aBbdYJWiKFGiC9Nyq/fk7fU0RtbX+7+ps+nK/5rfcrfj/AFoS6V4d0/4e/EvSdM8NCe10rWbS4Etk1w8kYmj2sHXexIJBINelV55ot5qHjTx9Ya7J4f1TRdN0i1mjQarF5Ms80pUHEeT8oVTyeua9BzT1sr/1qzOW46im5ozSJHUU3NGaAHUU3NGaAHUU3NGaAHUU3NGaAIrj/j4tP+u//srVvL90VgT/APHxaf8AXf8A9lat9fuikddH4TH1b/kIWn+8f/QTSUurf8hC0/3j/wCgmmZpmFb4x1FNzRmgxHUU3NGaAHUU3NGaAHUU3NGaAHUU3NGaAHUU3NGaAHUU3NGaAHVA3/H/ABf9cn/mtS5qFj/p0X/XN/5rQBYopuaM0AOopuaM0AOopuaM0AOopuaM0AOopuaM0AOopuaM0AOopuaM0AOqBf8Aj/l/65J/NqlzUKn/AE6X/rmn82oAsUU3NGaAHUU3NGaAHUU3NGaAHUU3NGaAHUU3NGaAHUU3NGaAHUU3NGaAHVBaf6lv+usn/oZqXNQ2p/ct/wBdJP8A0M0AWKKbmjNADqKbmjNADqKbmjNADqKbmjNADqKbmjNADqKbmjNADqKbmjNADqgs/wDjwt/+uS/yqXNQ2Z/0GD/rmv8AKgCxRTc0ZoAdRTc0ZoAdRTc0ZoAdRTc0ZoAdRTc0ZoAdRTc0ZoAdRTc0ZoAiu/8AUr/11j/9DFT1Xuj+5X/rpH/6GKmzQA6im5ozQA6im5ozQA6im5ozQA6im5ozQA6im5ozQA6im5ozQA6im5ozQBFN/wAfFp/12/8AZWroF+6K5+X/AI+bT/rt/wCytXQL90UjsofCZ+qaYuoIFft09qyf+ERj/wCesn/fVdPRQbWRzH/CIx/89ZP++qP+ERj/AOesn/fVdPRQFkcx/wAIjH/z1k/76o/4RGP/AJ6yf99V09FAWRzH/CIx/wDPWT/vqj/hEY/+esn/AH1XT0UBZHMf8IjH/wA9ZP8Avqj/AIRGP/nrJ/31XT0UBZHMf8IjH/z1k/76o/4RGP8A56yf99V09FAWRzH/AAiMf/PWT/vqj/hEY/8AnrJ/31XT0UBZHMf8IjH/AM9ZP++qP+ERj/56yf8AfVdPRQFkcx/wiMf/AD1k/wC+qT/hEU/56yf99V1FFAWRzH/CIx/89ZP++qP+ERj/AOesn/fVdPRQFkcx/wAIjH/z1k/76o/4RGP/AJ6yf99V09FAWRzH/CIx/wDPWT/vqj/hEY/+esn/AH1XT0UBZHMf8IjH/wA9ZP8Avqj/AIRGP/nrJ/31XT0UBZHMf8IjH/z1k/76o/4RGP8A56yf99V09FAWRzH/AAiMf/PWT/vqj/hEY/8AnrJ/31XT0UBZHMf8IjH/AM9ZP++qP+ERj/56yf8AfVdPRQFkcx/wiMf/AD1k/wC+qT/hEU/56yf99V1FFAWRzH/CIx/89ZP++qP+ERj/AOesn/fVdPRQFkcx/wAIjH/z1k/76o/4RGP/AJ6yf99V09FAWRzH/CIx/wDPWT/vqj/hEY/+esn/AH1XT0UBZHMf8IjH/wA9ZP8Avqj/AIRGP/nrJ/31XT0UBZHMf8IjH/z1k/76o/4RGP8A56yf99V09FAWRzH/AAiMf/PWT/vqj/hEY/8AnrJ/31XT0UBZHMf8IjH/AM9ZP++qP+ERj/56yf8AfVdPRQFkcv8A8IjH/wA9ZP8AvqgeEY/+esn/AH1XUUUBZHMf8IjH/wA9ZP8Avqj/AIRGP/nrJ/31XT0UBZHMf8IjH/z1k/76o/4RGP8A56yf99V09FAWRzH/AAiMf/PWT/vqj/hEY/8AnrJ/31XT0UBZHMf8IjH/AM9ZP++qP+ERj/56yf8AfVdPRQFkcx/wiMf/AD1k/wC+qP8AhEY/+esn/fVdPRQFkcx/wiMf/PWT/vqj/hEY/wDnrJ/31XT0UBZHMf8ACIx/89ZP++qP+ERj/wCesn/fVdPRQFkcufCMf/PWT/vqj/hEY/8AnrJ/31XUUUBZHMf8IjH/AM9ZP++qP+ERj/56yf8AfVdPRQFkcx/wiMf/AD1k/wC+qP8AhEY/+esn/fVdPRQFkcx/wiMf/PWT/vqj/hEY/wDnrJ/31XT0UBZHMf8ACIx/89ZP++qP+ERj/wCesn/fVdPRQFkcx/wiMf8Az1k/76o/4RGP/nrJ/wB9V09FAWRzH/CIx/8APWT/AL6o/wCERj/56yf99V09FAWRzH/CIx/89ZP++qP+ERj/AOesn/fVdPRQFkcufCMf/PWT/vql/wCERj/56yf99V09FAWRzH/CIx/89ZP++qP+ERj/AOesn/fVdPRQFkcx/wAIjH/z1k/76o/4RGP/AJ6yf99V09FAWRzH/CIx/wDPWT/vqj/hEY/+esn/AH1XT0UBZHMf8IjH/wA9ZP8Avqj/AIRGP/nrJ/31XT0UBZHMf8IjH/z1k/76o/4RGP8A56yf99V09FAWRzH/AAiMf/PWT/vqj/hEY/8AnrJ/31XT0UBZHMf8IjH/AM9ZP++qP+ERj/56yf8AfVdPRQFkYNj4bitZ1kJLlTkbucVvDgUUUDtY/9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {
      "image/jpeg": {
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='Images/A3_data1.JPG', width=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Der Bedarf ist vollständig zu befriedigen. \n",
    "- In der Endmontage der beiden Endprodukte können insgesamt pro Schicht maximal 20 ME (A und B zusammen) und in der Vorfertigung des Zwischenproduktes können pro Schicht maximal 10 Mengeneinheiten hergestellt werden.\n",
    "- Jede Planungsperiode umfasst 20 Arbeitstage. Die Endmontage arbeitet im Einschichtbetrieb, die Vorfertigung im Zweischichtbetrieb.\n",
    "\n",
