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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Encontra a velocidade ideal do carro que proporcione o menor custo"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import absolute_import\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"from ortools.linear_solver import pywraplp\n",
"from ortools.sat.python import cp_model\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"\n",
"# valComb = 5 (valor do litro de combustível)\n",
"# valAlu = 50 (Valor do alugue do carro por hora)\n",
"# dist = 200 (distância a percorrer)\n",
"# velo = 120 (velocidade do carro)\n",
"# cons = 0.0000002 * (velo ^ 3) (função de consumo em litros pela velocidade)\n",
"# valConsDist = (dist / cons) * valComb (valor gasto para percorrer distância)\n",
"# valTotal = valConsDist + (valAlu * (dist / velo)) (valor total do percurso)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Gráfico que mostra o consumo pela velocidade"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Litros (L/Km)')"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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2lHMO6s0ZeQ0OSCC7oKEkcAzexfx6F3ZzgE+AeUABUAp0xKs3GAGMBnYC9wAPNFfAIpIc5n27jSsnzWFY74785aTBQYfTYjVUJzATGOPCrj9wHnAUcCHeUNBVVgEf+8tfsZCVN1OsIpIkNm4v4TdPzaBjm3T++csRZKSpQ1hzaVRnMQvZYuDP/gsXdp2ATGCThays+cITkWRTUl7Jxf+eydYd5Tz324Pp2i4z6JBatF3qMWwh29LUgYiImBnXvfAVs1Zt5aFzhzNkzw5Bh9TiqbGtiMSNB6ct5cVZ33LlMQM4foh6BMeCkoCIxIUpX6/n7rcWcvL+e3DJT/sFHU7SUBIQkcDNX1vI5c/OZuieHbjr50PxZqKVWFASEJFAbSz0WgK1z0znkfPyNDR0jO12EvAnnhcRiVpRaQUXPDGdLTvKePT8PLq2V0ugWGt0EnBh9zsXdtdU+/kAF3ZrgE0u7Ga6sNP8biLSaBWVEf7wzJd8s347489RS6CgRPMk8EegsNrP9wNrgXP87fy1CeMSkRbMzLjxpXlMW5TPracO4ciBXYMOKWlF00+gN7AQwIVdDnAocJSF7H0XdmVoqAgRaaTx7y1h4vTVXHJkP34xsnfQ4SS1aJ4ESoFW/ucjgR3Ah/7Pm/HGERIRqdeLs9Zwz5RFnDZsT648dkDQ4SS9aJ4EvgD+4NcDXAq8aSGr9L/ri1c0JCJSp0+WFHDN5Lkc3LcLd/5MTUHjQTRPAlcCg4GvgF7ADdW+OxNvEDkRkVrNX1vIxU/PpE92Wx765QhapamFejxo9JOAhWw+0M+FXRdgs4Ws+pzCVwHrmzo4EWkZVhQUc97jX5CVkca/LhhJh9bpQYckvqgHkLOQbXJhl+2PJLrZQrbJQvZVM8QmIi3AhsISzn3scyojESaOO5g9O7YOOiSpJqrnMRd2Z7qwWwBsAL4BNrqwW+DC7vRmiU5EEtrWHWX88rHP2VJcxpMXjqRfV808G2+i6Sz2C2ACsAy4ABjjvy8DJrqwO6tZIhSRhLSjzOsNvKJgB4+cl6f5geNUNMVBNwAPW8h+W2P5Uy7sHgJuBCY2WWQikrBKK7yJYeas3sqD547gkH7ZQYckdYimOKgf8Hwd3z3vfy8iSa4yYlzx7Bw+XFzAX382lOP27R50SFKPaJLABiCvju/y/O9FJIlFIsa1z8/lta/Wcf2YQZyR1yvokKQB0RQH/Qu4yYVdKjAZ76LfFTgdryjojqYPT0QShZlxw0vzeG7mGi49qj/jDt876JCkEaJJAjcD6cC1QLja8p3APf739XLO9QKeAroDEeBhM/tHFDGISBwyM2565WsmfLGK3x+xN/97dP+gQ5JGiqazWAS4wYXdPcAQoAewDpgXxcTzFcCVZvalc64dMNM5N9XM5kcbuIjEBzPj1tcW8OSnK/nNYX24+riBGg4igTQqCbiwywReAW63kL3P9wPHRcXM1uElDsxsu3NuAbAnoCQgkoDMjDvfXMhjHy3nV4fkcv2YfZQAEkyjKoYtZCXAgUCTzfvmnMsFhgGfN9U2RSS2/j51EQ9NW8q5o3oTOmmwEkACiqZ10CvAqU2xU+dcFl6z0svNrLCW78c552Y452bk5+c3xS5FpAmZGfe9vYj7313CWQf24uaThygBJKhoKobfAu52YdcDeB2vdVD1QeSwkL3e0Eacc+l4CeAZM3uhtnXM7GHgYYC8vDyrbR0RCYaZcddbC3nw/aWcPqInt5+2HykpSgCJKpok8LT//j/+qyajgeIi590qPAYsMLO/RbFvEYkDZsbNr87nXx+v4NxRvbn55CFKAAkumiTQpwn2dyjwS+Ar59xsf9n1Zg0/QYhIsCIR48aX5/Gfz1dx0U/6cONYVQK3BNEkAQPWWcjKa37hwi4N2KPBDZh9BOhfjUiCqYwYf3p+LpNnruEPR+7NVceqGWhLEU3F8HK81jy12d//XkRamPLKCJdNnMXkmWu48pgBXH3cICWAFiSaJ4H6znom3kT0ItKClJRX8scJs5g6fwPXjxmkoSBaoHqTgAu7ocAB1RaNcWE3qMZqmcAZwKImjk1EArRtZzm/eWoG01ds5uZT9uW8g3ODDkmaQUNPAqcBIf+zAX+pY73lwMVNFZSIBGtjYQnnPf4FS/OLuP+sYZy0f4NVfpKgGkoCt+MNDueAQuCnwPQa65TVVlksIolpeUExv3zsczYXl/H4rw7ksP45QYckzajeJOBf3Ksu8FHNRywiieerNdv41b++wICJ40ZpSsgk0FCdwGBgqYWs1P9cLwtpNFCRRPXxkgLGPTWDjm1a8e+LRtI3JyvokCQGGioOmgeMAr7wP9c1hIOjET2GRSQ+PT9zDde+MJe+2Vk8ddFIurXPDDokiZGGksCRfD/M85HNHIuIxJiZ8fep3kBwh+zdhQfPGUGHNulBhyUx1FCdwLTaPtfkwi4baLC4SETiR0l5JddMnssrc9ZyRl5Pbj11P1qlqeov2UTTWaw+o4FJqDhIJCFsKirl4n/PZMbKLVxz/EB+N3pv9QJOUk2VBEQkQSzNL+LCJ6azblsJD5w9jBOHqg9AMlMSEEkiHy7O55L/zCItxTFx3CiG9+4UdEgSMCUBkSRgZjz64XLueGMB/bpm8dj5B9Krc5ugw5I4oCQg0sLtLKvk2hfm8vLstRy/b3fuPWN/2mbov754Guoslk/dfQOqy2iacESkKa3ZsoOL/z2T+esKuerYAfzhyH6qAJYfaOh2YDyNSwIiEmc+XbqJP/znS8orIjx6Xh5H7dMt6JAkDjXUT+CmGMUhIk3EzHjso+Xc8cY37NWlDY+cl8feGgJC6qCCQZEWZNuOcq6ePIcp8zdwzOBu3HvG/rTPVA9gqZuSgEgLMXfNVn7/zJes31bCjWP34aKf9FH5vzRISUAkwZkZT326ktteW0B2Vism/fZgtf+XRlMSEElg20vKufb5r3jtq3X8dFBX7j19fzq1bRV0WJJAlAREEtSMFZv530mzWbu1hGtPGMS4w/qSkqLiH4mOkoBIgimvjPB/7yzmgfeWsGen1ky6eBQj9uocdFiSoJQERBLIioJiLnt2NnNWb+Vnw3ty08mDaafWP7IblAREEoCZMWnGasL/nU96agrjzx7O2KE9gg5LWgAlAZE4t3F7CTe+OI8p8zdwyN5duPeM/enRoXXQYUkLoSQgEqfMjBdnfUv4v/PZWV7JDWO8tv+q/JWmFNMk4Jx7HDgR2GhmQ2K5b5FEsm7bTm54cR7vfrOREXt14q6fD9XQD9IsYv0k8ATwAPBUjPcrkhCqyv5vfXUB5ZEIfzlxMOcfkkuq7v6lmcQ0CZjZB8653FjuUyRRrCgo5s8vz+PDxQWM6tuZO382lL26tA06LGnh4rJOwDk3DhgH0Lt374CjEWlepRWVPPT+Msa/v4RWqSnccuoQzhnZW2X/EhNxmQTM7GHgYYC8vDzNZyAt1keLC/jzy/NYXlDMiUN78OcTB9OtfWbQYUkSicskINLSbSws4ZbXFvDfOWvJ7dKGpy4cyeEDcoIOS5KQkoBIDJVVRHjykxXc/85iSisiXHZUf353xN5kpqcGHZokqVg3EZ0AHAFkO+fWACEzeyyWMYgEwcyYMn8Dd7y+gBWbdnDEwBz+cuJg+qrZpwQs1q2DfhHL/YnEg/lrC7nl1fl8umwT/bpm8cQFB3LEwK5BhyUCqDhIpNls3F7C36cuYuL01XRonc7Np+zLL0b2Jj01JejQRL6jJCDSxLbtLOef05byr49XUF4Z4YJD+nDZUf3p0EajfUr8URIQaSI7yyp54pMVPDRtKdt2lnPy/ntwxTEDyM1Why+JX0oCIrupvDLCs9NXc/87i9m4vZSfDurKVccOZPAe7YMOTaRBSgIiu6i0opLJM9fw4PtLWbNlJwfmdmL8OcM5MFezfEniUBIQiVJJeSUTvljFP6ctY31hCQf06sgtpwzhiIE5OKehHiSxKAmINFJxaQXPfL6Shz9YTkFRKSP7dOae0/fn0H5ddPGXhKUkINKAjYUlPPnpCp75fBVbd5RzWP9sLjlyGAf17RJ0aCK7TUlApA4L1hXy6IfLeWXOt1REjOMGd2fc6L4M790p6NBEmoySgEg1kYjxweJ8HvtoOR8uLqBNq1TOOWgvLjg0V2P7S4ukJCACbCku47mZq3nm81Ws3LSDbu0zuOb4gZwzci918pIWTUlAkpaZ8eWqrTzz2Upe/WodZRURRuZ25opjBnDCkB60StPwDtLyKQlI0tlSXMYrc9by7PTVzF9XSFZGGmfm9eLcUXsxsHu7oMMTiSklAUkK5ZURpi3M5/kv1/D2gg2UVxr77tGe204bwqkH7EnbDP1XkOSkf/nSYpkZX68t5KVZ3/LS7G8pKCqjS9tWnHdwLj8f0ZN9emhYBxElAWlxFm3Yzqtz1vLfuetYXlBMWorjqH268vMRvThiYI6GchapRklAWoSl+UW8Pncdr85dx8IN20lxMKpvF35zWF+OH9Kdzm1bBR2iSFxSEpCEVBkxZq/ewpT5G5g6fwPL8osBODC3E+GT9+WE/brTtV1mwFGKxD8lAUkYRaUVfLp0E2/P38A732ygoKiMtBTHqL5dOP/gXI7dtxs9OrQOOkyRhKIkIHErEjHmrytk2qJ8PliUz5ertlBeabTLSGP0wByOGdyNIwZ2pUNrdeYS2VVKAhI3zIxVm3fw2bJNfLp0Ex8tKaCgqAyAfXq058Kf9GF0/xzycjurI5dIE1ESkMCYGSs27eDzZZv4bNkmPlu2mfWFJQB0aduKQ/tlM3pADof1z6Zre5XvizQHJQGJmeLSCuau2caXq7Ywa9UWZq3ayqZi704/OyuDg/p2ZlTfLozq05l+XbM0Rr9IDCgJSLMoKa/km/XbmfftNr5eW8ic1Vv5Zn0hEfO+75vTliMGdmX4Xh05qE8X9s5pq4u+SACUBGS3mBn520tZtKGIhRu2M39tIV+v3cbijUVU+lf8Dq3T2W/PDlxyZD+G9e7EAb060knt9kXigpKANEokYqzdtpPlBcUsLyhmsX/RX7RhO1t3lH+3XnZWK4bs2YGj9+nGkD3bs+8eHejZqbXu8kXilJKAfKe0opJvt+xk9ZadrNmyg1Wbd7CioJgVBTtYsamY0orId+u2y0xjYLd2nDCkBwO7ZTGgWzsGdG9HdlZGgEcgItGKeRJwzh0P/ANIBR41s7/GOoZkVFYRIb+olPXbdrJ+Wynrtu1kQ2EJ6wtLWbd1J6u37GBDYekPfic91dG7cxv6ZGcxemAOfbLbktulLX1z2tK1XYbu7kVagJgmAedcKjAeOAZYA0x3zr1iZvNjGUeii0SM4rIKtu4o9147y9iyo5ytO8rYuqOcTUWlFBSVkV9USkFRKQXbSyksqfjRdjLTU+jePpPuHTI5rH8OvTq1oWen1vTq7L13a59Jaoou9CItWayfBEYCS8xsGYBzbiJwCtAikkAkYlREjMqIUVYZodx/lVVUvRulFZWUlEcoqaiktLyS0ooIJeWV7CyrZEd5JTtKKykuq2BnWSXFZZXsKK1ge0kFhSXlbC+pYHtJOdtLKzCrO452mWnkZGWQnZXBoO7tyO6XTXZWBjntMujeIZMeHTLp3j6TDq3TdTcvkuRinQT2BFZX+3kNcFBz7OjE//uQkvJIrd+ZfwX97jpq379FzDADw383b/1KMyLmXegj5l3oI+YNZFYZMcojkXovzI2V4qBtqzTaZKTSplUabVql0i4zjV6d29AuM412GWm0y0ynXWYaHduk07FNKzq1aUUn/3OH1unqTSsijRbrJFDbbeePLp3OuXHAOIDevXvv0o765WRRXlnPVdn9MKCqO+IU5y1zzuEcOLz3VOdISXGkOEhNcaQ475WW6khNcaSlONJSUkhL9Zanpzoy0lJIT/VfaSm0Sk0hIy2FjPQUMtJSyUxPITM91XulpdA2I42MtBTdnYtIzMQ6CawBelX7uSewtuZKZvYw8DBAXl7eLt1f33fWsF35NRGRpBLrcoPpQH/nXB/nXCvgLOCVGMcgIiK+mD4JmFmFc+4S4C28JqKPm9nXsYxBRES+F/N+Amb2OvB6rPcrIiI/pmYkIiJJTElARCSJKQmIiCQxJQERkSTLDp4LAAAMOklEQVSmJCAiksScNcVYB83IOZcPrNzFX88GCpownCC1lGNpKccBOpZ41FKOA3bvWPYys5zGrBj3SWB3OOdmmFle0HE0hZZyLC3lOEDHEo9aynFA7I5FxUEiIklMSUBEJIm19CTwcNABNKGWciwt5ThAxxKPWspxQIyOpUXXCYiISP1a+pOAiIjUo0UmAefc8c65hc65Jc65a