def fix_mapping_variables_according_to_integral_solution(self, solution):
if not isinstance(solution, solutions.IntegralScenarioSolution):
msg = "Expected solutions.IntegralScenarioSolution instance, received {} of type {}".format(solution, type(solution))
raise TypeError(msg)
if solution.scenario is not self.scenario:
msg = "This method requires that the solution is based on the same scenario as the Modelcreator."
raise ClassicMCFError(msg)
for req in self.requests:
mapping = solution.request_mapping[req]
self._fix_embedding_variable(req, mapping)
if not mapping.is_embedded:
continue
for i in req.nodes:
for u in req.get_allowed_nodes(i):
fix_i_u_mapping_constraint = LinExpr([(1.0, self.var_y[req][i][u])])
name = "{req}_fix_{i}_{u}".format(req=req.name, i=i, u=u)
if u == mapping.get_mapping_of_node(i):
self.model.addConstr(fix_i_u_mapping_constraint, GRB.EQUAL, 1.0, name=name)
else:
self.model.addConstr(fix_i_u_mapping_constraint, GRB.EQUAL, 0.0, name=name)
for ij in req.edges:
i, j = ij
for uv in self.substrate.edges:
u, v = uv
fix_ij_uv_mapping_constraint = LinExpr([(1.0, self.var_z[req][ij][uv])])
name = "{}_fix_{}_{}__{}_{}".format(req.name, i, j, u, v)
if uv in mapping.mapping_edges[ij]:
self.model.addConstr(fix_ij_uv_mapping_constraint, GRB.EQUAL, 1.0, name=name)
else:
self.model.addConstr(fix_ij_uv_mapping_constraint, GRB.EQUAL, 0.0, name=name)
def add_node_variables(self):
n = self.G.vcount()
for i in range(n):
for j in range(self.k):
self.x[i, j] = self.model.addVar(vtype=GRB.CONTINUOUS)
if self.x_coefs:
for ((i, c), coef) in self.x_coefs.items():
self.x[i, c].obj = coef
self.model.update()
for i in range(n):
total_assign = LinExpr()
for j in range(self.k):
total_assign.addTerms(1.0, self.x[i, j])
self.model.addConstr(total_assign == 1.0)
for e in self.G.es():
u = min(e.source, e.target)
v = max(e.source, e.target)
for i in range(self.k):
self.model.addConstr(self.y[u, v] >= self.x[u, i] + self.x[v, i] - 1.0)
self.model.addConstr(self.x[u, i] >= self.x[v, i] + self.y[u, v] - 1.0)
self.model.addConstr(self.x[v, i] >= self.x[u, i] + self.y[u, v] - 1.0)
self.model.update()
def constraints_list_of_tuples(model, mylist, sign="="):
term_0 = mylist[0]
ROWS, COLUMNS = term_0[0].shape[0], term_0[1].shape[1]
for row in range(ROWS):
for column in range(COLUMNS):
expr = LinExpr()
for term in mylist:
q, qp = term[0].shape[1], term[1].shape[0]
if q != qp:
raise ValueError(term, "q=%d qp=%d" % (q, qp))
if type(term[1][0, column]) == type(model.addVar()):
expr.add(
LinExpr([(term[0][row, k], term[1][k, column])
for k in range(q)]))
elif type(term[0][row, 0]) == type(model.addVar()):
expr.add(
LinExpr([(term[1][k, column], term[0][row, k])
for k in range(q)]))
else:
expr.addConstant(
sum([
term[1][k, column] * term[0][row, k]
for k in range(q)
]))
if sign == "<":
model.addConstr(expr <= 0)
elif sign == "=":
model.addConstr(expr == 0)
elif sign == ">=":
model.addConstr(expr >= 0)
else:
raise "sign indefinite"
def create_objective(self):
# the original objective of the LP which produced the decomposable fractional solutions
objective = LinExpr()
for req in self.var_embedding_variable:
for fractional_mapping in self.var_embedding_variable[req]:
objective.addTerms(req.profit, self.var_embedding_variable[req][fractional_mapping])
self.model.setObjective(objective, GRB.MAXIMIZE)
def funcion_objetivo(model, D, C, T, F, cf_mod, cck_mod, pc_mod):
ganancias = LinExpr()
for k in range(CTDAD_TIPOS_CLAMSHELLS):
for i in range(4):
for d in range(8):
ganancias += D[k][i][d] * PC_LIST[pc_mod][k][i][d]
costo_clamshells = LinExpr()
costo_fruta = LinExpr()
for k in range(CTDAD_TIPOS_CLAMSHELLS):
for i in range(4):
for t in range(2):
for d in range(28):
costo_clamshells += C[k][i][t][d] * CCK_LIST[cck_mod][k]
costo_fruta += C[k][i][t][d] * CCD[d]
costo_trabajadores = LinExpr()
for n in range(54):
for m in range(2):
for t in range(2):
for d in range(28):
costo_trabajadores += T[n][m][t][d] * CT[m]
costo_frigorifico = LinExpr()
for d in range(28):
costo_frigorifico += F[d] * CF[cf_mod]
fo = ganancias - (costo_clamshells + costo_fruta + costo_trabajadores +
costo_frigorifico)
model.setObjective(fo, GRB.MAXIMIZE)
def acceptable(model, C):
id_r = 1
acc = ['a', 'b', 'c', 'd']
acc_values = [[0.1, 0.25, 0.45, 0.05], [0.2, 0.4, 0.6, 0.15]]
for t in range(CTDAD_TURNOS):
for d in range(CTDAD_DIAS):
restriccion_lr = LinExpr()
for k in range(CTDAD_TIPOS_CLAMSHELLS):
for i in range(CTDAD_MERCADOS):
restriccion_lr += C[k][i][t][d]
for x in range(4):
restriccion_m = LinExpr()
for k in range(CTDAD_TIPOS_CLAMSHELLS):
restriccion_m += C[k][x][t][d]
model.addConstr(
acc_values[0][x] * restriccion_lr <= restriccion_m,
name=f"Acceptabilidad_{acc[x]}{id_r}")
id_r += 1
model.addConstr(
restriccion_m <= acc_values[1][x] * restriccion_lr,
name=f"Acceptabilidad_{acc[x]}{id_r}")
id_r += 1
def min_trabajadores(model, T, C, qt_mod):
id_r_1 = 1
id_r_2 = 1
trab_1 = 'a'
trab_2 = 'b'
#"""
for m in range(CTDAD_TIPOS_TRABAJADORES):
for d in range(CTDAD_DIAS):
for t in range(CTDAD_TURNOS):
#"""
if not ((d + 1) % 7 == 0):
restriccion_1 = LinExpr()
for n in range(CTDAD_TRABAJADORES):
restriccion_1 += T[n][m][t][d]
model.addConstr(restriccion_1 >= QT_LIST[qt_mod][m],
name=f'Min_trabajadores_{trab_1}_{id_r_1}')
id_r_1 += 1
#"""
#'''
restriccion_2_left = LinExpr()
for n in range(CTDAD_TRABAJADORES):
restriccion_2_left += T[n][m][t][d]
restriccion_2_right = LinExpr()
for k in range(CTDAD_TIPOS_CLAMSHELLS):
for i in range(CTDAD_MERCADOS):
restriccion_2_right += C[k][i][t][d] * FM[m]
model.addConstr(restriccion_2_left >= restriccion_2_right,
name=f'Min_trabajadores_{trab_2}_{id_r_2}')
id_r_2 += 1
def build_lp(g, feasible_sol):
from gurobipy import LinExpr, GRB, Model #, setParam
# We introduce an integer index per node ID. Sometimes we will use this
# index, and sometimes the node ID; this makes the code a bit messy.
i_nodeid = {i: n for i, n in enumerate(g)}
n_nodes = len(i_nodeid)
model = Model()
# The lower half of the n^2 binary variables, the rest: y_{j,i} = 1-y_{i,j}
y = {(i, j): model.addVar(vtype=GRB.BINARY)
for i, j in combinations(irange(n_nodes), 2)}
model.update()
# The triangle inequalities
for i, j, k in combinations(irange(n_nodes), 3):
lhs = LinExpr([(1, y[(i, j)]), (1, y[(j, k)]), (-1, y[(i, k)])])
model.addConstr(lhs, GRB.LESS_EQUAL, 1.0)
lhs = LinExpr([(-1, y[(i, j)]), (-1, y[(j, k)]), (1, y[(i, k)])])
model.addConstr(lhs, GRB.LESS_EQUAL, 0.0)
# The objective
shift = 0
c = [[0] * n_nodes for _ in irange(n_nodes)]
indices = ((i, j) for i, j in product(irange(n_nodes), repeat=2)
if g.has_edge(i_nodeid[j], i_nodeid[i]))
for j, k in indices:
w = g[i_nodeid[k]][i_nodeid[j]]['weight']
if k < j:
c[k][j] += w
elif k > j:
c[j][k] -= w
shift += w
obj = [(c[i][j], y[(i, j)]) for i, j in combinations(irange(n_nodes), 2)]
model.setObjective(LinExpr(obj), GRB.MINIMIZE)
model.setAttr('ObjCon', shift)
set_start_point(g, model, y, i_nodeid, feasible_sol)
model.update()
return model, y
def solve_lp_knapsack_gurobi(scores, costs, budget):
from gurobipy import Model, LinExpr, GRB
n = len(scores)
# Create a new model.
m = Model("lp_knapsack")
# Create variables.
for i in range(n):
m.addVar(lb=0.0, ub=1.0)
m.update()
vars = m.getVars()
# Set objective.
obj = LinExpr()
for i in range(n):
obj += scores[i] * vars[i]
m.setObjective(obj, GRB.MAXIMIZE)
# Add constraint.
expr = LinExpr()
for i in range(n):
expr += costs[i] * vars[i]
m.addConstr(expr, GRB.LESS_EQUAL, budget)
# Optimize.
m.optimize()
assert m.status == GRB.OPTIMAL
x = np.zeros(n)
for i in range(n):
x[i] = vars[i].x
return x
def create_objective(self):
objective = LinExpr()
for req in self.var_embedding_variable:
for fractional_mapping in self.var_embedding_variable[req]:
objective.addTerms(
req.profit,
self.var_embedding_variable[req][fractional_mapping])
self.model.setObjective(objective, GRB.MAXIMIZE)
def plugin_constraint_embed_all_requests(self):
for req in self.requests:
expr = LinExpr()
expr.addTerms(1.0, self.var_embedding_decision[req])
constr_name = construct_name("embed_all_requests", req_name=req.name)
self.model.addConstr(expr, GRB.EQUAL, 1.0, name=constr_name)
def polytopic_trajectory_given_modes(x0,list_of_cells,goal,eps=0,order=1):
"""
Description:
Polytopic Trajectory Optimization with the ordered list of polytopes given
This is a convex program as mode sequence is already given
list_of_cells: each cell has the following attributes: A,B,c, and polytope(H,h)
"""
model=Model("Fixed Mode Polytopic Trajectory")
T=len(list_of_cells)
n,m=list_of_cells[0].B.shape
q=int(order*n)
x=model.addVars(range(T+1),range(n),lb=-GRB.INFINITY,ub=GRB.INFINITY,name="x")
u=model.addVars(range(T),range(m),lb=-GRB.INFINITY,ub=GRB.INFINITY,name="u")
G=model.addVars(range(T+1),range(n),range(q),lb=-GRB.INFINITY,ub=GRB.INFINITY,name="G")
theta=model.addVars(range(T),range(m),range(q),lb=-GRB.INFINITY,ub=GRB.INFINITY,name="theta")
model.update()
for j in range(n):
model.addConstr(x[0,j]<=x0[j,0]+eps)
model.addConstr(x[0,j]>=x0[j,0]-eps)
for t in range(T):
print "adding constraints of t",t
cell=list_of_cells[t]
A,B,c,p=cell.A,cell.B,cell.c,cell.p
for j in range(n):
expr_x=LinExpr([(A[j,k],x[t,k]) for k in range(n)])
expr_u=LinExpr([(B[j,k],u[t,k]) for k in range(m)])
model.addConstr(x[t+1,j]==expr_x+expr_u+c[j,0])
for i in range(n):
for j in range(q):
expr_x=LinExpr([(A[i,k],G[t,k,j]) for k in range(n)])
expr_u=LinExpr([(B[i,k],theta[t,k,j]) for k in range(m)])
model.addConstr(G[t+1,i,j]==expr_x+expr_u)
x_t=np.array([x[t,j] for j in range(n)]).reshape(n,1)
u_t=np.array([u[t,j] for j in range(m)]).reshape(m,1)
G_t=np.array([G[t,i,j] for i in range(n) for j in range(q)]).reshape(n,q)
theta_t=np.array([theta[t,i,j] for i in range(m) for j in range(q)]).reshape(m,q)
GT=sp.linalg.block_diag(G_t,theta_t)
xu=np.vstack((x_t,u_t))
subset_LP(model,xu,GT,Ball(2*q),p)
x_T=np.array([x[T,j] for j in range(n)]).reshape(n,1)
G_T=np.array([G[T,i,j] for i in range(n) for j in range(q)]).reshape(n,q)
z=zonotope(x_T,G_T)
subset_zonotopes(model,z,goal)
J=LinExpr([(1/(t+1.0),G[t,i,i]) for t in range(T+1) for i in range(n)])
model.setObjective(J)
model.write("sadra.lp")
model.setParam('TimeLimit', 150)
model.optimize()
x_num,G_num={},{}
for t in range(T+1):
x_num[t]=np.array([[x[t,j].X] for j in range(n)]).reshape(n,1)
G_num[t]=np.array([[G[t,i,j].X] for i in range(n) for j in range(q)]).reshape(n,q)
return (x_num,G_num)
def constraints_Ab_smaller_c(model, A, b, c):
delta = {}
for row in range(A.shape[0]):
lhs = LinExpr()
delta[row] = model.addVar(lb=0, obj=1)
model.update()
for k in range(A.shape[1]):
lhs.add(A[row, k] * b[k, 0])
model.addConstr(lhs <= c[row, 0] + delta[row])
def re_verification_tree_extension(s, x, G, i, state_end, factor=0.99):
"""
This is a method to deal with numerical inaccuracies in high dimensioanl MILPs that are terminated early.
