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  1. {
  2. "cells": [
  3. {
  4. "cell_type": "code",
  5. "execution_count": 1,
  6. "metadata": {},
  7. "outputs": [],
  8. "source": [
  9. "from pulp import *\n",
  10. "import pandas as pd"
  11. ]
  12. },
  13. {
  14. "cell_type": "code",
  15. "execution_count": 2,
  16. "metadata": {},
  17. "outputs": [
  18. {
  19. "data": {
  20. "text/html": [
  21. "<div>\n",
  22. "<style scoped>\n",
  23. " .dataframe tbody tr th:only-of-type {\n",
  24. " vertical-align: middle;\n",
  25. " }\n",
  26. "\n",
  27. " .dataframe tbody tr th {\n",
  28. " vertical-align: top;\n",
  29. " }\n",
  30. "\n",
  31. " .dataframe thead th {\n",
  32. " text-align: right;\n",
  33. " }\n",
  34. "</style>\n",
  35. "<table border=\"1\" class=\"dataframe\">\n",
  36. " <thead>\n",
  37. " <tr style=\"text-align: right;\">\n",
  38. " <th></th>\n",
  39. " <th>dmd</th>\n",
  40. " </tr>\n",
  41. " <tr>\n",
  42. " <th>supply_region</th>\n",
  43. " <th></th>\n",
  44. " </tr>\n",
  45. " </thead>\n",
  46. " <tbody>\n",
  47. " <tr>\n",
  48. " <th>usa</th>\n",
  49. " <td>2719.6</td>\n",
  50. " </tr>\n",
  51. " <tr>\n",
  52. " <th>germany</th>\n",
  53. " <td>84.1</td>\n",
  54. " </tr>\n",
  55. " <tr>\n",
  56. " <th>japan</th>\n",
  57. " <td>1676.8</td>\n",
  58. " </tr>\n",
  59. " <tr>\n",
  60. " <th>brazil</th>\n",
  61. " <td>145.4</td>\n",
  62. " </tr>\n",
  63. " <tr>\n",
  64. " <th>india</th>\n",
  65. " <td>156.4</td>\n",
  66. " </tr>\n",
  67. " </tbody>\n",
  68. "</table>\n",
  69. "</div>"
  70. ],
  71. "text/plain": [
  72. " dmd\n",
  73. "supply_region \n",
  74. "usa 2719.6\n",
  75. "germany 84.1\n",
  76. "japan 1676.8\n",
  77. "brazil 145.4\n",
  78. "india 156.4"
  79. ]
  80. },
  81. "execution_count": 2,
  82. "metadata": {},
  83. "output_type": "execute_result"
  84. }
  85. ],
  86. "source": [
  87. "# first we import the data\n",
  88. "demand = pd.read_csv('C:/Users/jperrigan/Documents/plant_demand.csv', index_col = ['supply_region'])\n",
  89. "demand"
  90. ]
  91. },
  92. {
  93. "cell_type": "code",
  94. "execution_count": 3,
  95. "metadata": {},
  96. "outputs": [
  97. {
  98. "data": {
  99. "text/html": [
  100. "<div>\n",
  101. "<style scoped>\n",
  102. " .dataframe tbody tr th:only-of-type {\n",
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  105. "\n",
  106. " .dataframe tbody tr th {\n",
  107. " vertical-align: top;\n",
  108. " }\n",
  109. "\n",
  110. " .dataframe thead th {\n",
  111. " text-align: right;\n",
  112. " }\n",
  113. "</style>\n",
  114. "<table border=\"1\" class=\"dataframe\">\n",
  115. " <thead>\n",
  116. " <tr style=\"text-align: right;\">\n",
  117. " <th></th>\n",
  118. " <th>usa</th>\n",
  119. " <th>germany</th>\n",
  120. " <th>japan</th>\n",
  121. " <th>brazil</th>\n",
  122. " <th>india</th>\n",
  123. " </tr>\n",
  124. " <tr>\n",
  125. " <th>supply_region</th>\n",
  126. " <th></th>\n",
  127. " <th></th>\n",
  128. " <th></th>\n",
  129. " <th></th>\n",
  130. " <th></th>\n",
  131. " </tr>\n",
  132. " </thead>\n",
  133. " <tbody>\n",
  134. " <tr>\n",
  135. " <th>usa</th>\n",
  136. " <td>6</td>\n",
  137. " <td>13</td>\n",
  138. " <td>20</td>\n",
  139. " <td>12</td>\n",
  140. " <td>17</td>\n",
  141. " </tr>\n",
  142. " <tr>\n",
  143. " <th>germany</th>\n",
  144. " <td>13</td>\n",
  145. " <td>6</td>\n",
  146. " <td>14</td>\n",
  147. " <td>14</td>\n",
  148. " <td>13</td>\n",
  149. " </tr>\n",
  150. " <tr>\n",
  151. " <th>japan</th>\n",
  152. " <td>20</td>\n",
  153. " <td>14</td>\n",
  154. " <td>3</td>\n",
  155. " <td>21</td>\n",
  156. " <td>9</td>\n",
  157. " </tr>\n",
  158. " <tr>\n",
  159. " <th>brazil</th>\n",
  160. " <td>12</td>\n",
  161. " <td>14</td>\n",
  162. " <td>21</td>\n",
  163. " <td>8</td>\n",
  164. " <td>21</td>\n",
  165. " </tr>\n",
  166. " <tr>\n",
  167. " <th>india</th>\n",
  168. " <td>22</td>\n",
  169. " <td>13</td>\n",
  170. " <td>10</td>\n",
  171. " <td>23</td>\n",
  172. " <td>8</td>\n",
  173. " </tr>\n",
  174. " </tbody>\n",
  175. "</table>\n",
  176. "</div>"
  177. ],
  178. "text/plain": [
  179. " usa germany japan brazil india\n",
  180. "supply_region \n",
  181. "usa 6 13 20 12 17\n",
  182. "germany 13 6 14 14 13\n",
  183. "japan 20 14 3 21 9\n",
  184. "brazil 12 14 21 8 21\n",
