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TwoRRSlave.py
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# This file contains the "slave" part of the master-slave
# algorithm. Given a home-away pattern (computed in the master)
# the slave creates the assignments between teams. Note that,
# given a certain home-away pattern, a feasible assignment
# does not necessarily exist.
# pylint: disable=no-name-in-module, no-member
import os
#os.environ["GRB_LICENSE_FILE"] = "C:\\gurobi\\gurobi-ac.lic"
import gurobipy as gp
from gurobipy import GRB
from TwoRRProblem import TwoRRProblem, write_solution
from TwoRRValidator import validate_constraint
def solve_slave(prob, model, ha_patterns, debug = True):
n_teams = len(prob.teams)
n_slots = len(prob.slots)
# Fix the variables
for team1 in range(n_teams):
for team2 in range(n_teams):
if team1 == team2:
continue
for slot in range(n_slots):
if ha_patterns[team1][slot] == 0 or ha_patterns[team2][slot] == 1:
model._vars[team1, team2, slot].ub = 0
else:
model._vars[team1, team2, slot].ub = 1
print(">>>> Slave: Finding assignment...")
# Optimize
model.optimize()
write_status(model)
if (model.solCount > 0):
solution = make_solution(model._vars, n_teams, n_slots)
obj = 0
for constraint in prob.constraints:
violated,diff,penalty = validate_constraint(prob, solution, constraint)
obj += penalty
#print(constraint[0], (violated,diff,penalty))
if model._best_obj == -1 or obj < model._best_obj:
model._best_obj = obj
print(">>>> Slave: Found new best incumbent with value: " + str(obj))
write_solution("ms_solution.xml", prob, model._vars, model.objVal)
else:
print(">>>> Slave: Found assignment with value: " + str(obj))
return False
def create_slave(prob: TwoRRProblem, env, skipSoft=False, lazy=0, debug=True):
# Set up and solve with Gurobi a "naive" model
# for the TwoRRProblem. The model is naive in
# the sense that il follows the standard integer
# programming techniques, building a large complex
# model and hope that Gurobi will be able to handle
# it. This works only for simple problems.
n_teams = len(prob.teams)
n_slots = len(prob.slots)
# Create Gurobi model
model = gp.Model(prob.name, env)
model.setParam("OutputFlag", 0)
model.setParam("Threads", 1)
# Tuning parameters
model.setParam("Presolve", 2)
model.setParam("Symmetry", 2)
model.setParam("GomoryPasses", 1)
model.setParam("PrePasses", 2)
model.setParam("MIPFocus", 1)
model.setParam("Heuristics", 0.5)
model.setParam("TimeLimit", 600)
model._best_obj = -1
# Create variables and store them in a dictionary:
# m_vars[home_team, away_team, slot]
m_vars = dict()
model._vars = m_vars
for team1 in range(n_teams):
for team2 in range(n_teams):
if team1 == team2:
continue
for slot in range(n_slots):
m_vars[team1, team2, slot] = \
model.addVar(vtype=GRB.BINARY, name="x_" + str(team1) + "_" + str(team2) + "_" + str(slot))
# Add constraints that force each team to play against
# another team at most once per slot.
for team1 in range(n_teams):
for slot in range(n_slots):
model.addConstr(gp.quicksum([m_vars[team1, team2, slot] + m_vars[team2, team1, slot]
for team2 in range(n_teams) if team1 != team2]) <= 1)
# Add constraints that force each team to meet another
# team exactly once in a home game
for team1 in range(n_teams):
for team2 in range(n_teams):
if team1 == team2:
continue
model.addConstr(gp.quicksum([m_vars[team1, team2, slot] for slot in range(n_slots)]) == 1)
# If phased, the teams must play in separate intervals, i.e., once in each
# n_slots/2 interval.
if (prob.game_mode == "P"):
for team1 in range(n_teams):
for team2 in range(team1 + 1, n_teams):
model.addConstr(gp.quicksum([m_vars[team1,team2,slot] + m_vars[team2,team1,slot]
for slot in range(int(n_slots/2))]) <= 1)
model.addConstr(gp.quicksum([m_vars[team1,team2,slot] + m_vars[team2,team1,slot]
for slot in range(int(n_slots/2), n_slots)]) <= 1)
# Add problem specific constraints
for (ind, (c_name, constraint)) in enumerate(prob.constraints):
if c_name == "CA2":
slots = [int(s) for s in constraint["slots"].split(';')]
teams1 = [int(t) for t in constraint["teams1"].split(';')]
teams2 = [int(t) for t in constraint["teams2"].split(';')]
c_min = int(constraint["min"])
c_max = int(constraint["max"])
penalty = int(constraint["penalty"])
if (c_min > 0):
raise Exception("Min value in CA2 not implemented!")