    "- Zu Beginn der ersten Periode liegen folgende Lagerbestände vor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "image/jpeg": {
       "width": 700
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='Images/A3_data2.JPG', width=700)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Die Lagerung von Zwischenprodukt C ist beschränkt auf 100 ME, ein Sicherheitsbestand von 20 ME ist einzuhalten.\n",
    "- Die Lagerung der Produkte A, B und C verursachen konstante Kosten von 1 und 1,25 und \u000b",
    "0,8 GE/(ME*Periode)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Vorbereitung**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Python-Module**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pulp import *\n",
    "from itertools import islice\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Daten aus der Aufgabenstellung**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": true
   },
   "source": [
    "**Primärbedarf / Nachfrage:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       1      2      3      4      5\n",
      "A  130.0  130.0  210.0  250.0  160.0\n",
      "B  180.0   75.0  110.0   60.0   40.0\n",
      "C    0.0    0.0    0.0    0.0    0.0\n"
     ]
    }
   ],
   "source": [
    "d = pd.DataFrame()\n",
    "\n",
    "d.loc['A', 1] = 130\n",
    "d.loc['A', 2] = 130\n",
    "d.loc['A', 3] = 210\n",
    "d.loc['A', 4] = 250\n",
    "d.loc['A', 5] = 160\n",
    "\n",
    "d.loc['B', 1] = 180\n",
    "d.loc['B', 2] = 75\n",
    "d.loc['B', 3] = 110\n",
    "d.loc['B', 4] = 60\n",
    "d.loc['B', 5] = 40\n",
    "\n",
    "d.loc['C'] = 0 # Zwischenprodukt C\n",
    "\n",
    "Produkte = d.index.tolist()\n",
    "Perioden = d.columns.tolist()\n",
    "\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": true
   },
   "source": [
    "$$ $$\n",
    "**Direktbedarf an Vorprodukten:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A  B  C\n",
      "A  0  0  0\n",
      "B  0  0  0\n",
      "C  1  1  0\n"
     ]
    }
   ],
   "source": [
    "A = pd.DataFrame(index=Produkte, columns=Produkte) # Direktbedarfsmatrix\n",
    "\n",
    "A.loc['C','A'] = 1\n",
    "A.loc['C','B'] = 1\n",
    "\n",
    "A = A.fillna(0)\n",
    "\n",
    "print(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": false
   },
   "source": [
    "$$ $$\n",
    "**Maximale Kapazitäten der zwei Abteilungen:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hide_input": true
   },
   "outputs": [],
   "source": [
    "# Kapazitaeten\n",
    "r_Vorfertigung = 400 # 20*20*1\n",
    "r_Endmontage = 400 # 10*20*2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ $$\n",
    "**Angaben zur Lagerhaltung:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Lagerkosten  Anfangsbestand  Mindestbestand  Maximalbestand\n",
      "A         1.00            25.0             NaN             NaN\n",
      "B         1.25            15.0             NaN             NaN\n",
      "C         0.80            50.0            20.0           100.0\n"
     ]
    }
   ],
   "source": [
    "# Weitere Inputdaten\n",
    "\n",
    "data = pd.DataFrame()\n",
    "\n",
    "data.loc['A','Lagerkosten'] = 1.00\n",
    "data.loc['B','Lagerkosten'] = 1.25\n",
    "data.loc['C','Lagerkosten'] = 0.80\n",
    "\n",