  185. "india 22 13 10 23 8"
  186. ]
  187. },
  188. "execution_count": 3,
  189. "metadata": {},
  190. "output_type": "execute_result"
  191. }
  192. ],
  193. "source": [
  194. "var_cost = pd.read_csv('C:/Users/jperrigan/Documents/plant_var_cost.csv', index_col = ['supply_region'])\n",
  195. "var_cost"
  196. ]
  197. },
  198. {
  199. "cell_type": "code",
  200. "execution_count": 4,
  201. "metadata": {},
  202. "outputs": [
  203. {
  204. "data": {
  205. "text/html": [
  206. "<div>\n",
  207. "<style scoped>\n",
  208. " .dataframe tbody tr th:only-of-type {\n",
  209. " vertical-align: middle;\n",
  210. " }\n",
  211. "\n",
  212. " .dataframe tbody tr th {\n",
  213. " vertical-align: top;\n",
  214. " }\n",
  215. "\n",
  216. " .dataframe thead th {\n",
  217. " text-align: right;\n",
  218. " }\n",
  219. "</style>\n",
  220. "<table border=\"1\" class=\"dataframe\">\n",
  221. " <thead>\n",
  222. " <tr style=\"text-align: right;\">\n",
  223. " <th></th>\n",
  224. " <th>lowcap</th>\n",
  225. " <th>highcap</th>\n",
  226. " </tr>\n",
  227. " <tr>\n",
  228. " <th>supply_region</th>\n",
  229. " <th></th>\n",
  230. " <th></th>\n",
  231. " </tr>\n",
  232. " </thead>\n",
  233. " <tbody>\n",
  234. " <tr>\n",
  235. " <th>usa</th>\n",
  236. " <td>6500</td>\n",
  237. " <td>9500</td>\n",
  238. " </tr>\n",
  239. " <tr>\n",
  240. " <th>germany</th>\n",
  241. " <td>4980</td>\n",
  242. " <td>7270</td>\n",
  243. " </tr>\n",
  244. " <tr>\n",
  245. " <th>japan</th>\n",
  246. " <td>6230</td>\n",
  247. " <td>9100</td>\n",
  248. " </tr>\n",
  249. " <tr>\n",
  250. " <th>brazil</th>\n",
  251. " <td>3230</td>\n",
  252. " <td>4730</td>\n",
  253. " </tr>\n",
  254. " <tr>\n",
  255. " <th>india</th>\n",
  256. " <td>2110</td>\n",
  257. " <td>3080</td>\n",
  258. " </tr>\n",
  259. " </tbody>\n",
  260. "</table>\n",
  261. "</div>"
  262. ],
  263. "text/plain": [
  264. " lowcap highcap\n",
  265. "supply_region \n",
  266. "usa 6500 9500\n",
  267. "germany 4980 7270\n",
  268. "japan 6230 9100\n",
  269. "brazil 3230 4730\n",
  270. "india 2110 3080"
  271. ]
  272. },
  273. "execution_count": 4,
  274. "metadata": {},
  275. "output_type": "execute_result"
  276. }
  277. ],
  278. "source": [
  279. "fix_cost = pd.read_csv('C:/Users/jperrigan/Documents/plant_fix_cost.csv', index_col = ['supply_region'])\n",
  280. "fix_cost"
  281. ]
  282. },
  283. {
  284. "cell_type": "code",
  285. "execution_count": 5,
  286. "metadata": {},
  287. "outputs": [
  288. {
  289. "data": {
  290. "text/html": [
  291. "<div>\n",
  292. "<style scoped>\n",
  293. " .dataframe tbody tr th:only-of-type {\n",
  294. " vertical-align: middle;\n",
  295. " }\n",
  296. "\n",
  297. " .dataframe tbody tr th {\n",
  298. " vertical-align: top;\n",
  299. " }\n",
  300. "\n",
  301. " .dataframe thead th {\n",
  302. " text-align: right;\n",
  303. " }\n",
  304. "</style>\n",
  305. "<table border=\"1\" class=\"dataframe\">\n",
  306. " <thead>\n",
  307. " <tr style=\"text-align: right;\">\n",
  308. " <th></th>\n",
  309. " <th>lowcap</th>\n",
  310. " <th>highcap</th>\n",
  311. " </tr>\n",
  312. " <tr>\n",
  313. " <th>supply_region</th>\n",
  314. " <th></th>\n",
  315. " <th></th>\n",
  316. " </tr>\n",
  317. " </thead>\n",
  318. " <tbody>\n",
  319. " <tr>\n",
  320. " <th>usa</th>\n",
  321. " <td>500</td>\n",
  322. " <td>1500</td>\n",
  323. " </tr>\n",
  324. " <tr>\n",
  325. " <th>germany</th>\n",
  326. " <td>500</td>\n",
  327. " <td>1500</td>\n",
  328. " </tr>\n",
  329. " <tr>\n",
  330. " <th>japan</th>\n",
  331. " <td>500</td>\n",
  332. " <td>1500</td>\n",
  333. " </tr>\n",
  334. " <tr>\n",
  335. " <th>brazil</th>\n",
  336. " <td>500</td>\n",
  337. " <td>1500</td>\n",
  338. " </tr>\n",
  339. " <tr>\n",
  340. " <th>india</th>\n",
  341. " <td>500</td>\n",
  342. " <td>1500</td>\n",
  343. " </tr>\n",
  344. " </tbody>\n",
  345. "</table>\n",
  346. "</div>"
  347. ],
  348. "text/plain": [
  349. " lowcap highcap\n",
  350. "supply_region \n",
  351. "usa 500 1500\n",
  352. "germany 500 1500\n",
  353. "japan 500 1500\n",
  354. "brazil 500 1500\n",
  355. "india 500 1500"
  356. ]
  357. },
  358. "execution_count": 5,
  359. "metadata": {},
  360. "output_type": "execute_result"
  361. }
  362. ],
  363. "source": [
