def vne(datafile):
# Input data. If no file is given on the command line then use a
# default file name. The data read is
# width - the width of the the roll,
# size - the sie of each strip,
# amount - the demand for each strip.
SN, VNN, VNR = read_dat_file(datafile)
SN_N = SN[1];
print (SN_N);
def __init__(self, filename):
# read the data in filename
self.arcCost = read_dat_file(filename)[0]
self.numNodes = len(self.arcCost)
# check data consistency
for i in range(self.numNodes):
if len(self.arcCost[i]) != self.numNodes:
print("ERROR: Data file '%s' contains inconsistent data\n" % filename)
raise Exception("data file error")
self.arcCost[i][i] = 0.0
def __init__(self, filename):
# read the data in filename
self.arcCost = read_dat_file(filename)[0]
self.numNodes = len(self.arcCost)
# check data consistency
for i in range(self.numNodes):
if len(self.arcCost[i]) != self.numNodes:
print "ERROR: Data file '%s' contains inconsistent data\n" % filename
raise Exception("data file error")
self.arcCost[i][i] = 0.0
def rates(datafile):
# Read in data file. If no file name is given on the command line
# we use a default file name. The data we read is
# min -- a list/array of minimal power production of generators.
# max -- a list/array of maximal power production of generators.
# cost -- a list/array of cost for using a generator
# demand -- the total power demand.
# The arrays are all of equal length and each entry corresponds to
# a generator.
min, max, cost, demand = read_dat_file(datafile)
generators = list(range(len(min))) # List of generators
# Create a new (empty) model and populate it below.
model = cplex.Cplex()
# Create one variable for each generator. The variables model the amount
# of power generated by the respective generator. They are of type
# semi-continuous because a generator g produces at least min[g] if it
# is switched on.
model.variables.add(obj=cost, lb=min, ub=max)
model.variables.set_types(
zip(generators,
len(generators) * [model.variables.type.semi_continuous]))
# Require that the sum of the production of all generators
# satisfies the demand.
totalproduction = cplex.SparsePair(ind=generators,
val=[1.0] * len(generators))
model.linear_constraints.add(lin_expr=[totalproduction],
senses=["G"],
rhs=[demand])
# Our objective is to minimize cost. Cost per unit of power generated
# was already set when variables were created.
model.objective.set_sense(model.objective.sense.minimize)
# Eport the model to disk and solve.
try:
model.write("rates.lp")
model.solve()
except CplexSolverError as e:
print("Exception raised during solve: " + e)
else:
# Solve succesful, dump solution.
print("Solution status = ", model.solution.get_status())
for g in generators:
print(" generator " + str(g) + ": " +
str(model.solution.get_values(g)))
print("Total cost = " + str(model.solution.get_objective_value()))
def rates(datafile):
# Read in data file. If no file name is given on the command line
# we use a default file name. The data we read is
# min -- a list/array of minimal power production of generators.
# max -- a list/array of maximal power production of generators.
# cost -- a list/array of cost for using a generator
# demand -- the total power demand.
# The arrays are all of equal length and each entry corresponds to
# a generator.
min, max, cost, demand = read_dat_file(datafile)
generators = list(range(len(min))) # List of generators
# Create a new (empty) model and populate it below.
model = cplex.Cplex()
# Create one variable for each generator. The variables model the amount
# of power generated by the respective generator. They are of type
# semi-continuous because a generator g produces at least min[g] if it
# is switched on.
model.variables.add(obj=cost, lb=min, ub=max)
model.variables.set_types(zip(generators,
len(generators) *
[model.variables.type.semi_continuous]))
# Require that the sum of the production of all generators
# satisfies the demand.
totalproduction = cplex.SparsePair(ind=generators,
val=[1.0] * len(generators))
model.linear_constraints.add(lin_expr=[totalproduction],
senses=["G"],
rhs=[demand])
# Our objective is to minimize cost. Cost per unit of power generated
# was already set when variables were created.
model.objective.set_sense(model.objective.sense.minimize)