Requires solving a linear program. If feasible, the computed control startegy is recomputed.
If infeasible, report back, raise a flag, and abort the tree extention
Inputs: state_X, state_end
Output: flag, u, theta
"""
model = Model("verifying tree extention")
G_dynamic = np.empty((s.n, s.n), dtype='object')
theta = np.empty((s.m, s.n), dtype='object')
u = np.empty((s.m, 1), dtype='object')
x_bar = np.empty((s.n, 1), dtype='object')
for row in range(s.n):
for column in range(s.n):
G_dynamic[row, column] = model.addVar(lb=-GRB.INFINITY,
ub=GRB.INFINITY)
for row in range(s.m):
u[row, 0] = model.addVar(lb=-GRB.INFINITY, ub=GRB.INFINITY)
for column in range(s.n):
theta[row, column] = model.addVar(lb=-GRB.INFINITY,
ub=GRB.INFINITY)
for row in range(s.n):
x_bar[row, 0] = model.addVar(lb=-GRB.INFINITY, ub=GRB.INFINITY)
model.update()
for row in range(s.n):
for column in range(s.n):
AG = LinExpr()
for k in range(s.n):
AG.add(s.A[i][row, k] * G[k, column])
for k in range(s.m):
AG.add(s.B[i][row, k] * theta[k, column])
model.addConstr(G_dynamic[row, column] == AG * factor)
for row in range(s.n):
Bu = LinExpr()
for k in range(s.m):
Bu.add(s.B[i][row, k] * u[k, 0])
model.addConstr(x_bar[row, 0] == np.dot(s.A[i], x)[row, 0] + Bu +
s.c[i][row, 0])
eps = subset_eps(model, G_dynamic, s.Pi, state_end.polytope.H,
state_end.polytope.h, x_bar)
subset(model, theta, s.Pi, s.F[i], s.f[i], u)
model.setParam("OutputFlag", False)
model.optimize()
if model.Status == 2:
print("eps=", valuation(eps).T)
if np.amax(valuation(eps)) > 10**-3:
print(
"\n-" * 10, "Too large of Epsilon: %d Tree extention failed!" %
np.amax(valuation(eps)), "\n-" * 10)
return None
else:
print("Tree extention verified succesuflly!")
return (valuation(u), valuation(theta), valuation(eps))
else:
print("\n-" * 10, "Infeasible! Tree extention failed!", "\n-" * 10)
return None
def _init_LP(self):
if self._lp_inited:
return
logging.debug('Init LP')
self.lp = LPModel('estep')
self.lp.setAttr("modelSense", 1) # minimzation
self.alpha = {}
beta2 = {}
beta3 = {}
# instantiate vars
logging.debug('Init LP - create vars')
for a in self.author_graph:
self.alpha[a] = {}
for p in self.parts:
self.alpha[a][p] = self.lp.addVar(lb=0.0)
for a, b in self.author_graph.edges():
beta2[(a, b)] = self.lp.addVar()
beta3[(a, b)] = {}
for p in self.parts:
beta3[(a, b)][p] = self.lp.addVar(lb=0.0)
# integrate added variables into the model
self.lp.update()
# add constraints once during this init
# alphas are indicator vars
logging.debug('Init LP - indiv constraints')
ones_arr = [1.0] * len(self.parts)
for a in self.author_graph:
self.lp.addConstr(LinExpr(ones_arr, self.alpha[a].values()),
GRB.EQUAL, 1.0)
# beta2 is the sum of beta3s
logging.debug('Init LP - pair constraints')
pt_five_array = [0.5] * len(self.parts)
for a, b in self.author_graph.edges():
self.lp.addConstr(LinExpr(pt_five_array, beta3[(a, b)].values()),
GRB.EQUAL, beta2[(a, b)])
for p in self.parts:
self.lp.addConstr(
LinExpr([1.0, -1.0], [self.alpha[a][p], self.alpha[b][p]]),
GRB.LESS_EQUAL, beta3[(a, b)][p])
self.lp.addConstr(
LinExpr([-1.0, 1.0], [self.alpha[a][p], self.alpha[b][p]]),
GRB.LESS_EQUAL, beta3[(a, b)][p])
self.lp.update()
# calculate pairwise potentials part of the objective
# the optimization is to minimize negated log-likelihood = maximize the log-likelihood
logging.debug('Obj func - pair potentials')
s = np.log(1 - self.TAU) - np.log(self.TAU)
lpcoeffs, lpvars = [], []
for a, b in self.author_graph.edges():
lpcoeffs.append(-self.author_graph[a][b]['weight'] * s)
lpvars.append(beta2[(a, b)])
self.objF_pair = LinExpr(list(lpcoeffs), list(lpvars))
self._lp_inited = True
logging.debug('Init LP Done')
def check_redundancy_row(H, h, ROW, atol=10**-8):
model = Model("Row Redundancy Check")
n = H.shape[1]
x = np.empty((n, 1), dtype='object')
for row in range(n):
x[row, 0] = model.addVar(lb=-GRB.INFINITY, ub=GRB.INFINITY)
model.update()
for row in [r for r in range(H.shape[0]) if r != ROW]:
Hx = LinExpr()
for column in range(n):
Hx.add(H[row, column] * x[column, 0])
model.addConstr(Hx <= h[row, 0])
J = LinExpr()
for column in range(n):
J.add(H[ROW, column] * x[column, 0])
model.setObjective(J, GRB.MAXIMIZE)
model.setParam('OutputFlag', False)
model.optimize()
if model.Status == 2:
if J.getValue() > h[ROW, 0] + atol:
return False # It is NOT redundant
else:
return True # It is redudant
else:
return False
def create_constraints_flow_preservation_and_induction(self):
for req in self.requests:
for (i, j) in req.edges:
for u in self.substrate.nodes:
right_expr = LinExpr()
if u in self.var_y[req][i]:
right_expr.addTerms(1.0, self.var_y[req][i][u])
if u in self.var_y[req][j]:
right_expr.addTerms(-1.0, self.var_y[req][j][u])
ij_mapping_vars = self.var_z[req][(i, j)]
left_outgoing = LinExpr([
(1.0, ij_mapping_vars[sedge])
for sedge in self.substrate.out_edges[u]
])
left_incoming = LinExpr([
(1.0, ij_mapping_vars[sedge])
for sedge in self.substrate.in_edges[u]
])
left_expr = LinExpr(left_outgoing - left_incoming)
constr_name = modelcreator.construct_name(
"flow_pres", req_name=req.name, vedge=(i, j), snode=u
) # Matthias: changed to conform to standard naming
self.model.addConstr(left_expr,
GRB.EQUAL,
right_expr,
name=constr_name)
def break_symmetry(self):
if self.verbosity > 0:
print("Adding symmetry breaking constraints")
if not self.x:
self.add_node_variables()
for i in range(self.k - 1):
sym = LinExpr()
for j in range(i + 1):
sym.addTerms(1.0, self.x[i, j])
self.model.addConstr(sym == 1)
self.model.update()
def _build_objective_linear(adj, variables, eigval, eigvec):
""" Special case. Build linear objective function for QP. Used if batch size = 1 """
obj = LinExpr()
eigvec_sq = np.square(eigvec)
n = adj.shape[0]
for i in range(n):
if eigvec_sq[i] != 0:
obj.addTerms(2 * eigval * eigvec_sq[i], variables[i])
return obj
def callback(model, where):
if where == GRB.Callback.MIPSOL:
sols = model.cbGetSolution([vars[e] for e in edges])
c_edges = [edges[i] for i in range(len(edges)) if sols[i] > 0.5]
cycles = cycles_from_edges(c_edges)
for cycle in cycles:
len_cycle = len(cycle)
if len_cycle > k:
cycle_vars = [vars[(cycle[i], cycle[(i+1) % len_cycle])] for i in range(len_cycle)]
ones = [1.0]*len(cycle_vars)
expr = LinExpr()
expr.addTerms(ones, cycle_vars)
model.cbLazy(expr <= len_cycle - 1)
def point_trajectory_given_modes_and_central_traj(x_traj,list_of_cells,goal,eps=0,scale=[]):
"""
Description:
Point Trajectory Optimization with the ordered list of polytopes given
This is a convex program as mode sequence is already given
list_of_cells: each cell has the following attributes: A,B,c, and polytope(H,h)
"""
model=Model("Fixed Mode Polytopic Trajectory")
T=len(list_of_cells)
n,m=list_of_cells[0].B.shape
x=model.addVars(range(T+1),range(n),lb=-GRB.INFINITY,ub=GRB.INFINITY,name="x")
u=model.addVars(range(T),range(m),lb=-GRB.INFINITY,ub=GRB.INFINITY,name="u")
model.update()
if len(scale)==0:
scale=np.ones(x_traj[0].shape[0])
print "inside function epsilon is",eps
for j in range(n):
model.addConstr(x[0,j]<=x_traj[0][j]+eps*scale[j])
model.addConstr(x[0,j]>=x_traj[0][j]-eps*scale[j])
for t in range(T):
cell=list_of_cells[t]
A,B,c,_p=cell.A,cell.B,cell.c,cell.p
for j in range(n):
expr_x=LinExpr([(A[j,k],x[t,k]) for k in range(n)])
expr_u=LinExpr([(B[j,k],u[t,k]) for k in range(m)])
model.addConstr(x[t+1,j]==expr_x+expr_u+c[j,0])
x_t=np.array([x[t,j] for j in range(n)]).reshape(n,1)
u_t=np.array([u[t,j] for j in range(m)]).reshape(m,1)
xu=np.vstack((x_t,u_t))
subset_LP(model,xu,np.zeros((n+m,1)),Ball(1),_p)
x_T=np.array([x[T,j] for j in range(n)]).reshape(n,1)
z=zonotope(x_T,np.zeros((n,1)))
subset_generic(model,z,goal)
# Cost function
J=QuadExpr()
for t in range(T):
for i in range(n):
J.add(x[t,i]*x[t,i]-x[t,i]*x_traj[t][i])
model.setObjective(J)
model.write("polytopic_trajectory.lp")
model.setParam('TimeLimit', 150)
model.optimize()
x_num,u_num={},{}
for t in range(T+1):
x_num[t]=np.array([[x[t,j].X] for j in range(n)])
for t in range(T):
u_num[t]=np.array([[u[t,i].X] for i in range(m) ])
return (x_num,u_num)
def plugin_obj_maximize_task_contingency(self):
print(" ..creating objective to maximize Task Contingency")
expr = LinExpr()
for day in coming_days(self.next_day):
for hour in hours_real(day):
for tutor in Data().tutor_by_name.keys():
for task in TASKS:
if self.past_plan[tutor][task][day][hour] != "":
expr.addTerms(
1.0,
self.schedule_entry[tutor][day][hour][task])
print(" ..final steps")
self.model.setObjective(expr, GRB.MAXIMIZE)
def construct_lp_model(c, A, d):
from gurobipy import Model, LinExpr, GRB
# A = numpy.array (matrix)
# c = numpy.array (vector)
# d = numpy.array (vector)
k, n = A.shape
# Creation of the gurobi model
m = Model("sp")
# Variables
x = list()
for i in range(n):
x.append(m.addVar(lb=-GRB.INFINITY, ub=GRB.INFINITY, name='x_%d' % i))
# Objective Function
objExpr = LinExpr()
for i in range(n):
objExpr.add(x[i], c[i])
m.setObjective(objExpr, GRB.MAXIMIZE)
# Constraints
expr = {}
for j in range(k):
expr = LinExpr()
for i in range(n):
expr.add(x[i], A[j, i])
m.addConstr(expr, GRB.LESS_EQUAL, d[j])
# Update the model to add new entries
m.update()
return m
def add_node_variables(self):
if not self.z:
self.add_z_variables()
n = self.G.vcount()
for i in range(n):
for j in range(self.k2 * self.k):
self.x[i, j] = self.model.addVar(vtype=GRB.CONTINUOUS)
self.model.update()
for i in range(n):
total_assign = LinExpr()
for j in range(self.k2 * self.k):
total_assign.addTerms(1.0, self.x[i, j])
self.model.addConstr(total_assign == 1.0)
for e in self.G.es():
u = min(e.source, e.target)
v = max(e.source, e.target)
for c in range(self.k):
mod_k_clashes = LinExpr()
for j in range(self.k2):
mod_k_clashes.addTerms(1.0, self.x[u, c + j * self.k])
mod_k_clashes.addTerms(1.0, self.x[v, c + j * self.k])
self.model.addConstr(self.y[u, v] >= mod_k_clashes - 1.0)
for e in self.G.es():
u = min(e.source, e.target)
v = max(e.source, e.target)
for c in range(self.k2 * self.k):
self.model.addConstr(self.z[u, v] >= self.x[u, c] + self.x[v, c] - 1.0)
self.model.addConstr(self.x[u, c] >= self.x[v, c] + self.z[u, v] - 1.0)
self.model.addConstr(self.x[v, c] >= self.x[u, c] + self.z[u, v] - 1.0)
def create_constraints_compute_edge_load(self):
# track edge loads
for req in self.requests:
for sedge in self.substrate.substrate_edge_resources:
expr_terms = []
for vedge in req.edges:
vedge_demand = req.get_edge_demand(vedge)
expr_terms.append(LinExpr(vedge_demand, self.var_z[req][vedge][sedge]))
constr_name = modelcreator.construct_name("compute_request_edge_load", req_name=req.name,
sedge=sedge)
expr_terms.append(LinExpr(-1.0, self.var_request_load[req][sedge]))
expr = gurobipy.quicksum(expr_terms)
self.model.addConstr(expr, GRB.EQUAL, 0.0, name=constr_name)
def propagate(x, layers, grb_model, complete):
n_outs = len(x[-1])
for layer_idx, layer in enumerate(layers):
x[layer_idx] = []
if isinstance(layer, nn.Linear):
for i in range(layer.out_features):
expr = LinExpr()
expr += layer.bias[i]
expr += LinExpr(
layer.weight[i].detach().cpu().numpy().tolist(),
x[layer_idx - 1])
grb_model.update()
grb_model.setObjective(expr, GRB.MINIMIZE)
grb_model.optimize()
lb = grb_model.objVal - EPS
grb_model.setObjective(expr, GRB.MAXIMIZE)
grb_model.optimize()
ub = grb_model.objVal + EPS
x[layer_idx] += [
grb_model.addVar(lb=lb,
ub=ub,
vtype=GRB.CONTINUOUS,
name='x_{}_{}'.format(layer_idx, i))
]
grb_model.addConstr(expr, GRB.EQUAL, x[layer_idx][i])
n_outs = layer.out_features
elif isinstance(layer, nn.ReLU):
for i in range(n_outs):
in_lb, in_ub = x[layer_idx - 1][i].lb, x[layer_idx - 1][i].ub
x[layer_idx] += [
grb_model.addVar(in_lb, in_ub, vtype=GRB.CONTINUOUS)
]
add_relu_constraints(grb_model, in_lb, in_ub,
x[layer_idx - 1][i], x[layer_idx][i],
complete)
else:
assert False
grb_model.update()
return x, n_outs
def run_one_compare(num_variables, num_equations, constraint_coefficent_sample=constraint_coefficent_sample,
obj_coefficent_sample=obj_coefficent_sample, constraint_scalar_sample=constraint_scalar_sample):
seed(0)
gmodel = Model("test")
variables = [lp_variable(i, gmodel) for i in range(1, num_variables + 1)]
equations_str = []
for i in range(num_equations):