4OOJ1rOuRXOua+cc7OdczP8ZZ2dc1Odc4v9905Bx1kb59zjzrmNzrl51ZbVGrvz3O+fp7nOueHBRf5jdRzLTc65b/1zM9s5N6bad9f5x7LQOXdcMFH/mHOul3PuPefcAufc1865y/zlCXde6jmWRDwvmc65L5xzc/xjCfvL+zjnPvfPy7P+sPs45zL8n5f43+c2SSBm1qJeeENULwX6Aq2AOcDgoOOK8hhWANk1lt0FXOt/vha4M+g464j9cGA4MK+h2IExwBt4k7mNAj4POv5GHMtNwFW1rDvY/7eWAfTx/w2mBn0Mfmw9gOH+53bAIj/ehDsv9RxLIp4XB2T5n9OBz/2/9yTgLH/5Q8Dv/M+/Bx7yP58FPNsUcbTEJ4HvJrM3szKgajL7RHcK8KT/+Ung1ABjqZOZfQBsrrG4rthPAZ4yz2dAR+dcj9hE2rA6jqUupwATzazUzJYDS/D+LQbOzNaZ2Zf+5+3AArz5vhPuvNRzLHWJ5/NiZlbk/5juvwz4KTDZX17zvFSdr8nAUa4J5qJtiUmgtsns6/tHEo8MmOKcm+nPtwzQzczWgfcfAegaWHTRqyv2RD1Xl/jFJI9XK5ZLiGPxixCG4d11JvR5qXEskIDnxTmX6pybDWwEpuI9qWw1swp/lerxfncs/vfbgC67G0NLTAKNmsw+zh1qZsOBE4A/OOcODzqgZpKI5+pBYG/gAGAdcK+/PO6PxTmXBTwPXG5mhfWtWsuyeD+WhDwvZlZpZgfgzbc+EtinttX892Y5lpaYBBo1mX08M7O1/vtG4EW8fxwbqh7J/feNwUUYtbpiT7hzZWYb/P+4EeARvi9aiOtjcc6l4100nzGzF/zFCXleajuWRD0vVcxsK/A+Xp1AR+dc1ayP1eP97lj87zvQ+OLKOrXEJJDQk9k759o659pVfQaOBebhHcP5/mrnAy8HE+EuqSv2V4Dz/NYoo4BtVcUT8apG2fhpeOcGvGM5y2/B0QfoD3wR6/hq45cbPwYsMLO/Vfsq4c5LXceSoOclxznX0f/cGjgar47jPeDn/mo1z0vV+fo58K75tcS7Jega8uZ44bVuWIRXvnZD0PFEGXtfvNYMc4Cvq+LHK/t7B1jsv3cOOtY64p+A9zhejnfnclFdseM93o73z9NXQF7Q8TfiWP7txzrX/0/Zo9r6N/jHshA4Iej4q8X1E7xig7nAbP81JhHPSz3HkojnZSgwy495HvAXf3lfvES1BHgOyPCXZ/o/L/G/79sUcajHsIhIEmuJxUEiItJISgIiIklMSUBEJIkpCYiIJDElARGRJKYkICKSxNIaXkWSnQu7V4G9LGT71fH9A8A5QHcLWWkjttcPr236CRayN5swzl/j9RZtbSErqWe9l4AsC9nRTbDP2cAMC9mvm2BbKXhtxu+wkD3jL/sIWGMhO6vaejnANLx244dbyNY0wb4Pweuk1AWvg2Wtf0cXdrcCv7aQdfd/HgX8F+hrIdu+u3FI7OlJQBpjAjDEhd2+Nb9wYZeK13vxhcYkgGb2MnAwEHQcu+oXQFvg2bpWcGHXCW+gsSzgp02RAHxjgWkW+m5Uy0axkH0GzAcua6I4JMaUBKQxXgZ24N0h1nQk0A0vUQTKQpZvIfvMQgnbA/JS4CkLfTeC5A+4sOsATMEb7fMoC9mKJtz3WOC1XfzdfwG/828IJMGoOEgaZCEr8ouEzgT+XOPrs4ANeEUJALiw2wtvwpJj8SbzmAZcaiFbXNc+/AvITcCv8C5yi4FbLWQTa6x3hL/egXjDOcwCLreQzamtOMiP5Z/AaGA9cHMt+x4MhIBD8IpDlvm/80D1hOLCbijeJB/D/XWuqeNYRgO3AHnATryx36+s7y7bhd0gvEHPai1WcmGXBbwO7AWMrv63rFa8dgZwEt7YOVuBayxkE1zYXYeXYFKBR4EbahzXnsD+7HoSeMnf7tHAW7u4DQmIngSksSYA/V3Yjaha4MIuHe+CM8lCVukvywY+BvoB4/ASR0dgqgu7jHq2fzvwJ7whgU/GGyN+ggu706vt72jgbbwL63l4xSefAHvUtkEXdg5vHJlBwIXAVf7rwBqr9sQbuOv3eOPQPA7c5q9bta22eHfhrf393g7c7/9u9X0ejldc8y3wM+AK/3gerefYAY4CCvl+4LPqWuOVuw8CjraQLahjG/cAq4D/AT4F/u3C7l684ZV/BTwAXOfHVd1YYJGFbEmN5aku7NKqv6hlOGML2Va8v99u17FI7OlJQBrrDby7y7OAmf6y44DO/LAo6Eq8u/+j/IsDLuw+wZsy81d4d9g/4CeOS4Gwhex2f/FbLux64d31P+cvuwOYAYypdif7Rj0xn4Q3SNeBFrIZ/r5m4d01f3chtZBNwbvAVyWOj/DK3H8D3O2vdpF/rMMs5I2o6cJuNd7wv9XdiVe2/otqx7cOeNOF3U0Wsm/qiHUEML+OoqyT/fexFrK59RzvFAvZjf4+Z+Bd7McCgy1kEby/6al4iXtytd+rqyiorieXDbUsm0OczNgl0dGTgDSKX+n7InCGf6EE7y5/JfBZtVWrigSKqt09bgO+xCseqc1Qvh8hsbpngcEu7Dq7sGuPd6F8Mooy/5HAt1UJwD+OZXgjT37HhV1rF3a3uLBbilepXA6EgX5+i52qbX1RlQD8bU2j2njufpHNQcCkGnfPHwARP/66dAcK6vhuhr+f2/y/Q13eqRbbFmAT8L6fAKosodrMWv7T2VHUngQOxXtqqv56vI59F/jHIAlGTwISjQnABcDBLuy+xJvzdHyNi3I23sX+nFp+f2cd260aC77mHWbVz52ASryiiGjGte9O7ZPvbMSbz7XKPXjFSzfj1TFsxbuLvhZoBZQ0sK0qXfwYH/ZfNfWqZVmVTOqeIGQp8Ee8i/xLLuxOqKMl1tYaP5fVsSyz2s9H4CWoD2vZ3pe1NBGt6+9fWmO7kiCUBCQa7+JdmM/Cu3C348etgjbjXUhv58fqmtKw6sLSFe+poUo3/30LUIE3jnw0E56vp/a5mLv626xyOvAPC1lV0Q8u7E6pZVu5dWyrStU2b6T2CtJv64l1M17dSa0sZJ/59SMvA0+7sDuzxh3+rhoLTLWQle3mdjrSBLNcSewpCUijWcgqXdg9h3fR3BNYUEsZ9Tt4TwhfRdFvYC7e3fbp/DB5nIFXTr4ZvivnPs+F3UONLBKaDtzgwi6vWp1AH7yWMO9XW6811foW+C2VzqxlW2e4sOtRrU5gNF49AQAWskIXdtOBARay2xoRX3ULqb0J7ncsZK+7sLsIeAKvkvf3Ue6jNmPxKsF3Vy7eRE6SYJQEJFoTgEvwKhf/Usv39wBnA+/6PYnX4hWljMYrn55U8xcsZAUu7O4HQi7sInj1B6fjNTE9o9qqf8KrwH3Nhd0jeH0XDgU+tZDVVkH8X7zWNpNd2F3L92X9NYt1pgKXurBbjld8cgk//r/xGHA98LoLu5uBNnjNQDfVWO8aYIoLO/DmwS3Ca9Y5FviThWxpLXGC16Lqehd2nfzy/FpZyJ5yYdcNuMuF3QYLWbiudRviN0vtg9f0dHeNoJbmtxL/VDEs0foUr6WPAybW/NJCthFvsuwlwH14F+078YqOvqpnuzfg9S24BHgVr+fv2Ray7yqLLWTv4SWG9sB/8CqOf0IdxSx+cclJeHfZTwB/82OqOcfs7/Gamj6I15Rzth9L9W0V4bWGKvGP+8/A5XjTTlZf7328hNcdeBovEV2NV4GeX8/xv4uXgI6vZ52qfdwN3Avc5MLu4obWr8dYvHL/9buxDVzYHYhXb/Pi7mxHgqHpJUXihAu78UBPC1nN+ojm2t+7wIcWstBubuduYD8LWYMJTOKPkoBInPB7Ny/Au6DWVWwUV/xmsauAky1kHwUdj0RPxUEiccJCthJv2Ihae0DHqb2AvygBJC49CYiIJDE9CYiIJDElARGRJKYkICKSxJQERESSmJKAiEgS+/8ky8z/muAIrwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(start = 0, stop = 300) \n",
"plt.plot(x, (x**3)*0.0000002) \n",
"plt.xlabel('Velocidade (Km/H)', fontsize=15, color='green')\n",
"plt.ylabel('Litros (L/Km)', fontsize=15, color='green')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
},
"outputs": [
{
"ename": "TypeError",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-415bee89302d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Custo da viagem = %i'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0msolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mValue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalTotal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0mcustoDeViagem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-3-415bee89302d>\u001b[0m in \u001b[0;36mcustoDeViagem\u001b[0;34m()\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;31m#model.Add(valAlu = 50)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;31m#model.Add(dist = 