for team in teams1:
if constraint["type"] == "HARD":
if constraint["mode1"] == "A":
constr = model.addConstr(gp.quicksum([m_vars[i, team, j]
for i in teams2 if i != team
for j in slots]) <= c_max,
name="CA2_" + str(team) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
elif constraint["mode1"] == "H":
constr = model.addConstr(gp.quicksum([m_vars[team, i, j]
for i in teams2 if i != team
for j in slots]) <= c_max,
name="CA2_" + str(team) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
else:
constr = model.addConstr(gp.quicksum([m_vars[team, i, j]
for i in teams2 if i != team
for j in slots]) +
gp.quicksum([m_vars[i, team, j]
for i in teams2 if i != team
for j in slots]) <= c_max,
name="CA2_" + str(team) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
elif not skipSoft:
slack = model.addVar(vtype=GRB.INTEGER, obj=penalty)
if constraint["mode1"] == "A":
constr = model.addConstr(gp.quicksum([m_vars[i, team, j]
for i in teams2 if i != team
for j in slots]) - slack <= c_max,
name="CA2_" + str(team) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
elif constraint["mode1"] == "H":
constr = model.addConstr(gp.quicksum([m_vars[team, i, j]
for i in teams2 if i != team
for j in slots]) - slack <= c_max,
name="CA2_" + str(team) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
else:
constr = model.addConstr(gp.quicksum([m_vars[team, i, j]
for i in teams2 if i != team
for j in slots]) +
gp.quicksum([m_vars[i, team, j]
for i in teams2 if i != team
for j in slots]) - slack <= c_max,
name="CA2_" + str(team) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
if c_name == "CA3":
teams1 = [int(t) for t in constraint["teams1"].split(';')]
teams2 = [int(t) for t in constraint["teams2"].split(';')]
c_min = int(constraint["min"])
c_max = int(constraint["max"])
intp = int(constraint["intp"])
penalty = int(constraint["penalty"])
if (c_min > 0):
raise Exception("Min value in CA3 not implemented!")
for team in teams1:
if constraint["type"] == "HARD":
if constraint["mode1"] == "A":
for slots in [range(z, z + intp) for z in range(n_slots - intp + 1)]:
constr = model.addConstr(gp.quicksum([m_vars[i, team, j]
for i in teams2 if i != team
for j in slots]) <= c_max,
name="CA3_" + str(team) + "_" + str(slots[0]) + "_" + str(slots[-1]) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
elif constraint["mode1"] == "H":
for slots in [range(z, z + intp) for z in range(n_slots - intp + 1)]:
constr = model.addConstr(gp.quicksum([m_vars[team, i, j]
for i in teams2 if i != team
for j in slots]) <= c_max,
name="CA3_" + str(team) + "_" + str(slots[0]) + "_" + str(slots[-1]) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
else:
for slots in [range(z, z + intp) for z in range(n_slots - intp + 1)]:
constr = model.addConstr(gp.quicksum([m_vars[team, i, j]
for i in teams2 if i != team
for j in slots]) +
gp.quicksum([m_vars[i, team, j]
for i in teams2 if i != team
for j in slots]) <= c_max,
name="CA3_" + str(team) + "_" + str(slots[0]) + "_" + str(slots[-1]) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
elif not skipSoft:
if constraint["mode1"] == "A":
for slots in [range(z, z + intp) for z in range(n_slots - intp + 1)]:
slack = model.addVar(vtype=GRB.INTEGER, obj=penalty)
constr = model.addConstr(gp.quicksum([m_vars[i, team, j]
for i in teams2 if i != team
for j in slots]) - slack <= c_max,
name="CA3_" + str(team) + "_" + str(slots[0]) + "_" + str(slots[-1]) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
elif constraint["mode1"] == "H":
for slots in [range(z, z + intp) for z in range(n_slots - intp + 1)]:
slack = model.addVar(vtype=GRB.INTEGER, obj=penalty)
constr = model.addConstr(gp.quicksum([m_vars[team, i, j]
for i in teams2 if i != team
for j in slots]) - slack <= c_max,
name="CA3_" + str(team) + "_" + str(slots[0]) + "_" + str(slots[-1]) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
else:
for slots in [range(z, z + intp) for z in range(n_slots - intp + 1)]:
slack = model.addVar(vtype=GRB.INTEGER, obj=penalty)
constr = model.addConstr(gp.quicksum([m_vars[team, i, j]
for i in teams2 if i != team
for j in slots]) +
gp.quicksum([m_vars[i, team, j]
for i in teams2 if i != team
for j in slots]) - slack <= c_max,
name="CA3_" + str(team) + "_" + str(slots[0]) + "_" + str(slots[-1]) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
if c_name == "CA4":
slots = [int(s) for s in constraint["slots"].split(';')]
teams1 = [int(t) for t in constraint["teams1"].split(';')]
teams2 = [int(t) for t in constraint["teams2"].split(';')]
c_min = int(constraint["min"])
c_max = int(constraint["max"])
penalty = int(constraint["penalty"])
if (c_min > 0):
raise Exception("Min value in CA4 not implemented!")