cur_equation = []
gurobi_eq = LinExpr()
for v in variables:
c = round(constraint_coefficent_sample(), 3)
cur_equation.append("{}{}".format(c, str(v)))
gurobi_eq += c * v.get_gurobi_var()
b = round(constraint_scalar_sample(), 3)
equations_str.append("{}<={}".format(','.join(cur_equation), b))
gmodel.addConstr(gurobi_eq <= b, '{}'.format(i))
objective_str = ''
gurobi_obj = LinExpr()
for v in variables:
c = round(obj_coefficent_sample(), 3)
objective_str += "{}{},".format(c, v)
gurobi_obj += c * v.get_gurobi_var()
# Remove the last ,
objective_str = objective_str[:-1]
gmodel.setObjective(gurobi_obj, GRB.MAXIMIZE)
gmodel.optimize()
our_objective, our_assignment = solve_our(equations_str, objective_str)
if gmodel.status == GRB.INF_OR_UNBD:
gmodel.setParam('DualReductions', 0)
gmodel.optimize()
print('UNBOUNDED' if gmodel.status == GRB.UNBOUNDED else 'INFEASIBLE')
if our_objective == np.inf:
assert gmodel.status == GRB.UNBOUNDED
elif our_objective is None:
assert gmodel.status == GRB.INFEASIBLE
else:
# IF the solution is not feasible the status would be GRB.INFEASIBLE
assert gmodel.status == GRB.OPTIMAL
gurobi_objective = gmodel.objVal
print(gurobi_objective)
for v in variables:
np.testing.assert_almost_equal(our_assignment.get(v.index, 0), v.gurobi_var.x)
return np.abs(gurobi_objective - our_objective) <= 10 ** -3
def _fix_embedding_variable(self, req, mapping):
force_embedding_constraint = LinExpr([(1.0, self.var_embedding_decision[req])])
name = modelcreator.construct_name("force_embedding", req_name=req.name)
if mapping.is_embedded:
self.model.addConstr(force_embedding_constraint, GRB.EQUAL, 1.0, name=name)
else:
self.model.addConstr(force_embedding_constraint, GRB.EQUAL, 0.0, name=name)
def add_disjunction_rhs(self, gmodel, conds: List[LinExpr], lhs: Var, greater: bool, cond_name: str):
'''
Add all the conditions in conds as a disjunction (using binary variables)
:param gmodel: model to add condition to
:param conds: list of rhs expressions
:param lhs: lhs condition
:param greater: if True them lhs >= rhs else lhs <= rhs
:param cond_name: name to add in gurobi
:return:
'''
deltas = []
cond_delta = LinExpr()
for cond in conds:
deltas.append(gmodel.addVar(vtype=GRB.BINARY))
cond_delta += deltas[-1]
if greater:
gmodel.addConstr(lhs - 2 * SMALL >= cond - (LARGE * deltas[-1]), name=cond_name)
else:
# lhs_f, _ = self.get_relu_constraint(gmodel, lhs, -1, -1, False)
# gmodel.addConstr(lhs_f <= cond + (LARGE * deltas[-1]), name=cond_name)
gmodel.addConstr(lhs + 2 * SMALL <= cond + (LARGE * deltas[-1]), name=cond_name)
# gmodel.addConstr(deltas[-1] <= 0)
gmodel.addConstr(cond_delta <= len(deltas) - 1, name="{}_deltas".format(cond_name))
def e_step(self):
logging.debug('E-Step')
if not self._lp_inited:
self._init_LP()
logging.debug('Obj func - indiv potentials')
# individual potentials
lpcoeffs, lpvars = [], []
for a in self.author_graph:
for p in self.parts:
lpcoeffs.append(-self.log_phi(a, p))
lpvars.append(self.alpha[a][p])
objF_indiv = LinExpr(lpcoeffs, lpvars)
self.lp.setObjective(objF_indiv + self.objF_pair)
# solve the LP
logging.debug('Solving the LP')
self.lp.optimize()
logging.debug('Solving the LP Done')
# hard partitions for nodes (authors)
for a in self.author_graph:
self.partition[a] = np.argmax(
[self.alpha[a][p].X for p in self.parts])
logging.debug('E-Step Done')
def subtourelim(mdl,where):
if where == GRB.callback.MIPSOL:
active_arcs = []
solutions = mdl.cbGetSolution(mdl._x)
for i, j in mdl._A:
for k in mdl._K:
if round(solutions[i, j, k]) == 1:
active_arcs.append([i, j, k])
active_arcs = np.vstack(active_arcs)
tours = mdl._subtour(mdl._K, active_arcs)
# add lazy constraints
for k in tours.keys():
if len(tours[k]) > 1:
for tour in tours[k]:
S = np.unique(tour)
expr = LinExpr()
for i in S:
if i != mdl._V[-1]:
for j in S:
if j != i and j != mdl._V[0]:
expr += mdl._x[i, j, k]
# expr = quicksum(mdl._x[i, j, k] for i in S for j in S if j != i if i != mdl._V[-1] or j != mdl._V[0])
mdl.cbLazy(expr <= len(S) - 1)
def two_cycle(A, C, gap):
"""
Solve high-vertex dense graphs by reduction to
weighted matching ILP.
"""
_ = '*'
m = Model()
m.modelsense = GRB.MAXIMIZE
m.params.mipgap = gap
m.params.timelimit = 60 * 60
n = A.shape[0]
vars = {}
edges = tuplelist()
# model as undirected graph
for i in range(n):
for j in range(i+1, n):
if A[i, j] == 1 and A[j, i] == 1:
e = (i, j)
edges.append(e)
w_i = 2 if i in C else 1
w_j = 2 if j in C else 1
w = w_i + w_j
var = m.addVar(vtype=GRB.BINARY, obj=w)
vars[e] = var
m.update()
# 2 cycle constraint <=> undirected flow <= 1
for i in range(n):
lhs = LinExpr()
lhs_vars = [vars[e] for e in chain(edges.select(i, _), edges.select(_, i))]
ones = [1.0]*len(lhs_vars)
lhs.addTerms(ones, lhs_vars)
m.addConstr(lhs <= 1)
m.optimize()
m.update()
cycles = [list(e) for e in edges if vars[e].x == 1.0]
return cycles, m.objval
def check_feasability_ILP(exams_to_schedule, period, data, verbose=False):
# More precise but by far to slow compared to heuristic
r = data['r']
T = data['T']
s = data['s']
z = {}
model = Model("RoomFeasability")
# z[i,k] = if exam i is written in room k
for k in range(r):
# print k, period
if T[k][period] == 1:
for i in exams_to_schedule:
z[i, k] = model.addVar(vtype=GRB.BINARY, name="z_%s_%s" % (i, k))
model.update()
# Building constraints...
# c1: seats for all students
for i in exams_to_schedule:
expr = LinExpr()
for k in range(r):
if T[k][period] == 1:
expr.addTerms(1, z[i, k])
model.addConstr(expr >= s[i], "c1")
# c2: only one exam per room
for k in range(r):
if T[k][period] == 1:
expr = LinExpr()
for i in exams_to_schedule:
expr.addTerms(1, z[i, k])
model.addConstr(expr <= 1, "c2")
model.setObjective(0, GRB.MINIMIZE)
if not verbose:
model.params.OutputFlag = 0
model.params.heuristics = 0
model.params.PrePasses = 1
model.optimize()
# return best room schedule
try:
return model.objval
except GurobiError:
logging.warning('check_feasability_ILP: model has no objVal')
return None
def _sensitivity_analysis_for_tardiness(z, CT, D):
m = Model("model_for_sensitivity_analysis_for_tardiness")
m.setParam('OutputFlag', False)
# m.params.IntFeasTol = 1e-7
DT = {}
TD = {}
for j in range(D.project_n):
## Project Tadeness,construction completion time
DT[j] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="DT_%d" % j)
TD[j] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="TD_%d" % j)
DT[-1] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="DT_-1")
m.update();
#### Add Constraint ####
## Constrain 2: project complete data>due data ##
# equation 17
for j in range(D.project_n):
m.addConstr(DT[j] - TD[j], GRB.LESS_EQUAL, D.DD[j], name="constraint_2_project_%d" % j)
## Constraint 13
# equation 12
for j in range(D.project_n):
m.addConstr(DT[j], GRB.GREATER_EQUAL, CT[j] + D.review_duration[j],
name="constraint_13_project_%d" % j)
## Constraint 14
# equation 13
for i in range(-1, D.project_n):
for j in range(D.project_n):
if i != j:
m.addConstr(DT[j], GRB.GREATER_EQUAL, DT[i] - D.M * (1 - z[i, j]) + D.review_duration[j],
name="constraint_14_project_%d_project_%d" % (i, j))
m.update()
# Set optimization objective - minimize sum of
expr = LinExpr()
for j in range(D.project_n):
expr.add(D.w[j] * TD[j])
m.setObjective(expr, GRB.MINIMIZE)
m.update()
# m.params.presolve = 1
m.update()
m.optimize()
# _logger.info("mm binding info:")
sj = {}
for c in m.getConstrs():
if c.ConstrName.startswith('constraint_13'):
j = int(c.ConstrName.split('_')[-1])
# if c.Pi != 0:
# sj[j] = 1
# _logger.info('%s binding Pi:%.4g' % (c.ConstrName, c.Pi))
# pass
# else:
# sj[j] = 0
# _logger.info('%s not binding Pi:%.4g' % (c.ConstrName, c.Pi))
# pass
sj[j] = c.Pi
return sj
def cycle_milp(A, C, k, gap):
n = A.shape[0]
t_0 = time.clock()
_ = '*'
m = Model()
m.modelsense = GRB.MAXIMIZE
m.params.mipgap = gap
cycles = []
vars = []
cycles_grouped = [[] for i in range(n)]
vars_grouped = [[] for i in range(n)]
print('[%.1f] Generating variables...' % (time.clock() - t_0))
print('i = ', end='')
for i in range(n):
for cycle in dfs_cycles(i, A, k):
w = sum([2 if j in C else 1 for j in cycle])
var = m.addVar(vtype=GRB.BINARY, obj=w)
vars.append(var)
cycles.append(cycle)
cycles_grouped[i].append(cycle)
vars_grouped[i].append(var)
for j in cycle:
if j > i:
vars_grouped[j].append(var)
cycles_grouped[j].append(cycle)
if (i + 1) % 10 == 0:
print(i + 1)
m.update()
print('[%.1f] Generated variables...' % (time.clock() - t_0))
print('[%.1f] Generating constraints...' % (time.clock() - t_0))
for i in range(n):
vars_i = vars_grouped[i]
lhs = LinExpr()
ones = [1.0]*len(vars_i)
lhs.addTerms(ones, vars_i)
m.addConstr(lhs <= 1.0)
print('[%.1f] Generated constraints...' % (time.clock() - t_0))
print('[%.1f] Begin Optimizing %d vertex %d cycle model' % (time.clock() - t_0, n, len(cycles)))
m.update()
m.optimize()
m.update()
print('[%.1f] Finished Optimizing' % (time.clock() - t_0))
print('[%.1f] Building cycles...' % (time.clock() - t_0))
final_cycles = []
for i in range(len(vars)):
var = vars[i]
if var.x == 1.0:
cycle = cycles[i]
final_cycles.append(cycle)
print('[%.1f] Finished building cycles' % (time.clock() - t_0))
return final_cycles, m.objval
def construct_model(self):
self.m = Model('mip1.log')
# Create model variables
self.solution_matrix = {(st, c, se): self.m.addVar(vtype=GRB.BINARY, name='{}_{}_{}'.format(st, c, se))
for st in self.student_ids for c in self.course_ids for se in self.semester_ids}
self.m.update()
# Constraint #1: Class availability and size
for course in self.course_ids:
for semester in self.semester_ids:
class_size_expr = LinExpr()
for student in self.student_ids:
class_size_expr.add(self.solution_matrix[student, course, semester])
if not self.is_course_offered(course, semester):
self.m.addConstr(class_size_expr, GRB.EQUAL, 0, 'max_size_{}_{}'.format(course, semester))
else:
self.m.addConstr(class_size_expr, GRB.LESS_EQUAL, 300, 'max_size_{}_{}'.format(course, semester))
# Constraint #2: No classes with pre-requisites in first semester
for student in self.student_ids:
for prereq in self.dependencies:
first_semester_prereq_expr = LinExpr()
first_semester_prereq_expr.add(self.solution_matrix[student, prereq[1], 1])
self.m.addConstr(first_semester_prereq_expr, GRB.EQUAL, 0, 'fs_prereq_{}_{}'.format(student, prereq))
# Constraint #3: Max course load per student per semester
for student in self.student_ids:
for semester in self.semester_ids:
max_load_expr = LinExpr()
for course in self.course_ids:
max_load_expr.add(self.solution_matrix[student, course, semester])
self.m.addConstr(max_load_expr, GRB.LESS_EQUAL, 2, 'max_load_{}_{}'.format(student, semester))
# Constraint #4: Course demand for student
for student in self.student_ids:
for course in self.course_ids:
if any([self.student_demand[student, course, semester] for semester in self.semester_ids]):
demand_expr = LinExpr()
for semester in self.semester_ids:
demand_expr.add(self.solution_matrix[student, course, semester])
self.m.addConstr(demand_expr, GRB.EQUAL, 1, 'student_demand_{}_{}'.format(student, course))
# Constraint #5: Prerequisite courses
for student in self.student_ids:
for prereq in self.dependencies:
prereq_expr = LinExpr()
for semester in sorted(self.semester_ids)[:-1]:
prereq_expr.add(self.solution_matrix[student, prereq[0], semester], 1.0)
prereq_expr.add(self.solution_matrix[student, prereq[1], semester + 1], -1.0)
self.m.addConstr(prereq_expr, GRB.GREATER_EQUAL, 0, 'prereq_{}_{}_{}'.format(student, prereq[0], prereq[1]))
# Create the objective
objective_expr = LinExpr()
for student in self.student_ids:
for course in self.course_ids:
for semester in self.semester_ids:
if self.student_demand[student, course, semester]:
objective_expr.add(1 - self.solution_matrix[student, course, semester])
self.m.setObjective(objective_expr, GRB.MINIMIZE)
def build_model(data, n_cliques = 0, verbose = True):
# Load Data Format
n = data['n']
r = data['r']
p = data['p']
s = data['s']
c = data['c']
h = data['h']
w = data['w']
location = data['location']
conflicts = data['conflicts']
locking_times = data['locking_times']
T = data['T']
similarp = data['similarp']
model = Model("ExaminationScheduling")
if verbose:
print("Building variables...")