  364. "cap = pd.read_csv('C:/Users/jperrigan/Documents/plant_cap.csv', index_col = ['supply_region'])\n",
  365. "cap"
  366. ]
  367. },
  368. {
  369. "cell_type": "code",
  370. "execution_count": 6,
  371. "metadata": {},
  372. "outputs": [],
  373. "source": [
  374. "# define the model and initialize class of model\n",
  375. "# out goal is to minimize costs, so it's an LpMinimize problem\n",
  376. "model = LpProblem(\"Capacitated Plant Location Model\", LpMinimize)"
  377. ]
  378. },
  379. {
  380. "cell_type": "code",
  381. "execution_count": 7,
  382. "metadata": {},
  383. "outputs": [
  384. {
  385. "data": {
  386. "text/plain": [
  387. "['lowcap', 'highcap']"
  388. ]
  389. },
  390. "execution_count": 7,
  391. "metadata": {},
  392. "output_type": "execute_result"
  393. }
  394. ],
  395. "source": [
  396. "# let's create a list with our capacity options using the dataframes\n",
  397. "capacity = list(cap.columns.values)\n",
  398. "capacity"
  399. ]
  400. },
  401. {
  402. "cell_type": "code",
  403. "execution_count": 8,
  404. "metadata": {},
  405. "outputs": [
  406. {
  407. "data": {
  408. "text/plain": [
  409. "['usa', 'germany', 'japan', 'brazil', 'india']"
  410. ]
  411. },
  412. "execution_count": 8,
  413. "metadata": {},
  414. "output_type": "execute_result"
  415. }
  416. ],
  417. "source": [
  418. "location = list(cap.index.values)\n",
  419. "location"
  420. ]
  421. },
  422. {
  423. "cell_type": "code",
  424. "execution_count": 9,
  425. "metadata": {},
  426. "outputs": [
  427. {
  428. "data": {
  429. "text/plain": [
  430. "{('usa', 'usa'): production_amount__('usa',_'usa'),\n",
  431. " ('usa', 'germany'): production_amount__('usa',_'germany'),\n",
  432. " ('usa', 'japan'): production_amount__('usa',_'japan'),\n",
  433. " ('usa', 'brazil'): production_amount__('usa',_'brazil'),\n",
  434. " ('usa', 'india'): production_amount__('usa',_'india'),\n",
  435. " ('germany', 'usa'): production_amount__('germany',_'usa'),\n",
  436. " ('germany', 'germany'): production_amount__('germany',_'germany'),\n",
  437. " ('germany', 'japan'): production_amount__('germany',_'japan'),\n",
  438. " ('germany', 'brazil'): production_amount__('germany',_'brazil'),\n",
  439. " ('germany', 'india'): production_amount__('germany',_'india'),\n",
  440. " ('japan', 'usa'): production_amount__('japan',_'usa'),\n",
  441. " ('japan', 'germany'): production_amount__('japan',_'germany'),\n",
  442. " ('japan', 'japan'): production_amount__('japan',_'japan'),\n",
  443. " ('japan', 'brazil'): production_amount__('japan',_'brazil'),\n",
  444. " ('japan', 'india'): production_amount__('japan',_'india'),\n",
  445. " ('brazil', 'usa'): production_amount__('brazil',_'usa'),\n",
  446. " ('brazil', 'germany'): production_amount__('brazil',_'germany'),\n",
  447. " ('brazil', 'japan'): production_amount__('brazil',_'japan'),\n",
  448. " ('brazil', 'brazil'): production_amount__('brazil',_'brazil'),\n",
  449. " ('brazil', 'india'): production_amount__('brazil',_'india'),\n",
  450. " ('india', 'usa'): production_amount__('india',_'usa'),\n",
  451. " ('india', 'germany'): production_amount__('india',_'germany'),\n",
  452. " ('india', 'japan'): production_amount__('india',_'japan'),\n",
  453. " ('india', 'brazil'): production_amount__('india',_'brazil'),\n",
  454. " ('india', 'india'): production_amount__('india',_'india')}"
  455. ]
  456. },
  457. "execution_count": 9,
  458. "metadata": {},
  459. "output_type": "execute_result"
  460. }
  461. ],
  462. "source": [
  463. "# next we define our decision variables\n",
  464. "# what variables can we control?\n",
  465. "# we can control the quantity that we produce in each location and ship to each location\n",
  466. "production = LpVariable.dicts(\"production_amount_\", [(i, j) for i in location for j in location], lowBound = 0, cat = 'Continuous')\n",
  467. "production"
  468. ]
  469. },
  470. {
  471. "cell_type": "code",
  472. "execution_count": 10,
  473. "metadata": {},
  474. "outputs": [
  475. {
  476. "data": {
  477. "text/plain": [
  478. "{('usa', 'lowcap'): plant_status__('usa',_'lowcap'),\n",
  479. " ('germany', 'lowcap'): plant_status__('germany',_'lowcap'),\n",
  480. " ('japan', 'lowcap'): plant_status__('japan',_'lowcap'),\n",
  481. " ('brazil', 'lowcap'): plant_status__('brazil',_'lowcap'),\n",