# Eport the model to disk and solve.
try:
model.write("rates.lp")
model.solve()
except CplexSolverError as e:
print("Exception raised during solve: " + e)
else:
# Solve succesful, dump solution.
print("Solution status = ", model.solution.get_status())
for g in generators:
print(" generator " + str(g) + ": " +
str(model.solution.get_values(g)))
print("Total cost = " + str(model.solution.get_objective_value()))
def __init__(self, filename):
# read the data in diet.dat
(self.foodCost, self.foodMin, self.foodMax, self.nutrMin,
self.nutrMax, self.nutrPer) = read_dat_file("../../data/diet.dat")
# check data consistency
if (len(self.foodCost) != len(self.foodMin) or
len(self.foodCost) != len(self.foodMax) or
len(self.nutrMin) != len(self.nutrMax) or
len(self.nutrMin) != len(self.nutrPer)):
print("ERROR: Data file '%s' contains inconsistent data\n" % filename)
raise Exception("data file error")
for np in self.nutrPer:
if len(self.foodCost) != len(np):
print("ERROR: Data file '%s' contains inconsistent data\n" % filename)
raise Exception("data file error")
def __init__(self, filename):
# read the data in diet.dat
self.foodCost, self.foodMin, self.foodMax, self.nutrMin, \
self.nutrMax, self.nutrPer = \
read_dat_file("../../data/diet.dat")
# check data consistency
if len(self.foodCost) != len(self.foodMin) or \
len(self.foodCost) != len(self.foodMax) or \
len(self.nutrMin) != len(self.nutrMax) or \
len(self.nutrMin) != len(self.nutrPer):
print "ERROR: Data file '%s' contains inconsistent data\n" % filename
raise Exception("data file error")
for np in self.nutrPer:
if len(self.foodCost) != len(np):
print "ERROR: Data file '%s' contains inconsistent data\n" % filename
raise Exception("data file error")
if __name__ == "__main__":
# Read in data file. If no file name is given on the command line
# we use a default file name. The data we read is
# min -- a list/array of minimal power production of generators.
# max -- a list/array of maximal power production of generators.
# cost -- a list/array of cost for using a generator
# demand -- the total power demand.
# The arrays are all of equal length and each entry corresponds to
# a generator.
datafile = "../../../examples/data/rates.dat"
if len(sys.argv) < 2:
print "Default data file : " + datafile
else:
datafile = sys.argv[1]
min, max, cost, demand = read_dat_file(datafile)
generators = range(len(min)) # List of generators
# Create a new (empty) model and populate it below.
model = cplex.Cplex()
# Create one variable for each generator. The variables model the amount
# of power generated by the respective generator. They are of type
# semi-continuous because a generator g produces at least min[g] if it
# is switched on.
model.variables.add(obj = cost, lb = min, ub = max)
model.variables.set_types(zip(generators,
len(generators) *
[model.variables.type.semi_continuous]))
# Require that the sum of the production of all generators
def facility():
# Read in data file. If no file name is given on the command line
# we use a default file name. The data we read is
# capacity -- a list/array of facility capacity
# fixedcost -- a list/array of facility fixed cost
# cost -- a matrix for the costs to serve each client by each facility
datafile = "../../../examples/data/facility.dat"
if len(sys.argv) < 2:
print("Default data file : " + datafile)
else:
datafile = sys.argv[1]
capacity, fixedcost, cost = read_dat_file(datafile)
num_facilities = len(fixedcost)
num_clients = len(cost)
# Create a new (empty) model and populate it below.
model = cplex.Cplex()
# Create one binary variable for each facility. The variables model
# whether each facility is open or not
model.variables.add(obj = fixedcost,
lb = [0] * num_facilities,
ub = [1] * num_facilities,
types = ["B"] * num_facilities)