# x[i,k,l] = 1 if exam i is at time l in room k
x = {}
for k in range(r):
for l in range(p):
if T[k][l] == 1:
for i in range(n):
if location[k] in w[i]:
x[i,k,l] = model.addVar(vtype=GRB.BINARY, name="x_%s_%s_%s" % (i,k,l))
# y[i,l] = 1 if exam i is at time l
y = {}
for i in range(n):
for l in range(p):
y[i, l] = model.addVar(vtype=GRB.BINARY, name="y_%s_%s" % (i,l))
# integrate new variables
model.update()
# for i in range(p+5):
# for l in range(i-5):
# y[i, l].setAttr("BranchPriority", s[i])
# model.update()
start = timeit.default_timer()
# not very readable but same constraints as in GurbiLinear_v_10: speeded up model building by 2 for small problems (~400 exams) and more for huger problem ~1500 exams
if verbose:
print("Building constraints...")
s_sorted = sorted(range(len(c)), key = lambda k: c[k])
obj = LinExpr()
sumconflicts = {}
maxrooms = {}
for i in range(n):
sumconflicts[i] = sum(conflicts[i])
if s[i] <= 50:
maxrooms[i] = 1
elif s[i] <= 100:
maxrooms[i] = 2
elif s[i] <= 400:
maxrooms[i] = 7
elif s[i] <= 700:
maxrooms[i] = 9
else:
maxrooms[i] = 12
c2 = LinExpr()
c4 = LinExpr()
for l in range(p):
c1 = LinExpr()
c1 = LinExpr()
c3 = LinExpr()
for k in range(r):
if T[k][l] == 1 and location[k] in w[i]:
# print k, c[k], 1-(1/(pow(2,s_sorted.index(k))))
obj.addTerms( 1-(1/(pow(2,s_sorted.index(k)))) , x[i, k, l])
c1.addTerms(1, x[i,k,l])
c4.addTerms(c[k],x[i,k,l])
model.addConstr(c1 <= maxrooms[i]* y[i,l], "c1a")
model.addConstr(c1 >= y[i,l], "C1b")
for j in conflicts[i]:
c3.addTerms(1,y[j,l])
model.addConstr(c3 <= (1 - y[i,l])*sumconflicts[i], "c3")
c2.addTerms(1,y[i,l])
model.addConstr( c2 == 1 , "c2")
model.addConstr(c4 >= s[i], "c4")
sumrooms = {}
for l in range(p):
sumrooms[l] = 0
cover_inequalities = LinExpr()
for k in range(r):
if T[k][l] == 1:
sumrooms[l] += 1
c5 = LinExpr()
for i in range(n):
if location[k] in w[i]:
c5.addTerms(1,x[i,k,l])
model.addConstr( c5 <= 1, "c5")
cover_inequalities += c5
model.addConstr(cover_inequalities <= sumrooms[l], "cover_inequalities")
# Break Symmetry
# First only use small rooms in a period if all bigger rooms are already used
# TODO Do for every location
if similarp[0] >= 0:
for i in range(i-1):
for l in range(p):
model.addConstr(y[i,l] <= quicksum( y[i+1,sim] for sim in similarp), "s1")
# for l in range(p):
# for index, k in enumerate(s_sorted):
# #print k, index
# s1 = LinExpr()
# if index < len(s_sorted)-1:
# if T[k][l] == 1:
# for k2 in range(r-index):
# if T[s_sorted[index+k2]][l] == 1:
# for i in range(n):
# # if location[k] in w[i]:
# s1.addTerms([1,-1], [x[i,k,l], x[i,s_sorted[index+k2],l]])
# break
# model.addConstr( s1 <= 0 , "s1")
#if p <= n:
# for l in range(p):
# model.addConstr( quicksum(y[l,i] for i in range(l)) >= 1, "s1")
# for l in range(p-1):
# for i in range(n):
# model.addConstr( y[i,l] - quicksum(y[i,l+1] for i in range(l,n)) <= 0, "l1")
model.setObjective( obj, GRB.MINIMIZE)
print timeit.default_timer()-start
if verbose:
print("All constrained and objective built - OK")
if not verbose:
model.params.OutputFlag = 0
# Set Parameters
#print("Setting Parameters...")
# max presolve agressivity
#model.params.presolve = 2
# Choosing root method 3= concurrent = run barrier and dual simplex in parallel
model.params.method = 3
#model.params.MIPFocus = 1
model.params.OutputFlag = 1
#model.params.MIPFocus = 1
# cuts
#model.params.cuts = 0
#model.params.coverCuts = 2
#model.params.CutPasses = 4
# heuristics
#model.params.heuristics = 0
#model.params.symmetry = 2
# # Tune the model
# model.tune()
# if model.tuneResultCount > 0:
# # Load the best tuned parameters into the model
# model.getTuneResult(0)
# # Write tuned parameters to a file
# model.write('tune1.prm')
# return
return(model)
def _objective_function_for_delta_weight(D, delta_weight):
m = Model("model_for_supplier_assignment")
x = {}
q = {}
for (r, s, p) in D.supplier_project_shipping:
# i resource, j supplier, k project
x[r, s, p] = m.addVar(vtype=GRB.BINARY, name="x_%s_%s_%s" % (r, s, p))
q[r, s, p] = m.addVar(vtype=GRB.CONTINUOUS, name="q_%s_%s_%s" % (r, s, p))
AT = {}
for j in range(D.project_n):
for k in sorted([r for r, p in D.resource_project_demand if p == D.project_list[j]]):
AT[j, k] = m.addVar(vtype=GRB.CONTINUOUS, name="AT_%s_%s" % (j, k))
m.update()
## define constraints
# constraint 20(3)
for (r, s) in D.resource_supplier_capacity:
m.addConstr(quicksum(q[r, s, D.project_list[j]] for j in range(D.project_n)), GRB.LESS_EQUAL,
D.resource_supplier_capacity[r, s],
name="constraint_3_resource_%s_supplier_%s" % (r, s))
# constraint 21(4) 23(6)
for (r, p) in D.resource_project_demand:
m.addConstr(quicksum(x[r, i, p] for i in D.resource_supplier_list[r]), GRB.EQUAL, 1,
name="constraint_6_resource_%s_project_%s" % (r, p))
m.addConstr(quicksum(q[r, i, p] for i in D.resource_supplier_list[r]), GRB.GREATER_EQUAL,
D.resource_project_demand[r, p], name="constraint_4_resource_%s_project_%s" % (r, p))
# constraint 22(5)
for (i, j, k) in q:
# i resource, j supplier, k project
m.addConstr(q[i, j, k], GRB.LESS_EQUAL, D.M * x[i, j, k],
name="constraint_5_resource_%s_supplier_%s_project_%s" % (i, j, k))
# constraint 7
expr = LinExpr()
for (i, j, k) in q:
expr = expr + D.c[i, j, k] * q[i, j, k]
m.addConstr(expr, GRB.LESS_EQUAL, D.B, name="constraint_7")
# constraint 8
for j in range(D.project_n):
p = D.project_list[j]
project_resources = sorted([r for (r, p_) in D.resource_project_demand.keys() if p_ == p])
for r in project_resources:
suppliers = D.resource_supplier_list[r]
# print(list(D.supplier_project_shipping.keys())[:10])
# print(D.supplier_project_shipping['NK0g77', 'S1671', 'P1'])
# print(list(x.keys())[:10])
# print(x['NK0g77', 'S1671', 'P1'])
m.addConstr(
quicksum(
x[r, s, p] * (D.resource_supplier_release_time[r, s] + D.supplier_project_shipping[r, s, p]) for
s in
suppliers), GRB.LESS_EQUAL, AT[j, r],
name="constraint_8_project_%d_resource_%s_deliver" % (j, r))
m.update()
expr = LinExpr()
for j in range(D.project_n):
for r in sorted([r for (r, p_) in D.resource_project_demand.keys() if p_ == p]):
expr.add(delta_weight[_delta_project_idx[j, r]] * AT[j, r])
m.setObjective(expr, GRB.MINIMIZE)
m.update()
##########################################
m.params.presolve = 1
m.update()
# Solve
# m.params.presolve=0
m.optimize()
print(m.Status)
X_ = {}
for (i, j, k) in D.supplier_project_shipping:
v = m.getVarByName("x_%s_%s_%s" % (i, j, k))
print(v)
if v.X == 1:
X_[i, j, k] = 1
AT_ = {}
for j, r in AT:
val = AT[j, r].X
if val > 0:
AT_[j, r] = val
return -_objective_function_for_tardiness(X_, AT_, D),
def _objective_function_for_delta_weight(D, delta_weight, d1, d2):
global _time_limit_per_model, _round, _pr_dataset, _tardiness_objective_dataset
m = Model("model_for_supplier_assignment")
m.setParam('OutputFlag', False)
m.params.timelimit = _time_limit_per_model
# m.params.IntFeasTol = 1e-7
x = {}
q = {}
for (r, s, p) in D.supplier_project_shipping:
x[r, s, p] = m.addVar(vtype=GRB.BINARY, name="x_%s_%s_%s" % (r, s, p))
q[r, s, p] = m.addVar(vtype=GRB.CONTINUOUS, name="q_%s_%s_%s" % (r, s, p))
AT = {}
for j in range(D.project_n):
for k in [r for r, p in D.resource_project_demand if p == D.project_list[j]]:
AT[j, k] = m.addVar(vtype=GRB.CONTINUOUS, name="AT_%s_%s" % (j, k))
m.update()
## define constraints
# equation 2
for (r, s) in D.resource_supplier_capacity:
m.addConstr(quicksum(q[r, s, D.project_list[j]] for j in range(D.project_n)), GRB.LESS_EQUAL,
D.resource_supplier_capacity[r, s],
name="constraint_3_resource_%s_supplier_%s" % (r, s))
# constraint 21(4) 23(6)
for (r, p) in D.resource_project_demand:
# equation 5
m.addConstr(quicksum(x[r, i, p] for i in D.resource_supplier_list[r]), GRB.EQUAL, 1,
name="constraint_6_resource_%s_project_%s" % (r, p))
# equation 3
m.addConstr(quicksum(q[r, i, p] for i in D.resource_supplier_list[r]), GRB.GREATER_EQUAL,
D.resource_project_demand[r, p], name="constraint_4_resource_%s_project_%s" % (r, p))
# constraint 22(5)
for (i, j, k) in q:
# i resource, j supplier, k project
# equation 4
m.addConstr(q[i, j, k], GRB.LESS_EQUAL, D.M * x[i, j, k],
name="constraint_5_resource_%s_supplier_%s_project_%s" % (i, j, k))
# constraint 7
shipping_cost_expr = LinExpr()
for (i, j, k) in q:
shipping_cost_expr.addTerms(D.c[i, j, k], q[i, j, k])
# equation 6
m.addConstr(shipping_cost_expr, GRB.LESS_EQUAL, D.B, name="constraint_7")
# constraint 8
# equation 26
for j in range(D.project_n):
p = D.project_list[j]
project_resources = [r for (r, p_) in D.resource_project_demand.keys() if p_ == p]
for r in project_resources:
suppliers = D.resource_supplier_list[r]
m.addConstr(
quicksum(
x[r, s, p] * (D.resource_supplier_release_time[r, s] + D.supplier_project_shipping[r, s, p]) for
s in
suppliers), GRB.LESS_EQUAL, AT[j, r],
name="constraint_8_project_%d_resource_%s_deliver" % (j, r))
m.update()
expr = LinExpr()
for j in range(D.project_n):
p = D.project_list[j]
for r in [r for (r, p_) in D.resource_project_demand.keys() if p_ == p]:
expr.add(delta_weight[j, r] * AT[j, r])
m.setObjective(expr, GRB.MINIMIZE)
m.update()
##########################################
# m.params.presolve = 1
m.update()
# Solve
# m.params.presolve=0
m.optimize()
_exit_if_infeasible(m)
m.write(join(_result_output_path, "round_%d_supplier_assign.lp" % _round))
m.write(join(_result_output_path, "round_%d_supplier_assign.sol" % _round))
with open(join(log_output_path, 'shipping_cost.txt'), 'a') as fout:
fout.write('shipping cost: %f\n' % shipping_cost_expr.getValue())
_logger.info('shipping cost: %f' % shipping_cost_expr.getValue())
print('status', m.status)
# m.write(join(_output_path, 'delta_weight.sol'))
# m.write(join(_output_path, 'delta_weight.lp'))
X_ = {}
for (i, j, k) in D.supplier_project_shipping:
v = m.getVarByName("x_%s_%s_%s" % (i, j, k))
if v.X == 1:
X_[i, j, k] = 1
AT_ = {}
for j, r in AT:
val = AT[j, r].X
if val > 0:
AT_[j, r] = val
tardiness_obj_val, skj, sj = _objective_function_for_tardiness(X_, AT_, D)
new_delta_weight = {}
# delta_weight_keys = list(delta_weight.keys())
# delta_weight_keys.sort(key=lambda x: x[1])
# delta_weight_keys.sort(key=lambda x: x[0])
for j, r in delta_weight.keys():
new_delta_weight[j, r] = delta_weight[j, r] * (1 + d1 * (d2 + sj.get(j, 0)) * skj.get((j, r), 0))
# print('j', type(j), j)
# print('r', type(r), r)
# print('previous weight', type(delta_weight[j, r]), delta_weight[j, r])
# print('d1', type(d1), d1)
# print('d2', type(d2), d2)
# print('sj', type(sj.get(j, 0)), sj.get(j, 0))
# print('skj', type(skj.get((j, r))), skj.get((j, r)))
# print('new weight', type(new_delta_weight[j, r]), new_delta_weight[j, r])
_logger.info(