  482. " ('india', 'lowcap'): plant_status__('india',_'lowcap'),\n",
  483. " ('usa', 'highcap'): plant_status__('usa',_'highcap'),\n",
  484. " ('germany', 'highcap'): plant_status__('germany',_'highcap'),\n",
  485. " ('japan', 'highcap'): plant_status__('japan',_'highcap'),\n",
  486. " ('brazil', 'highcap'): plant_status__('brazil',_'highcap'),\n",
  487. " ('india', 'highcap'): plant_status__('india',_'highcap')}"
  488. ]
  489. },
  490. "execution_count": 10,
  491. "metadata": {},
  492. "output_type": "execute_result"
  493. }
  494. ],
  495. "source": [
  496. "# we can choose whether to produce at a location\n",
  497. "# binary variable because the plant is either one or off\n",
  498. "status = LpVariable.dicts(\"plant_status_\", \n",
  499. " [(i,s) for s in capacity for i in location], cat='Binary')\n",
  500. "status"
  501. ]
  502. },
  503. {
  504. "cell_type": "code",
  505. "execution_count": 11,
  506. "metadata": {},
  507. "outputs": [],
  508. "source": [
  509. "# now let's create out objective function\n",
  510. "# we're trying to minimize production costs\n",
  511. "# which means we're trying to minimize the sum of fixed costs for each location (dependent on whether it's on or off)\n",
  512. "# plus the variable costs for each location (dependent on how many units produced)\n",
  513. "# the first part contains the fixed costs multiplied by the binary of off or on\n",
  514. "# the second part sums up the variable costs for each location-location tuple (produced in location A, shipped to A, etc.)\n",
  515. "model += (lpSum([fix_cost.loc[i,s] * status[(i,s)] \n",
  516. " for s in capacity for i in location])\n",
  517. " + lpSum([var_cost.loc[i,j] * production[(i,j)] \n",
  518. " for i in location for j in location]))"
  519. ]
  520. },
  521. {
  522. "cell_type": "code",
  523. "execution_count": 12,
  524. "metadata": {},
  525. "outputs": [],
  526. "source": [
  527. "# next step: constraints\n",
  528. "# the constraints are that total production equals total demand\n",
  529. "# the sum of the quantity produced by all locations (i) and shipped to location j should equal the demand for each location j\n",
  530. "# We'll need to loop over all the regions and sum the quantity arriving to that region\n",
  531. "# which should be equal to the demand from that region\n",
  532. "# the for loop basically means for each \"row\"\n",
  533. "for j in location:\n",
  534. " model += lpSum([production[(i, j)] for i in location]) == demand.loc[j,'dmd']"
  535. ]
  536. },
  537. {
  538. "cell_type": "code",
  539. "execution_count": 13,
  540. "metadata": {},
  541. "outputs": [],
  542. "source": [
  543. "# the next constraint is that total production of a particular region is less than or equal to the total production capacity\n",
  544. "# of that region\n",
  545. "# total production in a particular region <= total production capacity of that region, \n",
  546. "# which depends on whether that plant is on (binary variable)\n",
  547. "for i in location:\n",
  548. " model += lpSum([production[(i, j)] for j in location]) <= lpSum([cap.loc[i,s] * status[i,s] \n",
  549. " for s in capacity])"
  550. ]
  551. },
  552. {
  553. "cell_type": "code",
  554. "execution_count": 14,
  555. "metadata": {},
  556. "outputs": [],
  557. "source": [
  558. "# now let's add a logical constraint\n",
  559. "# the first is that if a high capacity plant in the USA is open, then the low capacity plant in germany is also opened\n",
  560. "# this is a binary variable, so if y(usa, high) = 1 then y(germany, low) also equals one\n",
  561. "# so y(usa, high) - y(germany, low) must equal zero, or be less than or equal to zero\n",
  562. "#model += status['usa', 'highcap'] - status['germany', 'lowcap'] <= 0"
  563. ]
  564. },
  565. {
  566. "cell_type": "code",
  567. "execution_count": 15,
  568. "metadata": {},
  569. "outputs": [
  570. {
  571. "name": "stdout",
  572. "output_type": "stream",
  573. "text": [
  574. "Status: Optimal\n"
  575. ]
  576. }
  577. ],
  578. "source": [
  579. "model.solve()\n",
  580. "print(\"Status:\", LpStatus[model.status])"
  581. ]
  582. },
  583. {
  584. "cell_type": "code",
  585. "execution_count": 16,
  586. "metadata": {},
  587. "outputs": [
  588. {
  589. "name": "stdout",
  590. "output_type": "stream",