# Create one binary variable for each facility/client pair. The variables
# model whether a client is served by a facility.
for c in range(num_clients):
model.variables.add(obj = cost[c],
lb = [0] * num_facilities,
ub = [1] * num_facilities,
types = ["B"] * num_facilities)
# Create corresponding indices for later use
supply = []
for c in range(num_clients):
supply.append([])
for f in range(num_facilities):
supply[c].append((c+1)*(num_facilities)+f)
# Equivalently, supply can be defined by list comprehension
# supply = [[(c + 1) * num_facilities + f
# for f in range(num_facilities)] for c in range(num_clients)]
# Each client must be assigned to exactly one location
for c in range(num_clients):
assignment_constraint = cplex.SparsePair(ind = [supply[c][f] for f in \
range(num_facilities)],
val = [1.0] * num_facilities)
model.linear_constraints.add(lin_expr = [assignment_constraint],
senses = ["E"],
rhs = [1]);
# The number of clients assigned to a facility must be less than the
# capacity of the facility, and clients must be assigned to an open facility
for f in range(num_facilities):
index = [f]
value = [-capacity[f]]
for c in range(num_clients):
index.append(supply[c][f])
value.append(1.0)
capacity_constraint = cplex.SparsePair(ind = index, val = value)
model.linear_constraints.add(lin_expr = [capacity_constraint],
senses = ["L"],
rhs = [0]);
# Our objective is to minimize cost. Fixed and variable costs
# have been set when variables were created.
model.objective.set_sense(model.objective.sense.minimize)
# Solve
try:
model.solve()
except CplexSolverError as e:
print("Exception raised during solve: " + e)
else:
solution = model.solution
# solution.get_status() returns an integer code
print("Solution status = " , solution.get_status(), ":", end=' ')
# the following line prints the corresponding string
print(solution.status[solution.get_status()])
# Display solution.
print("Total cost = " , solution.get_objective_value())
for f in range(num_facilities):
if (solution.get_values(f) >
model.parameters.mip.tolerances.integrality.get()):
print("Facility %d is open and serves the following clients:" % f, end=' ')
for c in range(num_clients):
if (solution.get_values(supply[c][f]) >
model.parameters.mip.tolerances.integrality.get()):
print(c, end=' ')
print()
def facility(datafile):
# Read in data file. If no file name is given on the command line
# we use a default file name. The data we read is
# capacity -- a list/array of facility capacity
# fixedcost -- a list/array of facility fixed cost
# cost -- a matrix for the costs to serve each client by each
# facility
capacity, fixedcost, cost = read_dat_file(datafile)
num_facilities = len(fixedcost)
num_clients = len(cost)
# Create a new (empty) model and populate it below.
model = cplex.Cplex()
# Create one binary variable for each facility. The variables model
# whether each facility is open or not
model.variables.add(obj=fixedcost,
lb=[0] * num_facilities,
ub=[1] * num_facilities,
types=["B"] * num_facilities)
# Create one binary variable for each facility/client pair. The variables
# model whether a client is served by a facility.
for c in range(num_clients):
model.variables.add(obj=cost[c],
lb=[0] * num_facilities,
ub=[1] * num_facilities,
types=["B"] * num_facilities)
# Create corresponding indices for later use
supply = []
for c in range(num_clients):
supply.append([])
for f in range(num_facilities):
supply[c].append((c + 1) * (num_facilities) + f)
# Equivalently, supply can be defined by list comprehension
# supply = [[(c + 1) * num_facilities + f
# for f in range(num_facilities)] for c in range(num_clients)]
# Each client must be assigned to exactly one location
for c in range(num_clients):
assignment_constraint = cplex.SparsePair(
ind=[supply[c][f] for f in range(num_facilities)],
val=[1.0] * num_facilities)
model.linear_constraints.add(lin_expr=[assignment_constraint],
senses=["E"],
rhs=[1])
# The number of clients assigned to a facility must be less than the
# capacity of the facility, and clients must be assigned to an open
# facility
for f in range(num_facilities):
index = [f]
value = [-capacity[f]]
for c in range(num_clients):
index.append(supply[c][f])
value.append(1.0)
capacity_constraint = cplex.SparsePair(ind=index, val=value)
model.linear_constraints.add(lin_expr=[capacity_constraint],
senses=["L"],
rhs=[0])