'r[%d,%s] = %f *(1+%f*(%f+%f)*%f) = %f' % (
j, r, delta_weight[j, r], d1, d2, sj.get(j, 0), skj.get((j, r), 0), new_delta_weight[j, r]))
# new_delta_weight[j, r] = 1
_normalize(new_delta_weight)
for j, r in new_delta_weight.keys():
# _logger.info('j:' + str(j))
# _logger.info('r:' + str(r))
# _logger.info(str([_round, j, r, new_delta_weight[j, r]]))
_weight_dataset.loc[_weight_dataset.shape[0]] = [_round, j, r, new_delta_weight[j, r]]
for j in range(D.project_n):
_pr_dataset.loc[_pr_dataset.shape[0]] = [_round, j, sj.get(j, 0)]
_tardiness_objective_dataset.loc[_tardiness_objective_dataset.shape[0]] = [_round, tardiness_obj_val]
return new_delta_weight
def _sensitivity_for_constraints(AT, j, project, y_, project_activity, M):
global _round, _pa_dataset
m = Model("SingleProject_%d_for_sensitivity" % j)
m.setParam('OutputFlag', False)
# m.params.IntFeasTol = 1e-7
## Project complete data,Project Tadeness,construction completion time
CT = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="CT_%d" % j)
## Activity start time
ST = {}
project_activities = project_activity[project]
for row in project_activities.nodes():
ST[row] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="ST_%d_%s" % (j, row))
## Review sequence z_ij
## move to annealing objective function
m.update()
## Constrain 8: activity starting constrain
# equation 20
for a in project_activities.nodes():
for r in project_activities.node[a]['resources']:
m.addConstr(ST[a], GRB.GREATER_EQUAL, AT[j, r],
name="constraint_8_project_%d_activity_%s_resource_%s" % (j, a, r))
## Constrain 9 activity sequence constrain
# equation 21
for row1, row2 in project_activities.edges():
m.addConstr(ST[row1] + project_activities.node[row1]['duration'], GRB.LESS_EQUAL,
ST[row2], name="constraint_9_project_%d_activity_%s_activity_%s" % (j, row1, row2))
## Constrain 10,11
for row1 in project_activities.nodes():
for row2 in project_activities.nodes():
if row1 != row2 and len(list(
set(project_activities.node[row1]['rk_resources']).intersection(
project_activities.node[row2]['rk_resources']))) > 0:
# equation 22
m.addConstr(ST[row1] + project_activities.node[row1]['duration'] - M * (
1 - _get_y_for_activities(y_, row1, row2)), GRB.LESS_EQUAL, ST[row2],
name="constraint_10_project_%d_activity_%s_activity_%s" % (j, row1, row2))
# equation 23
m.addConstr(
ST[row2] + project_activities.node[row2]['duration'] - M * _get_y_for_activities(y_, row1, row2),
GRB.LESS_EQUAL, ST[row1],
name="constraint_11_project_%d_activity_%s_activity_%s" % (j, row1, row2))
# m.addConstr(y[j,row1,row2]+y[j,row2,row1],GRB.LESS_EQUAL,1)
## Constrain 12
# equation 24
for row in project_activities.nodes():
m.addConstr(CT, GRB.GREATER_EQUAL, ST[row] + project_activities.node[row]['duration'],
name="constraint_12_project_%d_activity_%s" % (j, row))
m.update()
# Set optimization objective - minimize completion time
expr = LinExpr()
expr.add(CT)
m.setObjective(expr, GRB.MINIMIZE)
m.update()
##########################################
# m.params.presolve = 1
m.update()
m.setParam(GRB.Param.Method, 0)
m.update()
# Solve
# m.params.presolve=0
m.optimize()
_skja = {}
for c in m.getConstrs():
if c.ConstrName.startswith('constraint_8_project'):
splits = c.ConstrName.split('_')
r = splits[7]
if r not in _skja:
_skja[r] = []
_skja[r].append(c.Pi)
# if c.Pi != 0:
# logging.debug('project %d binding resource:%s Pi:%.4g' % (j, splits[-1], c.Pi))
# else:
# logging.debug('project %d not binding resource:%s Pi:%.4g' % (j, splits[-1], c.Pi))
_pa_dataset.loc[_pa_dataset.shape[0]] = [_round, j, r, splits[5], c.Pi]
_skj = {}
for r in _skja:
_skj[j, r] = max(_skja[r])
_pa_max_dataset.loc[_pa_max_dataset.shape[0]] = [_round, j, r, max(_skja[r])]
return _skj
def __objective_function(self, x, q, p_changed=None):
m = Model("Overall_Model")
if p_changed is not None:
j0 = self.project_list.index(p_changed)
CT = self.last_CT
CT[j0] = self.optmize_single_project(x, j0, self.project_list, self.project_activity,
self.resource_supplier_list,
self.resource_supplier_release_time, self.supplier_project_shipping,
self.M,
self.output_dir)
else:
CT = {}
CT_ASYNC = dict()
for j in range(self.project_n):
## solve individual model get Project complete date
CT_ASYNC[j] = self.pool.apply_async(HeuristicParallelModel.optmize_single_project,
(x, j, self.project_list, self.project_activity,
self.resource_supplier_list,
self.resource_supplier_release_time,
self.supplier_project_shipping,
self.M,
self.output_dir))
for j in range(self.project_n):
CT[j] = CT_ASYNC[j].get()
self.last_CT = deepcopy(CT)
DT = {}
TD = {}
for j in range(self.project_n):
## Project Tadeness,construction completion time
DT[j] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="(DT%d)" % j)
TD[j] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="(TD%d)" % j)
DT[-1] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="(DT-1)")
## Review Sequence z_ij
z = {}
for i in range(self.project_n):
for j in range(self.project_n):
if i != j:
z[i, j] = m.addVar(obj=0, vtype=GRB.BINARY, name="(z%d,%d)" % (i, j))
for j in range(self.project_n):
z[-1, j] = m.addVar(obj=0, vtype=GRB.BINARY, name="(z%d,%d)" % (-1, j))
m.update();
#### Add Constraint ####
## Constrain 2: project complete data>due data ##
for j in range(self.project_n):
m.addConstr(DT[j] - TD[j], GRB.LESS_EQUAL, self.DD[j], name="constraint_2_project_%d" % j)
## Constraint 13
for j in range(self.project_n):
m.addConstr(DT[j], GRB.GREATER_EQUAL, CT[j] + self.review_duration[j], name="constraint_13_project_%d" % j)
## Constraint 14
for i in range(-1, self.project_n):
for j in range(self.project_n):
if i != j:
m.addConstr(DT[j], GRB.GREATER_EQUAL, DT[i] - self.M * (1 - z[i, j]) + self.review_duration[j],
name="constraint_14_project_%d_project_%d" % (i, j))
## Constrain 15
for j in range(self.project_n):
m.addConstr(quicksum(z[i, j] for i in range(-1, self.project_n) if i != j), GRB.EQUAL, 1,
name="constraint_15_project_%d" % j)
## Constrain 16
m.addConstr(quicksum(z[-1, j] for j in range(self.project_n)), GRB.EQUAL, 1, name="constraint_16")
## Constrain 17
for i in range(self.project_n):
m.addConstr(quicksum(z[i, j] for j in range(self.project_n) if j != i), GRB.LESS_EQUAL, 1,
name="constraint_17_project_%d" % i)
m.update()
# Set optimization objective - minimize sum of
expr = LinExpr()
for j in range(self.project_n):
expr.add(self.w[j] * TD[j])
m.setObjective(expr, GRB.MINIMIZE)
m.update()
m.params.presolve = 1
m.update()
m.optimize()
m.write(join(self.output_dir, "heuristic_whole.lp"))
m.write(join(self.output_dir, "heuristic_whole.sol"))
self.obj_value_trace.append(m.objVal)
return m.objVal, argmax([self.w[j] * TD[j].X for j in range(self.project_n)])
def optimize_single_project(AT, j, project_list, project_activity, M):
global _time_limit_per_model
m = Model("SingleProject_%d" % j)
m.params.timelimit = _time_limit_per_model
# m.setParam('OutputFlag', False)
# m.params.IntFeasTol = 1e-7
#### Create variables ####
project = project_list[j]
## Project complete data,Project Tadeness,construction completion time
CT = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="CT_%d" % j)
## Activity start time
ST = {}
project_activities = project_activity[project]
for row in project_activities.nodes():
ST[row] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="ST_%d_%s" % (j, row))
## Review sequence z_ij
## move to annealing objective function
# y
y = {}
for activity_i in project_activities.nodes():
for activity_j in project_activities.nodes():
if activity_i != activity_j and len(list(
set(project_activities.node[activity_i]['rk_resources']).intersection(
project_activities.node[activity_j]['rk_resources']))) > 0:
y[activity_i, activity_j] = m.addVar(obj=0, vtype=GRB.BINARY,
name="y_%d_%s_%s" % (j, activity_i, activity_j))
m.update()
## Constrain 8: activity starting constrain
# equation 20
for a in project_activities.nodes():
for r in project_activities.node[a]['resources']:
m.addConstr(AT[j, r], GRB.LESS_EQUAL, ST[a],
name="constraint_8_project_%d_activity_%s_resource_%s" % (j, a, r))
## Constrain 9 activity sequence constrain
# equation 21
for row1, row2 in project_activities.edges():
m.addConstr(ST[row1] + project_activities.node[row1]['duration'], GRB.LESS_EQUAL,
ST[row2], name="constraint_9_project_%d_activity_%s_activity_%s" % (j, row1, row2))
## Constrain 10,11
for row1 in project_activities.nodes():
for row2 in project_activities.nodes():
if row1 != row2 and len(list(
set(project_activities.node[row1]['rk_resources']).intersection(
project_activities.node[row2]['rk_resources']))) > 0:
# equation 22
m.addConstr(ST[row1] + project_activities.node[row1]['duration'] - M * (
1 - y[row1, row2]), GRB.LESS_EQUAL, ST[row2],
name="constraint_10_project_%d_activity_%s_activity_%s" % (j, row1, row2))
# equation 23
m.addConstr(
ST[row2] + project_activities.node[row2]['duration'] - M * (y[row1, row2]),
GRB.LESS_EQUAL, ST[row1],
name="constraint_11_project_%d_activity_%s_activity_%s" % (j, row1, row2))
# m.addConstr(y[j,row1,row2]+y[j,row2,row1],GRB.LESS_EQUAL,1)
## Constrain 12
# equation 24
for row in project_activities.nodes():
m.addConstr(CT, GRB.GREATER_EQUAL, ST[row] + project_activities.node[row]['duration'],
name="constraint_12_project_%d_activity_%s" % (j, row))
m.update()
# Set optimization objective - minimize completion time
expr = LinExpr()
expr.add(CT)
m.setObjective(expr, GRB.MINIMIZE)
m.update()
##########################################
# m.params.presolve = 1
m.update()
# Solve
# m.params.presolve=0
m.optimize()
m.write(join(_result_output_path, "round_%d_optimize_single_project_%d.lp" % (_round, j)))
m.write(join(_result_output_path, "round_%d_optimize_single_project_%d.sol" % (_round, j)))
# _logger.info("project %d with optimalVal %r" % (j, m.objVal))
# m.fixedModel()
skj = _sensitivity_for_constraints(AT, j, project, y, project_activity, M)
# for c in m.getConstrs():
# if c.ConstrName.startswith('constraint_8_project'):
# splits = c.ConstrName.split('_')
# if c.Pi == 0:
# _logger.info('project %d bind resource:%s Slack:%.4g'%(j, splits[-1],c.Pi))
# break
# else:
# _logger.info('project %d not bind'%j)
_single_project_objective_dataset.loc[_single_project_objective_dataset.shape[0]] = [_round, j, m.objVal]
return m.objVal, skj
def build_model(data, n_cliques = 0, verbose = True):
# Load Data Format
n = data['n']
r = data['r']
p = data['p']
s = data['s']
c = data['c']
h = data['h']
w = data['w']
location = data['location']
conflicts = data['conflicts']
locking_times = data['locking_times']
T = data['T']
model = Model("ExaminationScheduling")
if verbose:
print("Building variables...")