  591. "text": [
  592. "plant_status__('brazil',_'highcap') = 1.0\n",
  593. "plant_status__('brazil',_'lowcap') = 0.0\n",
  594. "plant_status__('germany',_'highcap') = 0.0\n",
  595. "plant_status__('germany',_'lowcap') = 0.0\n",
  596. "plant_status__('india',_'highcap') = 0.0\n",
  597. "plant_status__('india',_'lowcap') = 1.0\n",
  598. "plant_status__('japan',_'highcap') = 1.0\n",
  599. "plant_status__('japan',_'lowcap') = 0.0\n",
  600. "plant_status__('usa',_'highcap') = 1.0\n",
  601. "plant_status__('usa',_'lowcap') = 0.0\n",
  602. "production_amount__('brazil',_'brazil') = 145.4\n",
  603. "production_amount__('brazil',_'germany') = 0.0\n",
  604. "production_amount__('brazil',_'india') = 0.0\n",
  605. "production_amount__('brazil',_'japan') = 0.0\n",
  606. "production_amount__('brazil',_'usa') = 1219.6\n",
  607. "production_amount__('germany',_'brazil') = 0.0\n",
  608. "production_amount__('germany',_'germany') = 0.0\n",
  609. "production_amount__('germany',_'india') = 0.0\n",
  610. "production_amount__('germany',_'japan') = 0.0\n",
  611. "production_amount__('germany',_'usa') = 0.0\n",
  612. "production_amount__('india',_'brazil') = 0.0\n",
  613. "production_amount__('india',_'germany') = 84.1\n",
  614. "production_amount__('india',_'india') = 156.4\n",
  615. "production_amount__('india',_'japan') = 176.8\n",
  616. "production_amount__('india',_'usa') = 0.0\n",
  617. "production_amount__('japan',_'brazil') = 0.0\n",
  618. "production_amount__('japan',_'germany') = 0.0\n",
  619. "production_amount__('japan',_'india') = 0.0\n",
  620. "production_amount__('japan',_'japan') = 1500.0\n",
  621. "production_amount__('japan',_'usa') = 0.0\n",
  622. "production_amount__('usa',_'brazil') = 0.0\n",
  623. "production_amount__('usa',_'germany') = 0.0\n",
  624. "production_amount__('usa',_'india') = 0.0\n",
  625. "production_amount__('usa',_'japan') = 0.0\n",
  626. "production_amount__('usa',_'usa') = 1500.0\n"
  627. ]
  628. }
  629. ],
  630. "source": [
  631. "for v in model.variables():\n",
  632. " print(v.name, \"=\", v.varValue)"
  633. ]
  634. },
  635. {
  636. "cell_type": "code",
  637. "execution_count": 17,
  638. "metadata": {},
  639. "outputs": [
  640. {
  641. "name": "stdout",
  642. "output_type": "stream",
  643. "text": [
  644. "The total cost is $ 58850.899999999994\n"
  645. ]
  646. }
  647. ],
  648. "source": [
  649. "print(\"The total cost is $\",model.objective.value())"
  650. ]
  651. },
  652. {
  653. "cell_type": "code",
  654. "execution_count": 18,
  655. "metadata": {},
  656. "outputs": [],
  657. "source": [
  658. "# prints all the model specifications to a text file\n",
  659. "model.writeLP('C:/Users/jperrigan/Documents/solution.lp')"
  660. ]
  661. },
  662. {
  663. "cell_type": "code",
  664. "execution_count": 20,
  665. "metadata": {},
  666. "outputs": [
  667. {
  668. "data": {
  669. "text/html": [
  670. "<div>\n",
  671. "<style scoped>\n",
  672. " .dataframe tbody tr th:only-of-type {\n",
  673. " vertical-align: middle;\n",
  674. " }\n",
  675. "\n",
  676. " .dataframe tbody tr th {\n",
  677. " vertical-align: top;\n",
  678. " }\n",
  679. "\n",
  680. " .dataframe thead th {\n",
  681. " text-align: right;\n",
  682. " }\n",
  683. "</style>\n",
  684. "<table border=\"1\" class=\"dataframe\">\n",
  685. " <thead>\n",
  686. " <tr style=\"text-align: right;\">\n",
  687. " <th></th>\n",
  688. " <th>prod</th>\n",
  689. " <th>quant</th>\n",
  690. " </tr>\n",
  691. " </thead>\n",
  692. " <tbody>\n",
  693. " <tr>\n",
  694. " <th>0</th>\n",
  695. " <td>usa to usa</td>\n",
  696. " <td>1500.0</td>\n",
  697. " </tr>\n",
  698. " <tr>\n",
  699. " <th>1</th>\n",
  700. " <td>usa to germany</td>\n",
  701. " <td>0.0</td>\n",
  702. " </tr>\n",
  703. " <tr>\n",
  704. " <th>2</th>\n",
  705. " <td>usa to japan</td>\n",
  706. " <td>0.0</td>\n",
  707. " </tr>\n",
  708. " <tr>\n",
  709. " <th>3</th>\n",
  710. " <td>usa to brazil</td>\n",
  711. " <td>0.0</td>\n",
  712. " </tr>\n",
  713. " <tr>\n",
  714. " <th>4</th>\n",
  715. " <td>usa to india</td>\n",
  716. " <td>0.0</td>\n",
  717. " </tr>\n",
  718. " <tr>\n",
  719. " <th>5</th>\n",
  720. " <td>germany to usa</td>\n",
  721. " <td>0.0</td>\n",
  722. " </tr>\n",
  723. " <tr>\n",
  724. " <th>6</th>\n",
  725. " <td>germany to germany</td>\n",
