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from pulp import *
import pandas as pd
import datetime
import random #Importações do Python
import time

item=[794, 793, 790, 786, 772, 764, 756, 743, 742, 740, 740, 736, 736, 732, 724, 724, 723, 713, 712, 709, 704, 703, 700, 699, 692, 691, 691, 678, 677, 673, 667, 662, 662, 659, 658, 646, 644, 640, 637, 635, 633, 629, 628, 625, 622, 617, 613, 612, 608, 608, 605, 602, 595, 595, 583, 580, 572, 568, 558, 541, 535, 531, 523, 521, 519, 518, 518, 518, 517, 517, 515, 512, 509, 506, 506, 504, 494, 487, 487, 485, 482, 476, 471, 469, 468, 466, 462, 459, 447, 446, 443, 436, 431, 423, 423, 419, 416, 415, 408, 407, 400, 399, 396, 395, 387, 371, 370, 370, 369, 365, 363, 360, 355, 354, 347, 346, 345, 337, 336, 334, 328, 321, 313, 312, 292, 278, 278, 276, 275, 266, 257, 254, 252, 250, 250, 249, 246, 242, 240, 233, 233, 232, 226, 223, 221, 220, 214, 213, 212, 210, 209, 208, 196, 196, 189, 185, 185, 184, 181, 179, 174, 171, 170, 168, 166, 164, 157, 147, 146, 141, 133, 123, 120, 119, 116, 116, 104, 103, 102, 99, 94, 93, 72, 66, 61, 61, 61, 59, 55, 51, 50, 45, 44, 41, 36, 21, 9, 7, 4, 1]
items=[]
for i in range(len(item)):
  x = tuple([i, item[i]])
  items.append(x)

itemCount = len(items)

# Max number of bins allowed.
maxBins = itemCount

# Bin Size
binCapacity = 1000

# Indicator variable assigned 1 when the bin is used.
y = pulp.LpVariable.dicts('BinUsed', range(maxBins),
                            lowBound = 0,
                            upBound = 1,
                            cat = LpInteger)

# An indicator variable that is assigned 1 when item is placed into binNum
possible_ItemInBin = [(itemTuple[0], binNum) for itemTuple in items
                                            for binNum in range(maxBins)]
x = pulp.LpVariable.dicts('itemInBin', possible_ItemInBin,
                            lowBound = 0,
                            upBound = 1,
                            cat = LpInteger)

# Initialize the problem
prob = LpProblem("Bin Packing Problem", LpMinimize)

# Add the objective function.
prob += lpSum([y[i] for i in range(maxBins)]), "Objective: Minimize Bins Used"

# First constraint: For every item, the sum of bins in which it appears must be 1
for j in items:
  prob += lpSum([x[(j[0], i)] for i in range(maxBins)]) == 1, ("An item can be in only 1 bin -- " + str(j[0]))

# Second constraint: For every bin, the number of items in the bin cannot exceed the bin capacity
for i in range(maxBins):
  prob += lpSum([items[j][1] * x[(items[j][0], i)] for j in range(itemCount)]) <= binCapacity*y[i], ("The sum of item sizes must be smaller than the bin -- " + str(i))

# Write the model to disk
prob.writeLP("BinPack.lp")

# Solve the optimization.
start_time = time.time()

prob.solve(use_mps=False)
print("Bins used: " + str(sum(([y[i].value() for i in range(maxBins)]))))
print("Solved in %s seconds." % (time.time() - start_time))

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