# x[i,k,l] = 1 if exam i is at time l in room k
x = {}
for k in range(r):
for l in range(p):
if T[k][l] == 1:
for i in range(n):
if location[k] in w[i]:
x[i,k,l] = model.addVar(vtype=GRB.BINARY, name="x_%s_%s_%s" % (i,k,l))
# y[i,l] = 1 if exam i is at time l
y = {}
for i in range(n):
for l in range(p):
y[i, l] = model.addVar(vtype=GRB.BINARY, name="y_%s_%s" % (i,l))
# integrate new variables
model.update()
start = timeit.default_timer()
# not very readable but same constraints as in GurbiLinear_v_10: speeded up model building by 2 for small problems (~400 exams) and more for huger problem ~1500 exams
if verbose:
print("Building constraints...")
obj = LinExpr()
sumconflicts = {}
maxrooms = {}
for i in range(n):
sumconflicts[i] = sum(conflicts[i])
if s[i] <= 50:
maxrooms[i] = 1
elif s[i] <= 100:
maxrooms[i] = 2
elif s[i] <= 400:
maxrooms[i] = 7
elif s[i] <= 700:
maxrooms[i] = 9
else:
maxrooms[i] = 12
c2 = LinExpr()
c4 = LinExpr()
for l in range(p):
c1 = LinExpr()
c1 = LinExpr()
c3 = LinExpr()
for k in range(r):
if T[k][l] == 1 and location[k] in w[i]:
c1.addTerms(1, x[i, k, l])
c4.addTerms(c[k],x[i,k,l])
obj += c1
model.addConstr(c1 <= maxrooms[i]* y[i,l], "c1a")
model.addConstr(c1 >= y[i,l], "C1b")
for j in conflicts[i]:
c3.addTerms(1,y[j,l])
if not conflicts[i]:
model.addConstr(c3 <= (1 - y[i,l])*sumconflicts[i], "c3")
c2.addTerms(1,y[i,l])
model.addConstr( c2 == 1 , "c2")
model.addConstr(c4 >= s[i], "c4")
sumrooms = {}
for l in range(p):
sumrooms[l] = 0
cover_inequalities = LinExpr()
for k in range(r):
if T[k][l] == 1:
sumrooms[l] += 1
c5 = LinExpr()
for i in range(n):
if location[k] in w[i]:
c5.addTerms(1,x[i,k,l])
model.addConstr( c5 <= 1, "c5")
cover_inequalities += c5
model.addConstr(cover_inequalities <= sumrooms[l], "cover_inequalities")
#lexicographic ordering in all periods for exams and rooms
for l in range(p):
for i in range(1,n):
for k in range(i,r):
if T[k][l] == 1:
model.addConstr(quicksum( x[i2,k,l] for i2 in range(i, min(r,k+1))) <= quicksum(x[i-1,k2,l] for k2 in range(k-1) if T[k2][l] == 1 ))
model.setObjective( obj, GRB.MINIMIZE)
print timeit.default_timer()-start
if verbose:
print("All constrained and objective built - OK")
if not verbose:
model.params.OutputFlag = 0
model.params.method = 3
#model.params.MIPFocus = 1
model.params.OutputFlag = 1
#model.params.MIPFocus = 1
return(model)
def build_model(data, n_cliques = 0, verbose = True):
# Load Data Format
n = data['n']
r = data['r']
p = data['p']
s = data['s']
c = data['c']
h = data['h']
w = data['w']
location = data['location']
conflicts = data['conflicts']
locking_times = data['locking_times']
T = data['T']
similarp = data['similarp']
similare = data['similare']
similarr = data['similarr']
model = Model("ExaminationScheduling")
if verbose:
print("Building variables...")
# Calculate Orbits
rs = 5
es = 10
orbit = {}
for l in range(p):
for k in range(r):
if k % rs == 0:
for i in range(n):
if i % es == 0:
orbit[i,k,l] = [ (i2,k2,l) for i2 in range(i,min(i+es,n)) for k2 in range(k,min(k+rs,r)) if T[k2][l] == 1 if conflicts[i] <= conflicts[i2] ]
# x[i,k,l] = 1 if exam i is at time l in room k
x = {}
for k in range(r):
for l in range(p):
if T[k][l] == 1:
for i in range(n):
if location[k] in w[i]:
x[i,k,l] = model.addVar(vtype=GRB.BINARY, name="x_%s_%s_%s" % (i,k,l))
# y[i,l] = 1 if exam i is at time l
y = {}
for i in range(n):
for l in range(p):
y[i, l] = model.addVar(vtype=GRB.BINARY, name="y_%s_%s" % (i,l))
#Orbit variable for orbital branching
o = {}
for key in orbit:
if orbit[key]:
o[key] = model.addVar(vtype=GRB.BINARY, name="o_%s_%s_%s" % (key[0],key[1],key[2]))
# integrate new variables
model.update()
for key in orbit:
if orbit[key]:
o[key].setAttr("BranchPriority", 1000000)
start = timeit.default_timer()
# not very readable but same constraints as in GurbiLinear_v_10: speeded up model building by 2 for small problems (~400 exams) and more for huger problem ~1500 exams
if verbose:
print("Building constraints...")
s_sorted = sorted(range(len(c)), key = lambda k: c[k])
obj = LinExpr()
sumconflicts = {}
maxrooms = {}
for i in range(n):
sumconflicts[i] = sum(conflicts[i])
if s[i] <= 50:
maxrooms[i] = 2
elif s[i] <= 100:
maxrooms[i] = 4
elif s[i] <= 400:
maxrooms[i] = 9
elif s[i] <= 700:
maxrooms[i] = 12
else:
maxrooms[i] = 12
c2 = LinExpr()
c4 = LinExpr()
for l in range(p):
c1 = LinExpr()
c1 = LinExpr()
c3 = LinExpr()
for k in range(r):
if T[k][l] == 1 and location[k] in w[i]:
# print k, c[k], 1-(1/(pow(2,s_sorted.index(k))))
#obj.addTerms( 1-(1/(pow(2,s_sorted.index(k)))) , x[i, k, l])
obj.addTerms(1, x[i,k,l])
c1.addTerms(1, x[i,k,l])
c4.addTerms(c[k],x[i,k,l])
model.addConstr(c1 <= maxrooms[i]* y[i,l], "c1a")
model.addConstr(c1 >= y[i,l], "C1b")
for j in conflicts[i]:
c3.addTerms(1,y[j,l])
if not conflicts[i]:
model.addConstr(c3 <= (1 - y[i,l])*sumconflicts[i], "c3")
c2.addTerms(1,y[i,l])
model.addConstr( c2 == 1 , "c2")
model.addConstr(c4 >= s[i], "c4")
sumrooms = {}
for l in range(p):
sumrooms[l] = 0
cover_inequalities = LinExpr()
for k in range(r):
if T[k][l] == 1:
sumrooms[l] += 1
c5 = LinExpr()
for i in range(n):
if location[k] in w[i]:
c5.addTerms(1,x[i,k,l])
model.addConstr( c5 <= 1, "c5")
cover_inequalities += c5
model.addConstr(cover_inequalities <= sumrooms[l], "cover_inequalities")
for key in orbit:
if orbit[key]:
model.addConstr(quicksum( x[i,k,l] for i,k,l in orbit[key] ) <= o[key]*len(orbit[key]), "symmetrie break")
# for l in range(p):
# print l
# for k in range(r):
# print k
# if T[k][l] == 1:
# for i in range(n):
# c6 = LinExpr()
# for i2 in similare[i]:
# for k2 in similarr[k]:
# if k2 >= 0:
# c6.addTerms(1,x[i2,k,l])
# model.addConstr(c6 <= o[i,k,l]*n, "symmetrie break")
# for i in range(p-2):
# for l in range(p):
# model.addConstr(y[i,l] <= quicksum( y[i+1,sim] for sim in similarp[l]), "s1")
model.setObjective( obj, GRB.MINIMIZE)
#model.write("CPLEX.mps")
print timeit.default_timer()-start
if verbose:
print("All constrained and objective built - OK")
if not verbose:
model.params.OutputFlag = 0
# Set Parameters
#print("Setting Parameters...")
# max presolve agressivity
#model.params.presolve = 2
# Choosing root method 3= concurrent = run barrier and dual simplex in parallel
#model.params.symmetrie = 2
model.params.method = 3
#model.params.presolve = 0
#model.params.MIPFocus = 1
model.params.OutputFlag = 1
#model.params.MIPFocus = 1
model.params.heuristics = 0
model.params.cuts = 0
return(model)
def __objective_function(self, x, q):
m = Model("Overall_Model")
CT = {}
DT = {}
TD = {}
#### Add Variable ####
for j in range(self.project_n):
## solve individual model get Project complete date
CT[j] = self.__optmize_single_project(x, j)
## Project Tadeness,construction completion time
DT[j] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="(DT%d)" % j)
TD[j] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="(TD%d)" % j)
DT[-1] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="(DT-1)")
## Review Sequence z_ij
z = {}
for i in range(self.project_n):
for j in range(self.project_n):
if i != j:
z[i, j] = m.addVar(obj=0, vtype=GRB.BINARY, name="(z%d,%d)" % (i, j))
for j in range(self.project_n):
z[-1, j] = m.addVar(obj=0, vtype=GRB.BINARY, name="(z%d,%d)" % (-1, j))
m.update();
#### Add Constraint ####
## Constrain 2: project complete data>due data ##
for j in range(self.project_n):
m.addConstr(DT[j] - TD[j], GRB.LESS_EQUAL, self.DD[j], name="constraint_2_project_%d" % j)
## Constraint 13
for j in range(self.project_n):
m.addConstr(DT[j], GRB.GREATER_EQUAL, CT[j] + self.review_duration[j], name="constraint_13_project_%d" % j)
## Constraint 14
for i in range(-1, self.project_n):
for j in range(self.project_n):
if i != j:
m.addConstr(DT[j], GRB.GREATER_EQUAL, DT[i] - self.M * (1 - z[i, j]) + self.review_duration[j],
name="constraint_14_project_%d_project_%d" % (i, j))
## Constrain 15
for j in range(self.project_n):
m.addConstr(quicksum(z[i, j] for i in range(-1, self.project_n) if i != j), GRB.EQUAL, 1,
name="constraint_15_project_%d" % j)
## Constrain 16
m.addConstr(quicksum(z[-1, j] for j in range(self.project_n)), GRB.EQUAL, 1, name="constraint_16")
## Constrain 17
for i in range(self.project_n):
m.addConstr(quicksum(z[i, j] for j in range(self.project_n) if j != i), GRB.LESS_EQUAL, 1,
name="constraint_17_project_%d" % i)
m.update()
# Set optimization objective - minimize sum of
expr = LinExpr()
for j in range(self.project_n):
expr.add(self.w[j] * TD[j])
m.setObjective(expr, GRB.MINIMIZE)
m.update()
m.params.presolve = 1
m.update()
m.optimize()
m.write(join(self.output_dir, "heuristic_whole.lp"))
m.write(join(self.output_dir, "heuristic_whole.sol"))
print([self.w[j] * TD[j].X for j in range(self.project_n)])
return m.objVal, argmax([self.w[j] * TD[j].X for j in range(self.project_n)])
def lazy_cycle_constraint(A, C, k, gap):
"""
Lazily generate cycle constraints as potential feasible solutions
are generated.
"""
_ = '*'
m = Model()
m.modelsense = GRB.MAXIMIZE
m.params.mipgap = gap
m.params.timelimit = 5 * 60 * 60
m.params.lazyconstraints = 1
n = A.shape[0]
edges = tuplelist()
vars = {}
for i in range(n):
for j in range(n):
if A[i, j] == 1:
e = (i, j)
edges.append(e)
w = 2 if j in C else 1
var = m.addVar(vtype=GRB.BINARY, obj=w)
vars[e] = var
m.update()
# flow constraints
for i in range(n):
out_vars = [vars[e] for e in edges.select(i, _)]
out_ones = [1.0]*len(out_vars)
out_expr = LinExpr()
out_expr.addTerms(out_ones, out_vars)
in_vars = [vars[e] for e in edges.select(_, i)]
in_ones = [1.0]*len(in_vars)
in_expr = LinExpr()
in_expr.addTerms(in_ones, in_vars)
m.addConstr(in_expr <= 1)
m.addConstr(out_expr == in_expr)
m.update()
ith_cycle = 0
def callback(model, where):
if where == GRB.Callback.MIPSOL:
sols = model.cbGetSolution([vars[e] for e in edges])
c_edges = [edges[i] for i in range(len(edges)) if sols[i] > 0.5]
cycles = cycles_from_edges(c_edges)
for cycle in cycles:
len_cycle = len(cycle)
if len_cycle > k:
cycle_vars = [vars[(cycle[i], cycle[(i+1) % len_cycle])] for i in range(len_cycle)]
ones = [1.0]*len(cycle_vars)
expr = LinExpr()
expr.addTerms(ones, cycle_vars)
model.cbLazy(expr <= len_cycle - 1)
m.optimize(callback)
m.update()
c_edges = [e for e in edges if vars[e].x == 1.0]
cycles = cycles_from_edges(c_edges)
return cycles, m.objval
def __optmize_single_project(self, x, j):
'''
Given the generated x for single project, try to optimize the tardiness of the project.