  726. " <td>0.0</td>\n",
  727. " </tr>\n",
  728. " <tr>\n",
  729. " <th>7</th>\n",
  730. " <td>germany to japan</td>\n",
  731. " <td>0.0</td>\n",
  732. " </tr>\n",
  733. " <tr>\n",
  734. " <th>8</th>\n",
  735. " <td>germany to brazil</td>\n",
  736. " <td>0.0</td>\n",
  737. " </tr>\n",
  738. " <tr>\n",
  739. " <th>9</th>\n",
  740. " <td>germany to india</td>\n",
  741. " <td>0.0</td>\n",
  742. " </tr>\n",
  743. " <tr>\n",
  744. " <th>10</th>\n",
  745. " <td>japan to usa</td>\n",
  746. " <td>0.0</td>\n",
  747. " </tr>\n",
  748. " <tr>\n",
  749. " <th>11</th>\n",
  750. " <td>japan to germany</td>\n",
  751. " <td>0.0</td>\n",
  752. " </tr>\n",
  753. " <tr>\n",
  754. " <th>12</th>\n",
  755. " <td>japan to japan</td>\n",
  756. " <td>1500.0</td>\n",
  757. " </tr>\n",
  758. " <tr>\n",
  759. " <th>13</th>\n",
  760. " <td>japan to brazil</td>\n",
  761. " <td>0.0</td>\n",
  762. " </tr>\n",
  763. " <tr>\n",
  764. " <th>14</th>\n",
  765. " <td>japan to india</td>\n",
  766. " <td>0.0</td>\n",
  767. " </tr>\n",
  768. " <tr>\n",
  769. " <th>15</th>\n",
  770. " <td>brazil to usa</td>\n",
  771. " <td>1219.6</td>\n",
  772. " </tr>\n",
  773. " <tr>\n",
  774. " <th>16</th>\n",
  775. " <td>brazil to germany</td>\n",
  776. " <td>0.0</td>\n",
  777. " </tr>\n",
  778. " <tr>\n",
  779. " <th>17</th>\n",
  780. " <td>brazil to japan</td>\n",
  781. " <td>0.0</td>\n",
  782. " </tr>\n",
  783. " <tr>\n",
  784. " <th>18</th>\n",
  785. " <td>brazil to brazil</td>\n",
  786. " <td>145.4</td>\n",
  787. " </tr>\n",
  788. " <tr>\n",
  789. " <th>19</th>\n",
  790. " <td>brazil to india</td>\n",
  791. " <td>0.0</td>\n",
  792. " </tr>\n",
  793. " <tr>\n",
  794. " <th>20</th>\n",
  795. " <td>india to usa</td>\n",
  796. " <td>0.0</td>\n",
  797. " </tr>\n",
  798. " <tr>\n",
  799. " <th>21</th>\n",
  800. " <td>india to germany</td>\n",
  801. " <td>84.1</td>\n",
  802. " </tr>\n",
  803. " <tr>\n",
  804. " <th>22</th>\n",
  805. " <td>india to japan</td>\n",
  806. " <td>176.8</td>\n",
  807. " </tr>\n",
  808. " <tr>\n",
  809. " <th>23</th>\n",
  810. " <td>india to brazil</td>\n",
  811. " <td>0.0</td>\n",
  812. " </tr>\n",
  813. " <tr>\n",
  814. " <th>24</th>\n",
  815. " <td>india to india</td>\n",
  816. " <td>156.4</td>\n",
  817. " </tr>\n",
  818. " </tbody>\n",
  819. "</table>\n",
  820. "</div>"
  821. ],
  822. "text/plain": [
  823. " prod quant\n",
  824. "0 usa to usa 1500.0\n",
  825. "1 usa to germany 0.0\n",
  826. "2 usa to japan 0.0\n",
  827. "3 usa to brazil 0.0\n",
  828. "4 usa to india 0.0\n",
  829. "5 germany to usa 0.0\n",
  830. "6 germany to germany 0.0\n",
  831. "7 germany to japan 0.0\n",
  832. "8 germany to brazil 0.0\n",
  833. "9 germany to india 0.0\n",
  834. "10 japan to usa 0.0\n",
  835. "11 japan to germany 0.0\n",
  836. "12 japan to japan 1500.0\n",
  837. "13 japan to brazil 0.0\n",
  838. "14 japan to india 0.0\n",
  839. "15 brazil to usa 1219.6\n",
  840. "16 brazil to germany 0.0\n",
  841. "17 brazil to japan 0.0\n",
  842. "18 brazil to brazil 145.4\n",
  843. "19 brazil to india 0.0\n",
  844. "20 india to usa 0.0\n",
  845. "21 india to germany 84.1\n",
  846. "22 india to japan 176.8\n",
  847. "23 india to brazil 0.0\n",
  848. "24 india to india 156.4"
  849. ]
  850. },
  851. "execution_count": 20,
  852. "metadata": {},
  853. "output_type": "execute_result"
  854. }
  855. ],
  856. "source": [
  857. "prod = [{'prod': \"{} to {}\".format(i, j), 'quant':production[(i, j)].varValue} for i in location for j in location]\n",
  858. "prod = pd.DataFrame(prod)\n",
  859. "prod"
  860. ]
  861. },
  862. {
  863. "cell_type": "code",
  864. "execution_count": 21,
  865. "metadata": {},
  866. "outputs": [
  867. {
  868. "data": {
  869. "text/html": [
  870. "<div>\n",
  871. "<style scoped>\n",
  872. " .dataframe tbody tr th:only-of-type {\n",
  873. " vertical-align: middle;\n",
  874. " }\n",
  875. "\n",
  876. " .dataframe tbody tr th {\n",
  877. " vertical-align: top;\n",
  878. " }\n",
  879. "\n",
  880. " .dataframe thead th {\n",
  881. " text-align: right;\n",
  882. " }\n",
  883. "</style>\n",
  884. "<table border=\"1\" class=\"dataframe\">\n",
  885. " <thead>\n",
  886. " <tr style=\"text-align: right;\">\n",
  887. " <th></th>\n",
  888. " <th>highcap</th>\n",