if constraint["type"] == "HARD":
if constraint["mode1"] == "A":
if constraint["mode2"] == "GLOBAL":
constr = model.addConstr(gp.quicksum([m_vars[i, j, z]
for i in teams2
for j in teams1 if i != j
for z in slots]) <= c_max,
name="CA4_" + str(ind))
if lazy:
constr.Lazy = lazy
else:
for slot in slots:
constr = model.addConstr(gp.quicksum([m_vars[i, j, slot]
for i in teams2
for j in teams1 if i != j]) <= c_max,
name="CA4_" + str(slot) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
elif constraint["mode1"] == "H":
if constraint["mode2"] == "GLOBAL":
constr = model.addConstr(gp.quicksum([m_vars[i, j, z]
for i in teams1
for j in teams2 if i != j
for z in slots]) <= c_max,
name="CA4_" + str(ind))
if lazy:
constr.Lazy = lazy
else:
for slot in slots:
constr = model.addConstr(gp.quicksum([m_vars[i, j, slot]
for i in teams1
for j in teams2 if i != j]) <= c_max,
name="CA4_" + str(slot) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
else:
if constraint["mode2"] == "GLOBAL":
constr = model.addConstr(gp.quicksum([m_vars[i, j, z]
for i in teams1
for j in teams2 if i != j
for z in slots]) +
gp.quicksum([m_vars[i, j, z]
for i in teams2
for j in teams1 if i != j
for z in slots]) <= c_max,
name="CA4_" + str(ind))
if lazy:
constr.Lazy = lazy
else:
for slot in slots:
constr = model.addConstr(gp.quicksum([m_vars[i, j, slot]
for i in teams1
for j in teams2 if i != j]) +
gp.quicksum([m_vars[i, j, slot]
for i in teams2
for j in teams1 if i != j]) <= c_max,
name="CA4_" + str(slot) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
elif not skipSoft:
if constraint["mode1"] == "A":
if constraint["mode2"] == "GLOBAL":
slack = model.addVar(vtype=GRB.INTEGER, obj=penalty)
constr = model.addConstr(gp.quicksum([m_vars[i, j, z]
for i in teams2
for j in teams1 if i != j
for z in slots]) - slack <= c_max,
name="CA4_" + str(ind))
if lazy:
constr.Lazy = lazy
else:
for slot in slots:
slack = model.addVar(vtype=GRB.INTEGER, obj=penalty, name="sCA4_" + str(slot) + "_" + str(ind))
constr = model.addConstr(gp.quicksum([m_vars[i, j, slot]
for i in teams2
for j in teams1 if i != j]) - slack <= c_max,
name="CA4_" + str(slot) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
elif constraint["mode1"] == "H":
if constraint["mode2"] == "GLOBAL":
slack = model.addVar(vtype=GRB.INTEGER, obj=penalty)
constr = model.addConstr(gp.quicksum([m_vars[i, j, z]
for i in teams1
for j in teams2 if i != j
for z in slots]) - slack<= c_max,
name="CA4_" + str(ind))
if lazy:
constr.Lazy = lazy
else:
for slot in slots:
slack = model.addVar(vtype=GRB.INTEGER, obj=penalty, name="sCA4_" + str(slot) + "_" + str(ind))
constr = model.addConstr(gp.quicksum([m_vars[i, j, slot]
for i in teams1
for j in teams2 if i != j]) - slack <= c_max,
name="CA4_" + str(slot) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
else:
if constraint["mode2"] == "GLOBAL":
slack = model.addVar(vtype=GRB.INTEGER, obj=penalty)
constr = model.addConstr(gp.quicksum([m_vars[i, j, z]
for i in teams1
for j in teams2 if i != j
for z in slots]) +
gp.quicksum([m_vars[i, j, z]
for i in teams2
for j in teams1 if i != j
for z in slots]) - slack <= c_max,
name="CA4_" + str(ind))
if lazy:
constr.Lazy = lazy
else:
for slot in slots:
slack = model.addVar(vtype=GRB.INTEGER, obj=penalty)
constr = model.addConstr(gp.quicksum([m_vars[i, j, slot]
for i in teams1
for j in teams2 if i != j]) +
gp.quicksum([m_vars[i, j, slot]
for i in teams2
for j in teams1 if i != j]) - slack <= c_max,
name="CA4_" + str(slot) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
# Game constraints
if c_name == "GA1":
slots = [int(s) for s in constraint["slots"].split(';')]
games = [(int(t.split(',')[0]),int(t.split(',')[1])) for t in constraint["meetings"].split(';') if len(t) > 0]
c_min = int(constraint["min"])
c_max = int(constraint["max"])
penalty = int(constraint["penalty"])
if constraint["type"] == "HARD":