200)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAdd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcons\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mvelo\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mvelo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 34\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAdd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalConsDist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdist\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mcons\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mvalComb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/ortools/linear_solver/pywraplp.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 561\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msetup_variable_operator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 562\u001b[0m setattr(Variable, opname,\n\u001b[0;32m--> 563\u001b[0;31m lambda self, *args: getattr(VariableExpr(self), opname)(*args))\n\u001b[0m\u001b[1;32m 564\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mopname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mLinearExpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOVERRIDDEN_OPERATOR_METHODS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 565\u001b[0m \u001b[0msetup_variable_operator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/ortools/linear_solver/linear_solver_natural_api.py\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, cst)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__mul__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mProductCst\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__rmul__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/ortools/linear_solver/linear_solver_natural_api.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, expr, coef)\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__coef\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoef\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 178\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__str__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: "
]
}
],
"source": [
"\n",
"\n",
"\n",
"\n",
"def custoDeViagem():\n",
" model = cp_model.CpModel()\n",
" # [END model]\n",
" solver = pywraplp.Solver('custoDeViagem',pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)\n",
" \n",
" infinity = solver.infinity()\n",
" \n",
" # [Variaveis]\n",
" \n",
" valComb = 5\n",
" valAlu = 50\n",
" dist = 200\n",
" k = 0.002\n",
" \n",