# Our objective is to minimize cost. Fixed and variable costs
# have been set when variables were created.
model.objective.set_sense(model.objective.sense.minimize)
# Solve
try:
model.solve()
except CplexSolverError as e:
print("Exception raised during solve: " + e)
else:
solution = model.solution
# solution.get_status() returns an integer code
print("Solution status = ", solution.get_status(), ":", end=' ')
# the following line prints the corresponding string
print(solution.status[solution.get_status()])
# Display solution.
print("Total cost = ", solution.get_objective_value())
for f in range(num_facilities):
if (solution.get_values(f) >
model.parameters.mip.tolerances.integrality.get()):
print("Facility %d is open and serves the "
"following clients:" % f,
end=' ')
for c in range(num_clients):
if (solution.get_values(supply[c][f]) >
model.parameters.mip.tolerances.integrality.get()):
print(c, end=' ')
print()
for v in range(cut.variables.get_num()):
print " Cut" + str(v) + " = " + str(cut.solution.get_values(v))
if __name__ == "__main__":
# Input data. If no file is given on the command line then use a
# default file name. The data read is
# width - the width of the the roll,
# size - the sie of each strip,
# amount - the demand for each strip.
datafile = "data/cutstock.dat"
if len(sys.argv) < 2:
print "Default data file : " + datafile
else:
datafile = sys.argv[1]
width, size, amount = read_dat_file(datafile)
# Setup cutting optimization (master) problem.
# This is the problem to which columns will be added in the loop
# below.
cut = cplex.Cplex()
cutcons = range(len(amount)) # constraint indices
cutvars = range(len(size)) # variable indices
cut.variables.add(obj = [1] * len(cutvars))
# Add constraints. They have empty left-hand side initially. The
# left-hand side is filled in the next loop.
cut.linear_constraints.add(lin_expr = [SparsePair()] * len(cutcons),
senses = ["G"] * len(cutcons),
rhs = amount)
for v in cutvars:
def cutstock(datafile):
# Input data. If no file is given on the command line then use a
# default file name. The data read is
# width - the width of the the roll,
# size - the sie of each strip,
# amount - the demand for each strip.
width, size, amount = read_dat_file(datafile)
# Setup cutting optimization (master) problem.
# This is the problem to which columns will be added in the loop
# below.
cut = cplex.Cplex()
cutcons = list(range(len(amount))) # constraint indices
cutvars = list(range(len(size))) # variable indices
cut.variables.add(obj=[1] * len(cutvars))
# Add constraints. They have empty left-hand side initially. The
# left-hand side is filled in the next loop.
cut.linear_constraints.add(lin_expr=[SparsePair()] * len(cutcons),
senses=["G"] * len(cutcons),
rhs=amount)
for v in cutvars:
cut.linear_constraints.set_coefficients(v, v, int(width / size[v]))
# Setup pattern generation (worker) problem.
# The constraints and variables in this problem always stay the same
# but the objective function will change during the column generation
# loop.
pat = cplex.Cplex()
use = list(range(len(size))) # variable indices
pat.variables.add(types=[pat.variables.type.integer] * len(use))
# Add a constant 1 to the objective.
pat.variables.add(obj=[1], lb=[1], ub=[1])
# Single constraint: total size must not exceed the width.
totalsize = SparsePair(ind=use, val=size)
pat.linear_constraints.add(lin_expr=[totalsize], senses=["L"], rhs=[width])
pat.objective.set_sense(pat.objective.sense.minimize)
# Column generation procedure
while True:
# Optimize over current patterns
cut.solve()
report1(cut)
# Find and add new pattern. The objective function of the
# worker problem is constructed from the dual values of the
# constraints of the master problem.
price = [-d for d in cut.solution.get_dual_values(cutcons)]
pat.objective.set_linear(list(zip(use, price)))
pat.solve()
report2(pat, use)
# If reduced cost (worker problem objective function value) is
# non-negative we are optimal. Otherwise we found a new column
# to be added. Coefficients of the new column are given by the
# optimal solution vector to the worker problem.
if pat.solution.get_objective_value() > -RC_EPS:
break
newpat = pat.solution.get_values(use)
# The new pattern constitutes a new variable in the cutting
# optimization problem. Create that variable and add it to all
# constraints with the coefficients read from the optimal solution
# of the pattern generation problem.
idx = cut.variables.get_num()
cut.variables.add(obj=[1.0])
cut.linear_constraints.set_coefficients(
list(zip(cutcons, [idx] * len(use), newpat)))
cutvars.append(idx)