    "data.loc['A','Anfangsbestand'] = 25\n",
    "data.loc['B','Anfangsbestand'] = 15\n",
    "data.loc['C','Anfangsbestand'] = 50\n",
    "\n",
    "data.loc['C','Mindestbestand'] = 20\n",
    "data.loc['C','Maximalbestand'] = 100\n",
    "\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": true
   },
   "source": [
    "$$ $$\n",
    "## **Optimierungsproblem**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Problemformulierung**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "$$\\min \\sum_{i}\\sum_{t}x_{i,t}^{L} \\cdot c_i^{L}$$\n",
    "\n",
    "$$ Restriktionen: $$\n",
    "\n",
    "$$ x_{A,t}^{P} + x_{B,t}^{P} \\leq r_{Endmontage} \\quad \\forall t \\in T $$\n",
    "\n",
    "$$ x_{C,t}^{P} \\leq r_{Vorfertigung} \\quad \\forall t \\in T $$\n",
    "\n",
    "$$ x_{i,t}^{L} = x_{i,t-1}^{L} + x_{i,t}^{P} - \\pi_{i,t} - \\sigma_{i,t} \\quad \\forall i \\in I, \\forall t \\in T $$\n",
    "\n",
    "$$ x_{i,t}^{L} \\geq x_{i,t}^{L^{min}} \\quad \\forall i \\in I, \\forall t \\in T $$\n",
    "\n",
    "$$ x_{i,t}^{L} \\leq x_{i,t}^{L^{max}} \\quad \\forall i \\in I, \\forall t \\in T $$\n",
    "\n",
    "$$ x_{i,t}^{P}, x_{i,t}^{L} \\geq 0 \\quad \\forall i \\in I, \\forall t \\in T $$\n",
    "\n",
    "$$ \\textrm{mit Produkten $I$ und Perioden $T$} $$\n",
    "$$ \\textrm{Anm.: $\\pi_{i,t}$: Primärbedarf, $\\sigma_{i,t}$: Sekundärbedarf} $$.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Formulierung mit PuLP**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "prob = LpProblem('Problem_Erweiterung', LpMinimize)\n",
    "\n",
    "xP = LpVariable.dicts('Produktionsmenge', (Produkte, Perioden), lowBound=0)\n",
    "xL = LpVariable.dicts('Lagermenge', (Produkte, Perioden), lowBound=0)\n",
    "\n",
    "prob += lpSum([xL[i][t]*data.loc[i,'Lagerkosten'] for i in Produkte for t in Perioden])\n",
    "\n",
    "for t in Perioden:\n",
    "    prob += xP['A'][t] + xP['B'][t] <= r_Endmontage\n",
    "    prob += xP['C'][t] <= r_Vorfertigung\n",
    "                \n",
    "for i in Produkte:\n",
    "    prob += xL[i][1] == data.loc[i,'Anfangsbestand'] + xP[i][1] - d.loc[i,1] - lpSum([xP[j][1] * A.loc[i,j] for j in Produkte])\n",
    "    for t in list(islice(Perioden, 1, None)):\n",
    "        prob += xL[i][t] == xL[i][t-1] + xP[i][t] - d.loc[i,t] - lpSum([xP[j][t] * A.loc[i,j] for j in Produkte])\n",
    "        \n",
    "for t in Perioden:\n",
    "    prob += xL['C'][t] >= data.loc['C','Mindestbestand']\n",
    "    prob += xL['C'][t] <= data.loc['C','Maximalbestand']\n",
    "                \n",
    "#print(prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ $$\n",
    "#### **Ergebnis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Status: Optimal\n",
      "Gesamtkosten = 80 GE\n",
      "\n",
      "\n",
      "   Bedarf_A  Produktion_A  LagerStart_A  LagerEnd_A  Bedarf_B  Produktion_B  \\\n",
      "1     130.0         105.0          25.0         0.0     180.0         165.0   \n",
      "2     130.0         130.0           0.0         0.0      75.0          75.0   \n",
      "3     210.0         210.0           0.0         0.0     110.0         110.0   \n",
      "4     250.0         250.0           0.0         0.0      60.0          60.0   \n",
