# Estimate an upper bound to the ruler length
MAX_LENGTH = (ORDER - 1)**2
#-----------------------------------------------------------------------------
# Build the model
#-----------------------------------------------------------------------------
# Create model
mdl = CpoModel()
# Create array of variables corresponding to position rule marks
marks = mdl.integer_var_list(ORDER, 0, MAX_LENGTH, "M")
# Create marks distances that should be all different
dist = [marks[i] - marks[j] for i in range(1, ORDER) for j in range(0, i)]
mdl.add(mdl.all_diff(dist))
# Avoid symmetric solutions by ordering marks
mdl.add(marks[0] == 0)
for i in range(1, ORDER):
mdl.add(marks[i] > marks[i - 1])
# Avoid mirror solution
mdl.add((marks[1] - marks[0]) < (marks[ORDER - 1] - marks[ORDER - 2]))
# Minimize ruler size (position of the last mark)
mdl.add(mdl.minimize(marks[ORDER - 1]))
#-----------------------------------------------------------------------------
# Solve the model and display the result
#-----------------------------------------------------------------------------
from docplex.cp.model import CpoModel mdl = CpoModel(name='buses') nbbus40 = mdl.integer_var(0, 1000, name='nbBus40') nbbus30 = mdl.integer_var(0, 1000, name='nbBus30') nbbus50 = mdl.integer_var(0, 1000, name='nbBus50') mdl.add(nbbus40 * 40 + nbbus30 * 30 + 50 * nbbus50 >= 120) mdl.add(mdl.all_diff(nbbus50, nbbus40, nbbus30)) mdl.minimize(nbbus50 * 500 + nbbus40 * 400 + nbbus30 * 300) msol = mdl.solve() print(msol[nbbus40], " buses 40 seats") print(msol[nbbus30], " buses 30 seats") print(msol[nbbus50], " buses 50 seats") """ which gives 0 buses 40 seats 1 buses 30 seats 2 buses 50 seats """
from sys import stdout
# Set model parameters
NB_QUEEN = 8 # Number of queens
# Create a CPO model
model = CpoModel()
# Create column index of each queen
# Arguments: size, domain_min, domain_max, name
# That is, create NB_QUEEN integer variables that take on a value between 0 and NB_QUEEN
# The value indicates the row of that ith column with a queen
x = model.integer_var_list(NB_QUEEN, 0, NB_QUEEN - 1, "X")
# One queen per row
model.add(model.all_diff(x))
# Queens cannot be the same number of columns apart as they are rows apart
for i in range(NB_QUEEN):
for j in range(NB_QUEEN):
if i != j:
model.add(model.abs(i - j) != model.abs(x[i] - x[j]))
# Solve model
print("Solving model....")
msol = model.solve(TimeLimit=10)
# Print solution
if msol: # If the model ran successfully, it returns True
stdout.write("Solution:")
for v in x:
#-----------------------------------------------------------------------------
# Build the model
#-----------------------------------------------------------------------------
# Create CPO model
mdl = CpoModel()
# Create grid of variables
GRNG = range(SQUARE_SIZE)
grid = [[mdl.integer_var(min=0, max=SQUARE_SIZE - 1, name="C_{}_{}".format(l, c)) for l in GRNG] for c in GRNG]
# Add alldiff constraints for lines
for l in GRNG:
mdl.add(mdl.all_diff([grid[l][c] for c in GRNG]))
# Add alldiff constraints for columns
for c in GRNG:
mdl.add(mdl.all_diff([grid[l][c] for l in GRNG]))
# Add alldiff constraints for diagonals
mdl.add(mdl.all_diff([grid[l][l] for l in GRNG]))
mdl.add(mdl.all_diff([grid[l][SQUARE_SIZE - l - 1] for l in GRNG]))
# Force first line to natural sequence
for c in GRNG:
mdl.add(grid[0][c] == c)
#-----------------------------------------------------------------------------
# Create CPO model
mdl = CpoModel()
# Create grid of variables
GRNG = range(CUBE_SIZE)
grid = [[[
mdl.integer_var(min=0,
max=CUBE_SIZE - 1,
name="C_{}_{}_{}".format(x, y, z)) for x in GRNG
] for y in GRNG] for z in GRNG]
# Add constraints for each slice on direction x
for x in GRNG:
# Add alldiff constraints for lines
for l in GRNG:
mdl.add(mdl.all_diff([grid[x][l][c] for c in GRNG]))
# Add alldiff constraints for columns
for c in GRNG:
mdl.add(mdl.all_diff([grid[x][l][c] for l in GRNG]))
# Add alldiff constraints for diagonals
if CONSTRAIN_DIAGONALS:
mdl.add(mdl.all_diff([grid[x][l][l] for l in GRNG]))
mdl.add(mdl.all_diff([grid[x][l][CUBE_SIZE - l - 1] for l in GRNG]))
# Add constraints for each slice on direction y
for y in GRNG:
# Add alldiff constraints for lines
for l in GRNG:
mdl.add(mdl.all_diff([grid[l][y][c] for c in GRNG]))
#-----------------------------------------------------------------------------
# Create CPO model
mdl = CpoModel()
# Grid range
GRNG = range(9)
# Create grid of variables
grid = [[
mdl.integer_var(min=1, max=9, name="C" + str(l) + str(c)) for l in GRNG
] for c in GRNG]
# Add alldiff constraints for lines
for l in GRNG:
mdl.add(mdl.all_diff([grid[l][c] for c in GRNG]))
# Add alldiff constraints for columns
for c in GRNG:
mdl.add(mdl.all_diff([grid[l][c] for l in GRNG]))
# Add alldiff constraints for sub-squares
ssrng = range(0, 9, 3)
for sl in ssrng:
for sc in ssrng:
mdl.add(
mdl.all_diff([
grid[l][c] for l in range(sl, sl + 3)
for c in range(sc, sc + 3)
]))
# Set model parameters
NB_QUEEN = 8
#-----------------------------------------------------------------------------
# Build the model
#-----------------------------------------------------------------------------
# Create model
mdl = CpoModel()
# Create column index of each queen
x = mdl.integer_var_list(NB_QUEEN, 0, NB_QUEEN - 1, "X")
# One queen per raw
mdl.add(mdl.all_diff(x))
# One queen per diagonal xi - xj != i - j
mdl.add(mdl.all_diff(x[i] + i for i in range(NB_QUEEN)))
# One queen per diagonal xi - xj != j - i
mdl.add(mdl.all_diff(x[i] - i for i in range(NB_QUEEN)))
#-----------------------------------------------------------------------------
# Solve the model and display the result
#-----------------------------------------------------------------------------
# Solve model
print("Solving model....")
msol = mdl.solve(TimeLimit=10)
Please refer to documentation for appropriate setup of solving configuration.
"""
from docplex.cp.model import CpoModel
#-----------------------------------------------------------------------------
# Build the model
#-----------------------------------------------------------------------------
# Create model
mdl = CpoModel()
# Create model variables
V = mdl.integer_var_list(size=9, min=1, max=9, name="V")
mdl.add(mdl.all_diff(V))
# Create constraint
mdl.add(V[0] + 13 * V[1] / V[2] + V[3] + 12 * V[4] - V[5] - 11 + V[6] * V[7] / V[8] - 10 == 66)
#-----------------------------------------------------------------------------
# Solve the model and display the result
#-----------------------------------------------------------------------------
# Solve model
print("Solving model....")
msol = mdl.solve(TimeLimit=10)
print("Solution: ")
msol.print_solution()