:param x: the assignment of resource supplier to project
:param j: index of project
:return:
'''
m = Model("SingleProject_%d" % j)
#### Create variables ####
project = self.project_list[j]
## Project complete data,Project Tadeness,construction completion time
CT = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="(CT%d)" % j)
## Activity start time
ST = {}
project_activities = self.project_activity[project]
# print(project_activities.nodes())
for row in project_activities.nodes():
ST[row] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="(ST%d,%s)" % (j, row))
## Review sequence z_ij
## move to annealing objective function
# y
y = {}
for activity_i in project_activities.nodes():
for activity_j in project_activities.nodes():
# print(project_activities.node[activity_i])
# print(dir(project_activities.node[activity_i]))
if activity_i != activity_j and len(list(
set(project_activities.node[activity_i]['rk_resources']).intersection(
project_activities.node[activity_j]['rk_resources']))) > 0:
y[activity_i, activity_j] = m.addVar(obj=0, vtype=GRB.BINARY,
name="(y%d,%s,%s)" % (j, activity_i, activity_j))
m.update()
#### Create constrains ####
## Constrain 2: project complete data>due data
## move to annealing objective function
## Constrain 3: supplier capacity limit
## move to annealing neighbor & random generator
## Constrain 4,6: project demand require; each project receive from one supplier for each resource
## move to annealing neighbor & random generator
## constrain 5: shipping constrain
## move to annealing neighbor & random generator
## Constrain 7:budget limit
## move to annealing constraint valid
## Constrain 8: activity starting constrain
for a in project_activities.nodes():
for r in project_activities.node[a]['resources']:
resource_delivered_days = 0
for s in self.resource_supplier_list[r]:
resource_delivered_days += x.get((r, s, project), 0) * \
(self.resource_supplier_release_time[r, s] +
self.supplier_project_shipping[
r, s, project])
m.addConstr(resource_delivered_days, GRB.LESS_EQUAL, ST[a],
name="constraint_8_project_%d_activity_%s_resource_%s" % (j, a, r))
## Constrain 9 activity sequence constrain
for row1, row2 in project_activities.edges():
# print(row1, '#', row2, '#', j)
# print(ST)
m.addConstr(ST[row1] + project_activities.node[row1]['duration'], GRB.LESS_EQUAL,
ST[row2], name="constraint_9_project_%d_activity_%s_activity_%s" % (j, row1, row2))
## Constrain 10,11
for row1 in project_activities.nodes():
for row2 in project_activities.nodes():
if row1 != row2 and len(list(
set(project_activities.node[row1]['rk_resources']).intersection(
project_activities.node[row2]['rk_resources']))) > 0:
m.addConstr(ST[row1] + project_activities.node[row1]['duration'] - self.M * (
1 - y[row1, row2]), GRB.LESS_EQUAL, ST[row2],
name="constraint_10_project_%d_activity_%s_activity_%s" % (j, row1, row2))
m.addConstr(
ST[row2] + project_activities.node[row2]['duration'] - self.M * (y[row1, row2]),
GRB.LESS_EQUAL, ST[row1],
name="constraint_11_project_%d_activity_%s_activity_%s" % (j, row1, row2))
# m.addConstr(y[j,row1,row2]+y[j,row2,row1],GRB.LESS_EQUAL,1)
## Constrain 12
for row in project_activities.nodes():
# print(project_activities.node[row]['duration'])
m.addConstr(CT, GRB.GREATER_EQUAL, ST[row] + project_activities.node[row]['duration'],
name="constraint_12_project_%d_activity_%s" % (j, row))
## Constrain 13
## move to anealing objective function
## Constrain 14
## move to anealing objective function
## Constrain 15
## move to anealing objective function
## Constrain 16
## move to anealing objective function
## Constrain 17
## move to anealing objective function
m.update()
# Set optimization objective - minimize completion time
expr = LinExpr()
expr.add(CT)
m.setObjective(expr, GRB.MINIMIZE)
m.update()
##########################################
m.params.presolve = 1
m.update()
# Solve
# m.params.presolve=0
m.optimize()
m.write(join(self.output_dir, "heuristic_%d.lp" % j))
m.write(join(self.output_dir, "heuristic_%d.sol" % j))
return m.objVal
def constantino(A, C, k, gap):
"""
Polynomial-sized CCMcP Edge-Extended Model
See Constantino et al. (2013)
"""
t_0 = time.clock()
_ = '*'
m = Model()
m.modelsense = GRB.MAXIMIZE
m.params.mipgap = gap
# m.params.timelimit = 60 * 60
# m.params.nodefilestart = 1.0
# m.params.nodefiledir = './.nodefiledir'
# m.params.presparsify = 0
# m.params.presolve = 0
n = A.shape[0]
vars = {}
edges = tuplelist()
print('[%.1f] Generating variables...' % (time.clock() - t_0))
# Variables
for l in range(n):
for i in range(l, n):
for j in range(l, n):
if A[i, j] == 1:
e = (l, i, j)
edges.append(e)
w = 2 if j in C else 1
var = m.addVar(vtype=GRB.BINARY, obj=w)
vars[e] = var
if l % 100 == 0 and l != 0:
print('[%.1f] l = %d' % (time.clock() - t_0, l))
m.update()
print('[%.1f] Generated variables' % (time.clock() - t_0))
print('[%.1f] Adding flow constraints...' % (time.clock() - t_0))
# Constraint (2): Flow in = Flow out
for l in range(n):
for i in range(l, n):
# Flow in
lhs_vars = [vars[e] for e in edges.select(l, _, i)]
ones = [1.0]*len(lhs_vars)
lhs = LinExpr()
lhs.addTerms(ones, lhs_vars)
# Flow out
rhs_vars = [vars[e] for e in edges.select(l, i, _)]
ones = [1.0]*len(rhs_vars)
rhs = LinExpr()
rhs.addTerms(ones, rhs_vars)
# Flow in = Flow out
m.addConstr(lhs == rhs)
if l % 100 == 0 and l != 0:
print('[%.1f] l = %d' % (time.clock() - t_0, l))
print('[%.1f] Added flow constraints' % (time.clock() - t_0))
print('[%.1f] Adding cycle vertex constraints...' % (time.clock() - t_0))
# Constraint (3): Use a vertex only once per cycle
for i in range(n):
c_vars = [vars[e] for e in edges.select(_, i, _)]
ones = [1.0]*len(c_vars)
expr = LinExpr()
expr.addTerms(ones, c_vars)
m.addConstr(expr <= 1.0)
if i % 100 == 0 and i != 0:
print('[%.1f] V_i = %d' % (time.clock() - t_0, i))
print('[%.1f] Added cycle vertex constraints' % (time.clock() - t_0))
print('[%.1f] Adding cycle cardinality constraints...' % (time.clock() - t_0))
# Constraint (4): Limit cardinality of cycles to k
for l in range(n):
c_vars = [vars[e] for e in edges.select(l, _, _)]
ones = [1.0]*len(c_vars)
expr = LinExpr()
expr.addTerms(ones, c_vars)
m.addConstr(expr <= k)
if l % 100 == 0 and l != 0:
print('[%.1f] l = %d' % (time.clock() - t_0, l))
print('[%.1f] Added cycle cardinality constraints' % (time.clock() - t_0))
print('[%.1f] Adding cycle index constraints...' % (time.clock() - t_0))
# Constraint (5): Cycle index is smallest vertex-index
for l in range(n):
rhs_vars = [vars[e] for e in edges.select(l, l, _)]
ones = [1.0]*len(rhs_vars)
rhs = LinExpr()
rhs.addTerms(ones, rhs_vars)
for i in range(l+1, n):
lhs_vars = [vars[e] for e in edges.select(l, i, _)]
if len(lhs_vars) > 0:
ones = [1.0]*len(lhs_vars)
lhs = LinExpr()
lhs.addTerms(ones, lhs_vars)
m.addConstr(lhs <= rhs)
if l % 100 == 0 and l != 0:
print('[%.1f] l = %d' % (time.clock() - t_0, l))
print('[%.1f] Added cycle index constraints...' % (time.clock() - t_0))
print('[%.1f] Begin Optimizing %d vertex model' % (time.clock() - t_0, n))
m.optimize()
m.update()
print('[%.1f] Finished Optimizing' % (time.clock() - t_0))
print('[%.1f] Building cycles...' % (time.clock() - t_0))
cycles = []
for l in range(n):
c_edges = [(e[1], e[2]) for e in edges.select(l, _, _) if vars[e].x == 1.0]
cycles.extend(cycles_from_edges(c_edges))
print('[%.1f] Finished building cycles' % (time.clock() - t_0))
return cycles, m.objval
def _objective_function_for_tardiness(x, AT, D):
global _last_CT, _last_x, _pool
m = Model("Overall_Model")
CT = {}
CT_ASYNC = dict()
# project_to_recompute, project_no_recompute = get_project_to_recompute(x, D.project_n, D.project_list)
project_suppliers = _get_project_suppliers_map(x, D.project_list)
for j in range(D.project_n):
p = D.project_list[j]
history_CT = _get_CT(p, project_suppliers[p])
if history_CT is None:
CT[j] = optmize_single_project(AT, j, D.project_list, D.project_activity, D.M)
# CT_ASYNC[j] = _pool.apply_async(optmize_single_project,
# (AT, j, D.project_list, D.project_activity, D.M))
else:
# logging.info('%s [%s]' % (p, ','.join(sorted(project_suppliers[p]))))
CT[j] = history_CT
# logging.info('project %s get historical value %f.' % (p, history_CT))
for j in CT_ASYNC:
CT[j] = CT_ASYNC[j].get()
p = D.project_list[j]
_put_CT(p, project_suppliers[p], CT[j])
# _last_CT = deepcopy(CT)
# _last_x = deepcopy(x)
# if len(project_no_recompute) == D.project_n:
# return _tardiness_obj_trace[-1]
# self.last_CT = deepcopy(CT)
DT = {}
TD = {}
for j in range(D.project_n):
## Project Tadeness,construction completion time
DT[j] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="(DT%d)" % j)
TD[j] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="(TD%d)" % j)
DT[-1] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="(DT-1)")
## Review Sequence z_ij
z = {}
for i in range(D.project_n):
for j in range(D.project_n):
if i != j:
z[i, j] = m.addVar(obj=0, vtype=GRB.BINARY, name="(z%d,%d)" % (i, j))
for j in range(D.project_n):
z[-1, j] = m.addVar(obj=0, vtype=GRB.BINARY, name="(z%d,%d)" % (-1, j))
m.update();
#### Add Constraint ####
## Constrain 2: project complete data>due data ##
for j in range(D.project_n):
m.addConstr(DT[j] - TD[j], GRB.LESS_EQUAL, D.DD[j], name="constraint_2_project_%d" % j)
## Constraint 13
for j in range(D.project_n):
m.addConstr(DT[j], GRB.GREATER_EQUAL, CT[j] + D.review_duration[j], name="constraint_13_project_%d" % j)
## Constraint 14
for i in range(-1, D.project_n):
for j in range(D.project_n):
if i != j:
m.addConstr(DT[j], GRB.GREATER_EQUAL, DT[i] - D.M * (1 - z[i, j]) + D.review_duration[j],
name="constraint_14_project_%d_project_%d" % (i, j))
## Constrain 15
for j in range(D.project_n):
m.addConstr(quicksum(z[i, j] for i in range(-1, D.project_n) if i != j), GRB.EQUAL, 1,
name="constraint_15_project_%d" % j)
## Constrain 16
m.addConstr(quicksum(z[-1, j] for j in range(D.project_n)), GRB.EQUAL, 1, name="constraint_16")
## Constrain 17
for i in range(D.project_n):
m.addConstr(quicksum(z[i, j] for j in range(D.project_n) if j != i), GRB.LESS_EQUAL, 1,
name="constraint_17_project_%d" % i)
m.update()
# Set optimization objective - minimize sum of
expr = LinExpr()
for j in range(D.project_n):
expr.add(D.w[j] * TD[j])
m.setObjective(expr, GRB.MINIMIZE)
m.update()
m.params.presolve = 1
m.update()
m.optimize()
# m.write(join(self.output_dir, "heuristic_whole.lp"))
# m.write(join(self.output_dir, "heuristic_whole.sol"))
# self.obj_value_trace.append(m.objVal)
_tardiness_obj_trace.append(m.objVal)
# logging.info("Tardiness value:%r" % m.objVal)
return m.objVal
def partition(self, k=None, size=None, balance=None, name='partition'):
n = self.number_of_nodes()
# Create a new model
self.model = Model(name)
# Create variables
# Xi :: Node i is representative of a partition
# Xij :: Edge between i and j is cut
# 0 => i & j are in the same partition
# 1 => i & j are in different partition
xi = [self.model.addVar(vtype=GRB.BINARY, name='x'+str(i+1))
for i in range(n)]
xij = Bind(self.model, n)
# Reinforcement of model
# Zij = Xi x Xij
zij = [[self.model.addVar(vtype=GRB.BINARY,
name='x'+str(i+1)+' x x'+str(i+1)+'.'+str(j+1))
for i in range(j)]
for j in range(1, n)]
# Integrate new variables
self.model.update()
# Number of nodes in the partition of node i
if balance != None or size != None:
wi = [quicksum([xij[i, j] for j in range(n) if j != i])
for i in range(n)]
# Set objective
# Minimize the weighted sum of Xij
obj = LinExpr()
for i, j in self.edges_iter():
obj.addTerms(self[i][j]['weight'], xij[i-1, j-1])
self.model.setObjective(obj, GRB.MINIMIZE)
# Add partition number
# There must be exactly K partitions
if k != None:
self.model.addConstr(quicksum(xi[i] for i in range(n)) == k)
else:
self.model.addConstr(quicksum(xi[i] for i in range(n)) >= 2)
# Absolute limitation the size of a partition
if size != None:
for i in range(n):
self.model.addConstr((n - wi[i]) <= size)
# Relative limit of the size of a partition
if balance != None:
for i in range(n):
self.model.addConstr((n - wi[i]) * k <= n * balance)
# Linerarisation of multiplication Xi x Xij
for j in range(n-1):
for i in range(j+1):
self.model.addConstr(zij[j][i] <= xi[i])
self.model.addConstr(zij[j][i] <= 1 - xij[i, j+1])
self.model.addConstr(zij[j][i] >= xi[i] - xij[i, j+1])
# Add triangle inequality
for i in range(n):
for j in range(i+1, n):
for k in range(j+1, n):
# xij <= xik + xjk
self.model.addConstr(xij[i, j] <= xij[i, k] + xij[j, k])
self.model.addConstr(xij[i, k] <= xij[i, j] + xij[j, k])
self.model.addConstr(xij[j, k] <= xij[i, j] + xij[i, k])
# A node is either a representative,
# either in a partition with a smaller node
for j in range(n):
obj = LinExpr()
obj.addTerms(1, xi[j])
for i in range(j):
obj.addTerms(1, zij[j-1][i])
self.model.addConstr(obj == 1)
# Resolve
self.model.optimize()
# Compute resultat
self.k = 0
for i, v in enumerate(xi):
if v.x == 1:
self.node[i+1]['partition'] = self.k
for j in range(i+1, n):
if xij[i, j].x == 0:
self.node[j+1]['partition'] = self.k
self.k += 1
self.compute()
def optmize_single_project(AT, j, project_list, project_activity, M):
m = Model("SingleProject_%d" % j)
m.setParam(GRB.Param.Method, 0)
m.update()
#### Create variables ####
project = project_list[j]
## Project complete data,Project Tadeness,construction completion time
CT = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="(CT%d)" % j)
## Activity start time
ST = {}
project_activities = project_activity[project]
# print(project_activities.nodes())
for row in project_activities.nodes():
ST[row] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="(ST%d,%s)" % (j, row))
## Review sequence z_ij
## move to annealing objective function
# y
y = {}
for activity_i in project_activities.nodes():
for activity_j in project_activities.nodes():
# print(project_activities.node[activity_i])
# print(dir(project_activities.node[activity_i]))
if activity_i != activity_j and len(list(
set(project_activities.node[activity_i]['rk_resources']).intersection(
project_activities.node[activity_j]['rk_resources']))) > 0:
y[activity_i, activity_j] = m.addVar(obj=0, vtype=GRB.BINARY,
name="(y%d,%s,%s)" % (j, activity_i, activity_j))
m.update()
#### Create constrains ####
## Constrain 2: project complete data>due data
## move to annealing objective function
## Constrain 3: supplier capacity limit
## move to annealing neighbor & random generator
## Constrain 4,6: project demand require; each project receive from one supplier for each resource
## move to annealing neighbor & random generator
## constrain 5: shipping constrain
## move to annealing neighbor & random generator
## Constrain 7:budget limit
## move to annealing constraint valid
## Constrain 8: activity starting constrain
for a in project_activities.nodes():
for r in project_activities.node[a]['resources']:
m.addConstr(AT[j, r], GRB.LESS_EQUAL, ST[a],
name="constraint_8_project_%d_activity_%s_resource_%s" % (j, a, r))
## Constrain 9 activity sequence constrain
for row1, row2 in project_activities.edges():
m.addConstr(ST[row1] + project_activities.node[row1]['duration'], GRB.LESS_EQUAL,
ST[row2], name="constraint_9_project_%d_activity_%s_activity_%s" % (j, row1, row2))
## Constrain 10,11
for row1 in project_activities.nodes():
for row2 in project_activities.nodes():
if row1 != row2 and len(list(
set(project_activities.node[row1]['rk_resources']).intersection(
project_activities.node[row2]['rk_resources']))) > 0:
m.addConstr(ST[row1] + project_activities.node[row1]['duration'] - M * (
1 - y[row1, row2]), GRB.LESS_EQUAL, ST[row2],
name="constraint_10_project_%d_activity_%s_activity_%s" % (j, row1, row2))
m.addConstr(
ST[row2] + project_activities.node[row2]['duration'] - M * (y[row1, row2]),
GRB.LESS_EQUAL, ST[row1],
name="constraint_11_project_%d_activity_%s_activity_%s" % (j, row1, row2))
# m.addConstr(y[j,row1,row2]+y[j,row2,row1],GRB.LESS_EQUAL,1)
## Constrain 12
for row in project_activities.nodes():
m.addConstr(CT, GRB.GREATER_EQUAL, ST[row] + project_activities.node[row]['duration'],
name="constraint_12_project_%d_activity_%s" % (j, row))
## Constrain 13
## move to anealing objective function
## Constrain 14
## move to anealing objective function
## Constrain 15
## move to anealing objective function
## Constrain 16
## move to anealing objective function
## Constrain 17
## move to anealing objective function
m.update()
# Set optimization objective - minimize completion time
expr = LinExpr()
expr.add(CT)
m.setObjective(expr, GRB.MINIMIZE)
m.update()
##########################################
m.params.presolve = 1
m.update()
# Solve
# m.params.presolve=0
m.optimize()
# m.write(join(output_dir, "heuristic_%d.lp" % j))
# m.write(join(output_dir, "heuristic_%d.sol" % j))
# logging.info("project %d with optimalVal %r" % (j, m.objVal))
print(m.status == GRB.OPTIMAL)
for c in m.getConstrs():
if c.ConstrName.startswith('constraint_8_project'):
c.getAttr(GRB.Attr.SARHSLow)
logging.info('%s shadow price (%f,%f)' % (c.ConstrName, c.SARHSLow, c.SARHSUp))
return m.objVal
def build_model(data, n_cliques = 0, verbose = True):
# Load Data Format
n = data['n']
r = data['r']
p = data['p']
s = data['s']
c = data['c']
h = data['h']
w = data['w']
location = data['location']
conflicts = data['conflicts']
locking_times = data['locking_times']
T = data['T']
model = Model("ExaminationScheduling")
if verbose:
print("Building variables...")
# x[i,k,l] = 1 if exam i is at time l in room k
x = {}
for k in range(r):
for l in range(p):
if T[k][l] == 1:
for i in range(n):
if location[k] in w[i]:
x[i,k,l] = model.addVar(vtype=GRB.BINARY, name="x_%s_%s_%s" % (i,k,l))
# y[i,l] = 1 if exam i is at time l
y = {}
for i in range(n):
for l in range(p):
y[i, l] = model.addVar(vtype=GRB.BINARY, name="y_%s_%s" % (i,l))
# integrate new variables
model.update()
start = timeit.default_timer()
# not very readable but same constraints as in GurbiLinear_v_10: speeded up model building by 2 for small problems (~400 exams) and more for huger problem ~1500 exams
if verbose:
print("Building constraints...")
obj = LinExpr()
sumconflicts = {}
maxrooms = {}
for i in range(n):
sumconflicts[i] = sum(conflicts[i])
if s[i] <= 50:
maxrooms[i] = 1
elif s[i] <= 100:
maxrooms[i] = 2
elif s[i] <= 400:
maxrooms[i] = 7
elif s[i] <= 700:
maxrooms[i] = 9
else:
maxrooms[i] = 12
c2 = LinExpr()
c4 = LinExpr()
for l in range(p):
c1 = LinExpr()
c1 = LinExpr()
c3 = LinExpr()
for k in range(r):
if T[k][l] == 1 and location[k] in w[i]:
c1.addTerms(1, x[i, k, l])
c4.addTerms(c[k],x[i,k,l])
obj += c1
model.addConstr(c1 <= maxrooms[i]* y[i,l], "c1a")
model.addConstr(c1 >= y[i,l], "C1b")
for j in conflicts[i]:
c3.addTerms(1,y[j,l])
model.addConstr(c3 <= (1 - y[i,l])*sumconflicts[i], "c3")
c2.addTerms(1,y[i,l])
model.addConstr( c2 == 1 , "c2")
model.addConstr(c4 >= s[i], "c4")
sumrooms = {}
for l in range(p):
sumrooms[l] = 0
cover_inequalities = LinExpr()
for k in range(r):
if T[k][l] == 1:
sumrooms[l] += 1
c5 = LinExpr()
for i in range(n):
if location[k] in w[i]:
c5.addTerms(1,x[i,k,l])
model.addConstr( c5 <= 1, "c5")
cover_inequalities += c5
model.addConstr(cover_inequalities <= sumrooms[l], "cover_inequalities")
model.setObjective( obj, GRB.MINIMIZE)
print timeit.default_timer()-start
if verbose:
print("All constrained and objective built - OK")
if not verbose:
model.params.OutputFlag = 0
# Set Parameters
#print("Setting Parameters...")
# max presolve agressivity
#model.params.presolve = 2
# Choosing root method 3= concurrent = run barrier and dual simplex in parallel
#model.params.method = 1
#model.params.MIPFocus = 1
model.params.OutputFlag = 1
model.params.Method = 3
# cuts
model.params.cuts = 0
model.params.cliqueCuts = 0
model.params.coverCuts = 0
model.params.flowCoverCuts = 0
model.params.FlowPathcuts = 0
model.params.GUBCoverCuts = 0
model.params.impliedCuts = 0
model.params.MIPSepCuts = 0
model.params.MIRCuts = 0
model.params.ModKCuts = 0
model.params.NetworkCuts = 2
model.params.SUBMIPCuts = 0
model.params.ZeroHalfCuts = 0
model.params.TimeLimit = 30
# # Tune the model
# model.tune()
# if model.tuneResultCount > 0:
# # Load the best tuned parameters into the model
# model.getTuneResult(0)
# # Write tuned parameters to a file
# model.write('tune1.prm')
# return
return(model)
def _objective_function_for_tardiness(x, AT, D):
global _last_CT, _last_x, _pool, _time_limit_per_model
m = Model("Overall_Model")
m.params.timelimit = _time_limit_per_model
# m.setParam('OutputFlag', False)
# m.params.IntFeasTol = 1e-7
CT = {}
# CT_ASYNC = dict()
# project_to_recompute, project_no_recompute = get_project_to_recompute(x, D.project_n, D.project_list)
project_suppliers = _get_project_suppliers_map(x, D.project_list)
critical_project_resource = dict()
# for j in range(D.project_n):
# p = D.project_list[j]
# CT_ASYNC[j] = _pool.apply_async(optimize_single_project,
# (AT, j, D.project_list, D.project_activity, D.M))
#
# for j in CT_ASYNC:
# p = D.project_list[j]
# CT[j], skj = CT_ASYNC[j].get()
# _put_CT(p, project_suppliers[p], CT[j])
# _put_historical_delta_weight_idx_map(p, project_suppliers[p], skj)
# critical_project_resource.update(skj)
for j in range(D.project_n):
p = D.project_list[j]
CT[j], skj = optimize_single_project(AT, j, D.project_list, D.project_activity, D.M)
_put_CT(p, project_suppliers[p], CT[j])
_put_historical_delta_weight_idx_map(p, project_suppliers[p], skj)
critical_project_resource.update(skj)
DT = {}
TD = {}
for j in range(D.project_n):
## Project Tadeness,construction completion time
DT[j] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="DT_%d" % j)
TD[j] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="TD_%d" % j)
DT[-1] = m.addVar(obj=0, vtype=GRB.CONTINUOUS, name="DT_-1")
## Review Sequence z_ij
z = {}
for i in range(D.project_n):
for j in range(D.project_n):
if i != j:
z[i, j] = m.addVar(obj=0, vtype=GRB.BINARY, name="z_%d_%d" % (i, j))
for j in range(D.project_n):
z[-1, j] = m.addVar(obj=0, vtype=GRB.BINARY, name="z_%d_%d" % (-1, j))
m.update();
#### Add Constraint ####
## Constrain 2: project complete data>due data ##
# equation 17
for j in range(D.project_n):
m.addConstr(DT[j] - TD[j], GRB.LESS_EQUAL, D.DD[j], name="constraint_2_project_%d" % j)
## Constraint 13
# equation 12
for j in range(D.project_n):
m.addConstr(DT[j], GRB.GREATER_EQUAL, CT[j] + D.review_duration[j],
name="constraint_13_project_%d" % j)
## Constraint 14
# equation 13
for i in range(-1, D.project_n):
for j in range(D.project_n):
if i != j:
m.addConstr(DT[j], GRB.GREATER_EQUAL, DT[i] - D.M * (1 - z[i, j]) + D.review_duration[j],
name="constraint_14_project_%d_project_%d" % (i, j))
## Constrain 15
# equation 14
for j in range(D.project_n):
m.addConstr(quicksum(z[i, j] for i in range(-1, D.project_n) if i != j), GRB.EQUAL, 1,
name="constraint_15_project_%d" % j)
## Constrain 16
# equation 15
m.addConstr(quicksum(z[-1, j] for j in range(D.project_n)), GRB.EQUAL, 1, name="constraint_16")
## Constrain 17
# equation 16
for i in range(D.project_n):
m.addConstr(quicksum(z[i, j] for j in range(D.project_n) if j != i), GRB.LESS_EQUAL, 1,
name="constraint_17_project_%d" % i)
m.update()
# Set optimization objective - minimize sum of
expr = LinExpr()
for j in range(D.project_n):
expr.add(D.w[j] * TD[j])
m.setObjective(expr, GRB.MINIMIZE)
m.update()
# m.params.presolve = 1
m.update()
m.optimize()
m.write(join(_result_output_path, 'round_%d_tardiness.sol' % _round))
m.write(join(_result_output_path, 'round_%d_tardiness.lp' % _round))
z_ = {}
for i, j in z:
z_[i, j] = z[i, j].X
critical_projs = _sensitivity_analysis_for_tardiness(z_, CT, D)
_tardiness_obj_trace.append(m.objVal)
_gap_trace.append(m.MIPGap)
return m.objVal, critical_project_resource, critical_projs