      "5     160.0         160.0           0.0         0.0      40.0          40.0   \n",
      "\n",
      "   LagerStart_B  LagerEnd_B  Bedarf_C  Produktion_C  LagerStart_C  LagerEnd_C  \\\n",
      "1          15.0         0.0       0.0         240.0          50.0        20.0   \n",
      "2           0.0         0.0       0.0         205.0          20.0        20.0   \n",
      "3           0.0         0.0       0.0         320.0          20.0        20.0   \n",
      "4           0.0         0.0       0.0         310.0          20.0        20.0   \n",
      "5           0.0         0.0       0.0         200.0          20.0        20.0   \n",
      "\n",
      "   Bedarf_A+B  Produktion_A+B  LagerStart_A+B  \n",
      "1       310.0           270.0            40.0  \n",
      "2       205.0           205.0             0.0  \n",
      "3       320.0           320.0             0.0  \n",
      "4       310.0           310.0             0.0  \n",
      "5       200.0           200.0             0.0  \n"
     ]
    }
   ],
   "source": [
    "prob.writeLP(\"Problem_Erweiterung.lp\")\n",
    "prob.solve()\n",
    "\n",
    "print()\n",
    "print()\n",
    "print(\"Status:\", LpStatus[prob.status])\n",
    "print(\"Gesamtkosten = {:,.0f} GE\".format(value(prob.objective)))\n",
    "print()\n",
    "print()\n",
    "\n",
    "Results = pd.DataFrame()\n",
    "\n",
    "for i in Produkte:\n",
    "    for t in Perioden:\n",
    "        Results.loc[t, 'Bedarf_'+str(i)] = d.loc[i,t]\n",
    "        Results.loc[t, 'Produktion_'+str(i)] = xP[i][t].varValue        \n",
    "    Results.loc[1, 'LagerStart_'+str(i)] = data.loc[i,'Anfangsbestand']\n",
    "    for t in list(islice(Perioden, 1, None)):   \n",
    "        Results.loc[t, 'LagerStart_'+str(i)] = xL[i][t-1].varValue\n",
    "    for t in Perioden:       \n",
    "        Results.loc[t, 'LagerEnd_'+str(i)] = xL[i][t].varValue\n",
    "    \n",
    "Results['Bedarf_A+B'] = Results['Bedarf_A'] + Results['Bedarf_B']\n",
    "Results['Produktion_A+B'] = Results['Produktion_A'] + Results['Produktion_B']\n",
    "Results['LagerStart_A+B'] = Results['LagerStart_A'] + Results['LagerStart_B']\n",
    "       \n",
    "print(Results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ $$\n",
    "#### **Zeitlicher Verlauf**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "y_max = max([Results['Bedarf_A+B'].max(), r_Endmontage])\n",
    "\n",
    "p1 = plt.bar(Results.index, Results['Produktion_A+B'], label='Produktion A+B')\n",
    "p2 = plt.bar(Results.index, Results['LagerStart_A+B'], bottom=Results['Produktion_A+B'], label='Bestand A+B (Periodenbeginn)')\n",
    "p3 = plt.step(Results.index, Results['Bedarf_A+B'], color='red', where='mid', linewidth=3, label='Bedarf A+B')\n",
    "p4 = plt.axhline(y=r_Endmontage, linewidth=3, color='black', linestyle='dashed', label='Kapazität Endmontage')\n",
    "\n",
    "plt.xlabel('Periode t', fontsize=14, labelpad=10)\n",
    "plt.ylabel('Mengeneinheiten', fontsize=14, labelpad=12)\n",
    "plt.ylim(0, 1.1*y_max)\n",
    "fig.legend(loc=\"center right\", fontsize=12, borderaxespad=0.2)\n",
    "plt.subplots_adjust(right=0.7)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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