# Perform a final solve on the cutting optimization problem.
# Turn all variables into integers before doing that.
cut.variables.set_types(
list(zip(cutvars, [cut.variables.type.integer] * len(cutvars))))
cut.solve()
report3(cut)
print("Solution status = ", cut.solution.get_status())
def cutstock(datafile):
# Input data. If no file is given on the command line then use a
# default file name. The data read is
# width - the width of the the roll,
# size - the sie of each strip,
# amount - the demand for each strip.
width, size, amount = read_dat_file(datafile)
# Setup cutting optimization (master) problem.
# This is the problem to which columns will be added in the loop
# below.
cut = cplex.Cplex()
cutcons = list(range(len(amount))) # constraint indices
cutvars = list(range(len(size))) # variable indices
cut.variables.add(obj=[1] * len(cutvars))
# Add constraints. They have empty left-hand side initially. The
# left-hand side is filled in the next loop.
cut.linear_constraints.add(lin_expr=[SparsePair()] * len(cutcons),
senses=["G"] * len(cutcons),
rhs=amount)
for v in cutvars:
cut.linear_constraints.set_coefficients(v, v, int(width / size[v]))
# Setup pattern generation (worker) problem.
# The constraints and variables in this problem always stay the same
# but the objective function will change during the column generation
# loop.
pat = cplex.Cplex()
use = list(range(len(size))) # variable indices
pat.variables.add(types=[pat.variables.type.integer] * len(use))
# Add a constant 1 to the objective.
pat.variables.add(obj=[1], lb=[1], ub=[1])
# Single constraint: total size must not exceed the width.
totalsize = SparsePair(ind=use, val=size)
pat.linear_constraints.add(lin_expr=[totalsize],
senses=["L"],
rhs=[width])
pat.objective.set_sense(pat.objective.sense.minimize)
# Column generation procedure
while True:
# Optimize over current patterns
cut.solve()
report1(cut)
# Find and add new pattern. The objective function of the
# worker problem is constructed from the dual values of the
# constraints of the master problem.
price = [-d for d in cut.solution.get_dual_values(cutcons)]
pat.objective.set_linear(list(zip(use, price)))
pat.solve()
report2(pat, use)
# If reduced cost (worker problem objective function value) is
# non-negative we are optimal. Otherwise we found a new column
# to be added. Coefficients of the new column are given by the
# optimal solution vector to the worker problem.
if pat.solution.get_objective_value() > -RC_EPS:
break
newpat = pat.solution.get_values(use)
# The new pattern constitutes a new variable in the cutting
# optimization problem. Create that variable and add it to all
# constraints with the coefficients read from the optimal solution
# of the pattern generation problem.
idx = cut.variables.get_num()
cut.variables.add(obj=[1.0])
cut.linear_constraints.set_coefficients(list(zip(cutcons,
[idx] * len(use),
newpat)))
cutvars.append(idx)
# Perform a final solve on the cutting optimization problem.
# Turn all variables into integers before doing that.
cut.variables.set_types(
list(zip(cutvars, [cut.variables.type.integer] * len(cutvars))))
cut.solve()
report3(cut)
print("Solution status = ", cut.solution.get_status())
if __name__ == "__main__":