" #valComb = solver.Var(0.0, infinity, 'valComb')\n",
" #valAlu = solver.Var(0.0, infinity, 'valAlu')\n",
" #dist = solver.Var(0.0, infinity, 'dist')\n",
" \n",
" valConsDist = solver.IntVar(0.0, infinity, 'valConsDist')\n",
" cons = solver.IntVar(0.0, infinity, 'cons')\n",
" velo = solver.IntVar(0.0, infinity, 'velo')\n",
" valTotal = solver.IntVar(0.0, infinity, 'valTotal')\n",
" \n",
" # [Constraints]\n",
"\n",
" #model.Add(valComb = 5)\n",
" #model.Add(valAlu = 50)\n",
" #model.Add(dist = 200) \n",
" model.Add(cons = k * (velo * velo))\n",
" model.Add(valConsDist = (dist / cons) * valComb)\n",
" \n",
" # [Resolve]\n",
" \n",
" solver = cp_model.CpSolver()\n",
" solver.Minimize(valTotal = valConsDist + (valAlu * (dist / velo)))\n",
" status = solver.Solve(model)\n",
"\n",
" if status == cp_model.FEASIBLE:\n",
" print('Velocidade ideal = %i' % solver.Value(velo))\n",
" print('Custo da viagem = %i' % solver.Value(valTotal))\n",
"\n",
"custoDeViagem()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| 0016/548/16548398.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00016.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\(...TRUNCATED) | 0016/548/16548469.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00016.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\(...TRUNCATED) | 0016/548/16548630.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00016.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n (...TRUNCATED) | 0016/548/16548690.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00016.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\(...TRUNCATED) | 0016/548/16548772.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00016.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\(...TRUNCATED) | 0016/548/16548994.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00016.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\(...TRUNCATED) | 0016/549/16549083.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00016.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n (...TRUNCATED) | 0016/549/16549087.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00016.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n (...TRUNCATED) | 0016/549/16549193.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00016.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\(...TRUNCATED) | 0016/550/16550053.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00016.jsonl.gz |
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