def solver(cost, OWA):
assert OWA in ['utilitarism', 'egalitarism', 'gini']
n_task = cost.shape[1]
n_agent = cost.shape[0]
boolean_variable = np.array([
facile.array([facile.variable([0, 1]) for i in range(n_task)])
for j in range(n_agent)
])
# Every task must be complete, by only one agent !
for j in range(n_task):
facile.constraint(sum(boolean_variable[:, j]) == 1)
how_does_it_cost = facile.array([
np.matmul(cost[i, :], facile.array(boolean_variable[i, :]))
for i in range(n_agent)
])
if OWA == 'utilitarism':
weights = np.array([1 for i in range(n_agent)])
elif OWA == 'egalitarism':
# as we only have the .sort() method for desutilities, weights must be in reverse order because the 'desutilities' should be sorted in descending order
weights = np.array([0 for i in range(n_agent)])
weights[-1] = 1
else:
# as we only have the .sort() method for desutilities, weights must be in reverse order because the 'desutilities' should be sorted in descending order
weights = np.flip(
np.array([2 * (n_agent - i) + 1 for i in range(1, n_agent + 1)]))
print(weights)
to_minimize = np.matmul(weights, how_does_it_cost.sort())
vars = []
for i in range(n_agent):
for j in range(n_task):
vars.append(boolean_variable[i, j])
res = np.array(facile.minimize(vars, to_minimize).solution)
boolean_res = res > 0
tasks = np.array([list(string.ascii_lowercase)[0:n_task]])
for i in range(n_agent):
boolean_res[i:i + n_task]
print(i + 1, ' does : ',
tasks[:, boolean_res[n_task * i:n_task * (i + 1)]][0])
def test_invalid_array() -> None:
n = 5
var_array = [[facile.variable(0, 1) for _ in range(n)] for _ in range(n)]
np_array = np.array([var_array[0], var_array[1][1:]])
msg = (
"Only numpy arrays of variables, expressions, constraints and integers "
"are accepted")
with pytest.raises(TypeError, match=msg):
_ = facile.array(np_array)
def test_basic_array() -> None:
var_list = [facile.variable(0, 1) for _ in range(5)]
array = facile.array(var_list)
x = facile.variable(range(10))
msg = "list indices must be integers or slices, not facile.core.Variable"
with pytest.raises(TypeError, match=msg):
facile.constraint(var_list[x] == 1) # type: ignore
facile.constraint(array[x] == 1)
facile.constraint(array.sum() == 1)
facile.constraint(x == 1)
solution = facile.solve([*array, x])
assert solution.solved
assert array.value() == [0, 1, 0, 0, 0]
def test_2d_array() -> None:
n = 5
# array = facile.Array.binary((n, n))
var_array = [[facile.variable(0, 1) for _ in range(n)] for _ in range(n)]
array = facile.array(np.array(var_array))
for i in range(n):
facile.constraint(array[:, i].sum() == 1)
facile.constraint(array[i, :].sum() == 1)
x, y = facile.variable(range(n)), facile.variable(range(n))
facile.constraint(array[x, y] == 1)
# TODO redundant but necessary to test one of the arguments as a variable
# facile.constraint(array[:, x].sum() == 1)
sol = facile.solve([*array])
assert sol.solved
sol = facile.solve([*array, x, y])