# Read in data file. If no file name is given on the command line
# we use a default file name. The data we read is
# min -- a list/array of minimal power production of generators.
# max -- a list/array of maximal power production of generators.
# cost -- a list/array of cost for using a generator
# demand -- the total power demand.
# The arrays are all of equal length and each entry corresponds to
# a generator.
datafile = "../../../examples/data/rates.dat"
if len(sys.argv) < 2:
print("Default data file : " + datafile)
else:
datafile = sys.argv[1]
min, max, cost, demand = read_dat_file(datafile)
generators = list(range(len(min))) # List of generators
# Create a new (empty) model and populate it below.
model = cplex.Cplex()
# Create one variable for each generator. The variables model the amount
# of power generated by the respective generator. They are of type
# semi-continuous because a generator g produces at least min[g] if it
# is switched on.
model.variables.add(obj=cost, lb=min, ub=max)
model.variables.set_types(
zip(generators,
len(generators) * [model.variables.type.semi_continuous]))
# Require that the sum of the production of all generators
def etsp(datafile):
# Read in data file. If no file name is given on the command line
# we use a default file name. The data we read is
# activityOnAResource --
# An array of arrays represents the resources
# required for each activity of a job
# duration --
# An array of arrays represents the duration required
# for each activity of a job
# jobDueDate --
# contains the due date for each job
# jobEarlinessCost --
# contains the penalty for being too early for each job
# jobTardinessCost --
# contains the penalty for being too late for each job
(activityOnAResource, duration, jobDueDate, jobEarlinessCost,
jobTardinessCost) = read_dat_file(datafile)
nbJob = len(jobDueDate)
nbResource = len(activityOnAResource[1])
def starttime(job, res):
return job * nbJob + res
try:
# Build model
model = cplex.Cplex()
model.objective.set_sense(model.objective.sense.minimize)
# Add activity start time variables
nbStart = nbJob * nbResource
obj = [0.0] * nbStart
lb = [0.0] * nbStart
ub = [10000.0] * nbStart
model.variables.add(obj, lb, ub)
# State precedence constraints
# starttime(i, j) - starttime(i, j-1) >= duration(i, j-1)
for i in range(nbJob):
for j in range(1, nbResource):
ind = [starttime(i, j), starttime(i, j - 1)]
val = [1.0, -1.0]
row = [[ind, val]]
model.linear_constraints.add(lin_expr=row,
senses="G",
rhs=[duration[i][j - 1]])
# Add indicator variables
nIndicatorVars = nbResource * nbJob * (nbJob - 1)
colname_ind = ["ind" + str(j + 1) for j in range(nIndicatorVars)]
obj = [0.0] * nIndicatorVars
lb = [0.0] * nIndicatorVars
ub = [1.0] * nIndicatorVars
types = ["B"] * nIndicatorVars
model.variables.add(obj, lb, ub, types, colname_ind)
# Add ind1 + ind2 >= 1
# ind3 + ind4 >= 1
# ind5 + ind6 >= 1
# ...
# constraints
j = nbStart
for i in range(nIndicatorVars // 2):
ind = [j, j + 1]
val = [1.0, 1.0]
row = [[ind, val]]
model.linear_constraints.add(lin_expr=row, senses="G", rhs=[1])
j = j + 2
# Add indicator constraints
# i1 = 1 <-> c1
# i2 = 1 <-> c2
index = 0
for i in range(nbResource):
e = nbJob - 1
for j in range(e):
activity1 = activityOnAResource[i][j]
for k in range(j + 1, nbJob):
activity2 = activityOnAResource[i][k]
ic_dict = {}
ic_dict["indvar"] = nbStart + index
ic_dict["lin_expr"] = cplex.SparsePair(
ind=[starttime(j, activity1),
starttime(k, activity2)],
val=[1.0, -1.0])
ic_dict["rhs"] = duration[k][activity2] / 1.0
ic_dict["sense"] = "G"
ic_dict["complemented"] = 0
# ind(nbStart + index) = 1 -> ...
# starttime(j, activity1) - starttime(k, activity2) >=
# duration(k, activity2)
model.indicator_constraints.add(**ic_dict)
ic_dict["sense"] = "L"
ic_dict["complemented"] = 1
# ind(nbStart + index) = 0 -> ...
# starttime(j, activity1) - starttime(k, activity2) <=
# duration(k, activity2)
model.indicator_constraints.add(**ic_dict)
ic_dict = {}
ic_dict["indvar"] = nbStart + index + 1
ic_dict["lin_expr"] = cplex.SparsePair(
ind=[starttime(k, activity2),
starttime(j, activity1)],
val=[1.0, -1.0])
ic_dict["rhs"] = duration[j][activity1] / 1.0
ic_dict["sense"] = "G"