  889. " <th>lowcap</th>\n",
  890. " </tr>\n",
  891. " <tr>\n",
  892. " <th>location</th>\n",
  893. " <th></th>\n",
  894. " <th></th>\n",
  895. " </tr>\n",
  896. " </thead>\n",
  897. " <tbody>\n",
  898. " <tr>\n",
  899. " <th>usa</th>\n",
  900. " <td>1.0</td>\n",
  901. " <td>0.0</td>\n",
  902. " </tr>\n",
  903. " <tr>\n",
  904. " <th>germany</th>\n",
  905. " <td>0.0</td>\n",
  906. " <td>0.0</td>\n",
  907. " </tr>\n",
  908. " <tr>\n",
  909. " <th>japan</th>\n",
  910. " <td>1.0</td>\n",
  911. " <td>0.0</td>\n",
  912. " </tr>\n",
  913. " <tr>\n",
  914. " <th>brazil</th>\n",
  915. " <td>1.0</td>\n",
  916. " <td>0.0</td>\n",
  917. " </tr>\n",
  918. " <tr>\n",
  919. " <th>india</th>\n",
  920. " <td>0.0</td>\n",
  921. " <td>1.0</td>\n",
  922. " </tr>\n",
  923. " </tbody>\n",
  924. "</table>\n",
  925. "</div>"
  926. ],
  927. "text/plain": [
  928. " highcap lowcap\n",
  929. "location \n",
  930. "usa 1.0 0.0\n",
  931. "germany 0.0 0.0\n",
  932. "japan 1.0 0.0\n",
  933. "brazil 1.0 0.0\n",
  934. "india 0.0 1.0"
  935. ]
  936. },
  937. "execution_count": 21,
  938. "metadata": {},
  939. "output_type": "execute_result"
  940. }
  941. ],
  942. "source": [
  943. "stat = [{'location':i, 'lowcap':status[(i, capacity[0])].varValue, 'highcap':status[(i, capacity[1])].varValue} \n",
  944. " for i in location]\n",
  945. "stat = pd.DataFrame(stat).set_index('location')\n",
  946. "stat"
  947. ]
  948. },
  949. {
  950. "cell_type": "code",
  951. "execution_count": 22,
  952. "metadata": {},
  953. "outputs": [
  954. {
  955. "data": {
  956. "text/plain": [
  957. "[[('_C1',\n",
  958. " 1*production_amount__('brazil',_'usa') + 1*production_amount__('germany',_'usa') + 1*production_amount__('india',_'usa') + 1*production_amount__('japan',_'usa') + 1*production_amount__('usa',_'usa') + -2719.6 = 0)],\n",
  959. " [('_C2',\n",
  960. " 1*production_amount__('brazil',_'germany') + 1*production_amount__('germany',_'germany') + 1*production_amount__('india',_'germany') + 1*production_amount__('japan',_'germany') + 1*production_amount__('usa',_'germany') + -84.1 = 0)],\n",
  961. " [('_C3',\n",
  962. " 1*production_amount__('brazil',_'japan') + 1*production_amount__('germany',_'japan') + 1*production_amount__('india',_'japan') + 1*production_amount__('japan',_'japan') + 1*production_amount__('usa',_'japan') + -1676.8 = 0)],\n",
  963. " [('_C4',\n",
  964. " 1*production_amount__('brazil',_'brazil') + 1*production_amount__('germany',_'brazil') + 1*production_amount__('india',_'brazil') + 1*production_amount__('japan',_'brazil') + 1*production_amount__('usa',_'brazil') + -145.4 = 0)],\n",
  965. " [('_C5',\n",
  966. " 1*production_amount__('brazil',_'india') + 1*production_amount__('germany',_'india') + 1*production_amount__('india',_'india') + 1*production_amount__('japan',_'india') + 1*production_amount__('usa',_'india') + -156.4 = 0)],\n",
  967. " [('_C6',\n",
  968. " -1500*plant_status__('usa',_'highcap') + -500*plant_status__('usa',_'lowcap') + 1*production_amount__('usa',_'brazil') + 1*production_amount__('usa',_'germany') + 1*production_amount__('usa',_'india') + 1*production_amount__('usa',_'japan') + 1*production_amount__('usa',_'usa') + 0 <= 0)],\n",
  969. " [('_C7',\n",
  970. " -1500*plant_status__('germany',_'highcap') + -500*plant_status__('germany',_'lowcap') + 1*production_amount__('germany',_'brazil') + 1*production_amount__('germany',_'germany') + 1*production_amount__('germany',_'india') + 1*production_amount__('germany',_'japan') + 1*production_amount__('germany',_'usa') + 0 <= 0)],\n",
  971. " [('_C8',\n",
  972. " -1500*plant_status__('japan',_'highcap') + -500*plant_status__('japan',_'lowcap') + 1*production_amount__('japan',_'brazil') + 1*production_amount__('japan',_'germany') + 1*production_amount__('japan',_'india') + 1*production_amount__('japan',_'japan') + 1*production_amount__('japan',_'usa') + 0 <= 0)],\n",
  973. " [('_C9',\n",
  974. " -1500*plant_status__('brazil',_'highcap') + -500*plant_status__('brazil',_'lowcap') + 1*production_amount__('brazil',_'brazil') + 1*production_amount__('brazil',_'germany') + 1*production_amount__('brazil',_'india') + 1*production_amount__('brazil',_'japan') + 1*production_amount__('brazil',_'usa') + 0 <= 0)],\n",
  975. " [('_C10',\n",