constr = model.addConstr(gp.quicksum([m_vars[i, j, slot]
for i,j in games
for slot in slots]) <= c_max,
name="GA1_max_" + str(slot) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
constr = model.addConstr(gp.quicksum([m_vars[i, j, slot]
for i,j in games
for slot in slots]) >= c_min,
name="GA1_min_" + str(slot) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
elif not skipSoft:
slack_plus = model.addVar(vtype=GRB.INTEGER, obj=penalty)
constr = model.addConstr(gp.quicksum([m_vars[i, j, slot]
for i,j in games
for slot in slots]) - slack_plus <= c_max,
name="GA1_max_" + str(slot) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
slack_minus = model.addVar(vtype=GRB.INTEGER, obj=penalty)
constr = model.addConstr(gp.quicksum([m_vars[i, j, slot]
for i,j in games
for slot in slots]) + slack_minus >= c_min,
name="GA1_min_" + str(slot) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
# Separation constraints
if c_name == "SE1":
teams = [int(t) for t in constraint["teams"].split(';')]
penalty = int(constraint["penalty"])
c_min = int(constraint["min"])
if constraint["type"] == "HARD":
raise Exception("The HARD version of constraint SE1 is not implemented!")
elif not skipSoft:
for i in range(len(teams)):
for j in range(i + 1, len(teams)):
sepc_var = model.addVar(vtype=GRB.INTEGER, name="sep_" + str(teams[i]) + "_" + str(teams[j]))
min1_var = model.addVar(vtype=GRB.BINARY, name="min1_" + str(teams[i]) + "_" + str(teams[j]))
min2_var = model.addVar(vtype=GRB.BINARY, name="min2_" + str(teams[i]) + "_" + str(teams[j]))
slack = model.addVar(vtype=GRB.INTEGER, obj=penalty)
constr = model.addConstr(sepc_var - slack <= - c_min - 1 + n_slots)
if lazy:
constr.Lazy = lazy
constr = model.addConstr(gp.quicksum([slot * m_vars[teams[i],teams[j],slot]
for slot in range(n_slots)]) - \
gp.quicksum([slot * m_vars[teams[j],teams[i],slot]
for slot in range(n_slots)]) + n_slots <= sepc_var + min1_var * 2 * n_slots,
name="SE1_1_" + str(teams[i]) + "_" + str(teams[j]) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
constr = model.addConstr(gp.quicksum([slot * m_vars[teams[j],teams[i],slot]
for slot in range(n_slots)]) - \
gp.quicksum([slot * m_vars[teams[i],teams[j],slot]
for slot in range(n_slots)]) + n_slots <= sepc_var + min2_var * 2 * n_slots,
name="SE1_2_" + str(teams[i]) + "_" + str(teams[j]) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
constr = model.addConstr(min1_var + min2_var == 1, name="SE1_3_" + str(teams[i]) + "_" + str(teams[j]) + "_" + str(ind))
if lazy:
constr.Lazy = lazy
return model
def write_status(model: gp.Model):
# Displays the status of Gurobi in a more human readable format
if model.status == GRB.OPTIMAL:
print('>>>> Slave: Optimal objective: %g' % model.objVal)
elif model.status == GRB.INF_OR_UNBD:
print('>>>> Slave: Model is infeasible or unbounded')
elif model.status == GRB.INFEASIBLE:
print('>>>> Slave: Model is infeasible')
#model.write("infeasible.lp")
#raise Exception("Infeasilbe!")
elif model.status == GRB.UNBOUNDED:
print('>>>> Slave: Model is unbounded')
elif model.status == GRB.TIME_LIMIT:
print('>>>> Slave: Reached time limit')
else:
print('>>>> Slave: Optimization ended with status %d' % model.status)
def make_solution(m_vars, n_teams, n_slots):
# Computes the solution from the binary variables of the model
solution = []
for slot in range(n_slots):
games = []
for team1 in range(n_teams):
for team2 in range(n_teams):
if team1 == team2:
continue
var_value = m_vars[team1, team2, slot]
if isinstance(var_value, gp.Var):
var_value = var_value.x
if var_value > 0.5:
games.append((team1, team2))
solution.append(games)
return solution
def print_solution(solution):
# Displays the solution in a more human readble format
for slot,games in enumerate(solution):
print("Slot " + str(slot) + ":")
for h,a in games:
print("({},{})".format(str(h), str(a)), end=' ')
print("")