def create_model(cuts_quantity, maxl):
patterns = get_patterns(cuts_quantity, maxl)
model = LpProblem("Cubicator3000", LpMinimize)
'''
patterns is a array of vectors. i=1...n, j=1..m
cuts is a array of floats j=1..m
X_i is int variable for times the pattern is used.
'''
dict_X = {}
n = len(patterns)
m = len(cuts_quantity)
for i in range(n):
dict_X[i] = LpVariable("Pattern {} gets used".format(i), cat=LpInteger,
lowBound=0)
dict_X[i].index = i
# F.O sum over X variables
model += lpSum([dict_X[i] for i in range(n)]), "Minimize tables used"
# restrictions: the amount of every cut should be the amount demanded
for j in range(m):
temp_var = lpSum([dict_X[i] * patterns[i][j] for i in range(n)])
temp_string = "the amount of cut {} produced equals the demanded"
model += temp_var == cuts_quantity[j][1], temp_string.format(j)
return model, patterns