  976. " -1500*plant_status__('india',_'highcap') + -500*plant_status__('india',_'lowcap') + 1*production_amount__('india',_'brazil') + 1*production_amount__('india',_'germany') + 1*production_amount__('india',_'india') + 1*production_amount__('india',_'japan') + 1*production_amount__('india',_'usa') + 0 <= 0)]]"
  977. ]
  978. },
  979. "execution_count": 22,
  980. "metadata": {},
  981. "output_type": "execute_result"
  982. }
  983. ],
  984. "source": [
  985. "conList = []\n",
  986. "for value in model.constraints.items():\n",
  987. " temp = [value]\n",
  988. " conList.append(temp)\n",
  989. "#model.constraints.items()\n",
  990. "conList"
  991. ]
  992. },
  993. {
  994. "cell_type": "code",
  995. "execution_count": 23,
  996. "metadata": {},
  997. "outputs": [
  998. {
  999. "data": {
  1000. "text/html": [
  1001. "<div>\n",
  1002. "<style scoped>\n",
  1003. " .dataframe tbody tr th:only-of-type {\n",
  1004. " vertical-align: middle;\n",
  1005. " }\n",
  1006. "\n",
  1007. " .dataframe tbody tr th {\n",
  1008. " vertical-align: top;\n",
  1009. " }\n",
  1010. "\n",
  1011. " .dataframe thead th {\n",
  1012. " text-align: right;\n",
  1013. " }\n",
  1014. "</style>\n",
  1015. "<table border=\"1\" class=\"dataframe\">\n",
  1016. " <thead>\n",
  1017. " <tr style=\"text-align: right;\">\n",
  1018. " <th></th>\n",
  1019. " <th>name</th>\n",
  1020. " <th>shadow price</th>\n",
  1021. " <th>slack</th>\n",
  1022. " </tr>\n",
  1023. " </thead>\n",
  1024. " <tbody>\n",
  1025. " <tr>\n",
  1026. " <th>0</th>\n",
  1027. " <td>_C1</td>\n",
  1028. " <td>12.0</td>\n",
  1029. " <td>-0.0</td>\n",
  1030. " </tr>\n",
  1031. " <tr>\n",
  1032. " <th>1</th>\n",
  1033. " <td>_C2</td>\n",
  1034. " <td>13.0</td>\n",
  1035. " <td>-0.0</td>\n",
  1036. " </tr>\n",
  1037. " <tr>\n",
  1038. " <th>2</th>\n",
  1039. " <td>_C3</td>\n",
  1040. " <td>10.0</td>\n",
  1041. " <td>-0.0</td>\n",
  1042. " </tr>\n",
  1043. " <tr>\n",
  1044. " <th>3</th>\n",
  1045. " <td>_C4</td>\n",
  1046. " <td>8.0</td>\n",
  1047. " <td>-0.0</td>\n",
  1048. " </tr>\n",
  1049. " <tr>\n",
  1050. " <th>4</th>\n",
  1051. " <td>_C5</td>\n",
  1052. " <td>8.0</td>\n",
  1053. " <td>-0.0</td>\n",
  1054. " </tr>\n",
  1055. " <tr>\n",
  1056. " <th>5</th>\n",
  1057. " <td>_C6</td>\n",
  1058. " <td>-6.0</td>\n",
  1059. " <td>-0.0</td>\n",
  1060. " </tr>\n",
  1061. " <tr>\n",
  1062. " <th>6</th>\n",
  1063. " <td>_C7</td>\n",
  1064. " <td>-7.0</td>\n",
  1065. " <td>-0.0</td>\n",
  1066. " </tr>\n",
  1067. " <tr>\n",
  1068. " <th>7</th>\n",
  1069. " <td>_C8</td>\n",
  1070. " <td>-7.0</td>\n",
  1071. " <td>-0.0</td>\n",
  1072. " </tr>\n",
  1073. " <tr>\n",
  1074. " <th>8</th>\n",
  1075. " <td>_C9</td>\n",
  1076. " <td>0.0</td>\n",
  1077. " <td>135.0</td>\n",
  1078. " </tr>\n",
  1079. " <tr>\n",
  1080. " <th>9</th>\n",
  1081. " <td>_C10</td>\n",
  1082. " <td>0.0</td>\n",
  1083. " <td>82.7</td>\n",
  1084. " </tr>\n",
  1085. " </tbody>\n",
  1086. "</table>\n",
  1087. "</div>"
  1088. ],
  1089. "text/plain": [
  1090. " name shadow price slack\n",
  1091. "0 _C1 12.0 -0.0\n",
  1092. "1 _C2 13.0 -0.0\n",
  1093. "2 _C3 10.0 -0.0\n",
  1094. "3 _C4 8.0 -0.0\n",
  1095. "4 _C5 8.0 -0.0\n",
  1096. "5 _C6 -6.0 -0.0\n",
  1097. "6 _C7 -7.0 -0.0\n",
  1098. "7 _C8 -7.0 -0.0\n",
  1099. "8 _C9 0.0 135.0\n",
  1100. "9 _C10 0.0 82.7"
  1101. ]
  1102. },
  1103. "execution_count": 23,
  1104. "metadata": {},
  1105. "output_type": "execute_result"
  1106. }
  1107. ],
  1108. "source": [
  1109. "# print shadow price and slack\n",
  1110. "sp = [{'name':name, 'shadow price':c.pi, 'slack':c.slack}\n",
  1111. " for name, c in model.constraints.items()]\n",
  1112. "sp = pd.DataFrame(sp)\n",
  1113. "sp"
  1114. ]
  1115. }
  1116. ],
  1117. "metadata": {
  1118. "kernelspec": {
  1119. "display_name": "Python 3",
  1120. "language": "python",
  1121. "name": "python3"
  1122. },
  1123. "language_info": {
  1124. "codemirror_mode": {
  1125. "name": "ipython",
  1126. "version": 3
  1127. },
  1128. "file_extension": ".py",
  1129. "mimetype": "text/x-python",
  1130. "name": "python",
  1131. "nbconvert_exporter": "python",
  1132. "pygments_lexer": "ipython3",
  1133. "version": "3.7.3"
  1134. }
  1135. },
  1136. "nbformat": 4,
  1137. "nbformat_minor": 2
  1138. }
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