assert sol.solved
*_, x_, y_ = sol.solution
assert array[x_, y_].value() == 1
#
# The formulation is generalized to any number of golfers, groups and weeks.
nb_groups = 5
nb_golfers = 15
size_group = 3
assert (size_group * nb_groups == nb_golfers)
nb_weeks = 5
# An array of nb_weeks * nb_golfers decision variables to choose the group of
# every golfer every week
groups = [ facile.array(
[facile.variable(0, nb_groups-1) for i in range(nb_golfers)]
) for j in range(nb_weeks)]
# For each week, exactly size_group golfers in each group:
for i in range(nb_weeks):
# [1] Use a Sorting Constraint (redundant with [2])
s = groups[i].sort()
for j in range(nb_golfers):
facile.constraint(s[j] == j//size_group)
# [2] Use a Global Cardinality Constraint (redundant with [1])
gcc = groups[i].gcc([(size_group, i) for i in range(nb_groups)])
facile.constraint(gcc)
# Two golfers do not play in the same group more than once
n = 4
duration = [30, 10, 15, 15]
demand = [3, 1, 3, 2]
upper_limit = 160
start_times = [facile.variable(range(upper_limit)) for i in range(n)]
end_times = [facile.variable(range(2 * upper_limit)) for i in range(n)]
end_time = facile.variable(range(2 * upper_limit))
n_resources = facile.variable(range(11))
for i in range(n):
facile.constraint(end_times[i] == start_times[i] + duration[i])
facile.constraint(end_time == facile.array(end_times).max())
# detail here!
def cumulative(s, d, r, b):
tasks = [i for i in range(len(s)) if r[i] > 0 and d[i] > 0]
times_min = min([s[i].domain()[0] for i in tasks])
times_max = max([s[i].domain()[-1] + max(d) for i in tasks])
for t in range(times_min, times_max + 1):
bb = []
for i in tasks:
c1 = s[i] <= t
c2 = t < s[i] + d[i]
bb.append(c1 * c2 * r[i])
facile.constraint(sum(bb) <= b)
n = 4
duration = [30, 10, 15, 15]
demand = [3, 1, 3, 2]
upper_limit = 160
start_times = [fcl.variable(range(upper_limit)) for i in range(n)]
end_times = [fcl.variable(range(2 * upper_limit)) for i in range(n)]
end_time = fcl.variable(range(2 * upper_limit))
n_resources = fcl.variable(range(11))
for i in range(n):
fcl.constraint(end_times[i] == start_times[i] + duration[i])
fcl.constraint(end_time == fcl.array(end_times).max())
# detail here!
def cumulative(s, d, r, b):
tasks = [i for i in range(len(s)) if r[i] > 0 and d[i] > 0]
times_min = min([s[i].domain()[0] for i in tasks])
times_max = max([s[i].domain()[-1] + max(d) for i in tasks])
for t in range(times_min, times_max + 1):
bb = []
for i in tasks:
c1 = s[i] <= t
c2 = t < s[i] + d[i]
bb.append(c1 * c2 * r[i])
fcl.constraint(sum(bb) <= b)
#
# The formulation is generalized to any number of golfers, groups and weeks.
nb_groups = 5
nb_golfers = 15
size_group = 3
assert (size_group * nb_groups == nb_golfers)
nb_weeks = 5
# An array of nb_weeks * nb_golfers decision variables to choose the group of
# every golfer every week
groups = [
facile.array(
[facile.variable(0, nb_groups - 1) for i in range(nb_golfers)])
for j in range(nb_weeks)
]
# For each week, exactly size_group golfers in each group:
for i in range(nb_weeks):
# [1] Use a Sorting Constraint (redundant with [2])
s = groups[i].sort()
for j in range(nb_golfers):
facile.constraint(s[j] == j // size_group)
# [2] Use a Global Cardinality Constraint (redundant with [1])
gcc = groups[i].gcc([(size_group, i) for i in range(nb_groups)])
facile.constraint(gcc)
[1, 2, 4, 3, 5],
[3, 5, 1, 2, 4],
[5, 4, 2, 1, 3],
[1, 3, 5, 4, 2],
[4, 2, 3, 5, 1],
]
rank_men = [
[5, 1, 2, 4, 3],
[4, 1, 3, 2, 5],
[5, 3, 2, 4, 1],
[1, 5, 4, 3, 2],
[4, 3, 2, 1, 5],
]
wife = array([variable(range(n)) for i in range(n)])
husband = array([variable(range(n)) for i in range(n)])
# You are your wife's husband, and conversely
for m in range(n):
constraint(husband[wife[m]] == m)
for w in range(n):
constraint(wife[husband[w]] == w)
for m in range(n):
for w in range(n):
# m prefers this woman to his wife
c1 = rank_men[m][w] < array(rank_men[m])[wife[m]]
# w prefers her husband to this man
c2 = array(rank_women[w])[husband[w]] < rank_women[w][m]
# alias for c1 => c2