ic_dict["complemented"] = 0
# ind(nbStart + index) = 1 -> ...
# starttime(k, activity2) - starttime(j, activity1) >=
# duration(j, activity1)
model.indicator_constraints.add(**ic_dict)
ic_dict["sense"] = "L"
ic_dict["complemented"] = 1
# ind(nbStart + index) = 0 -> ...
# starttime(k, activity2) - starttime(j, activity1) <=
# duration(j, activity1)
model.indicator_constraints.add(**ic_dict)
index = index + 2
# Add Objective function
# each job has a cost which contains jobEarlinessCost and
# jobTardinessCost, and get the index of Earliness variables,
# Tardiness variables and Endness variables
indexOfEarlinessVar = list(
range(model.variables.get_num(),
model.variables.get_num() + nbJob))
model.variables.add(obj=jobEarlinessCost)
indexOfTardinessVar = list(
range(model.variables.get_num(),
model.variables.get_num() + nbJob))
model.variables.add(obj=jobTardinessCost)
# Add finished time variables
indexOfEndnessVar = list(
range(model.variables.get_num(),
model.variables.get_num() + nbJob))
model.variables.add(obj=[0.0] * nbJob)
# Add constraints for each Job
# indexOfEndnessVar[i] - starttime(i, nbResource) =
# duration[i][nbResource]
for i in range(nbJob):
ind = [indexOfEndnessVar[i], starttime(i, nbResource - 1)]
val = [1.0, -1.0]
row = [[ind, val]]
model.linear_constraints.add(lin_expr=row,
senses="E",
rhs=[duration[i][nbResource - 1]])
# Add constraints for each Job
# jobDueDate[i] = \
# indexOfEndnessVar[i] + indexOfEarlinessVar[i] -
# indexOfTardinessVar[i]
for i in range(nbJob):
ind = [
indexOfEndnessVar[i], indexOfEarlinessVar[i],
indexOfTardinessVar[i]
]
val = [1.0, 1.0, -1.0]
row = [[ind, val]]
model.linear_constraints.add(lin_expr=row,
senses="E",
rhs=[jobDueDate[i]])
model.parameters.emphasis.mip = 4
model.solve()
model.write('etsp.sav')
except CplexError as exc:
print(exc)
return
# Display solution
print("Solution status = ", model.solution.get_status())
print("Optimal Value = ", model.solution.get_objective_value())
def facility():
# Read in data file. If no file name is given on the command line
# we use a default file name. The data we read is
# capacity -- a list/array of facility capacity
# fixedcost -- a list/array of facility fixed cost
# cost -- a matrix for the costs to serve each client by each facility
datafile = "../../../examples/data/facility.dat"
if len(sys.argv) < 2:
print "Default data file : " + datafile
else:
datafile = sys.argv[1]
capacity, fixedcost, cost = read_dat_file(datafile)
num_facilities = len(fixedcost)
num_clients = len(cost)
# Create a new (empty) model and populate it below.
model = cplex.Cplex()
# Create one binary variable for each facility. The variables model
# whether each facility is open or not
model.variables.add(obj=fixedcost,
lb=[0] * num_facilities,
ub=[1] * num_facilities,
types=["B"] * num_facilities)
# Create one binary variable for each facility/client pair. The variables
# model whether a client is served by a facility.
for c in range(num_clients):
model.variables.add(obj=cost[c],
lb=[0] * num_facilities,
ub=[1] * num_facilities,
types=["B"] * num_facilities)
# Create corresponding indices for later use
supply = []
for c in range(num_clients):
supply.append([])
for f in range(num_facilities):
supply[c].append((c + 1) * (num_facilities) + f)
# Equivalently, supply can be defined by list comprehension
# supply = [[(c + 1) * num_facilities + f
# for f in range(num_facilities)] for c in range(num_clients)]
# Each client must be assigned to exactly one location
for c in range(num_clients):
assignment_constraint = cplex.SparsePair(ind = [supply[c][f] for f in \
range(num_facilities)],
val = [1.0] * num_facilities)
model.linear_constraints.add(lin_expr=[assignment_constraint],
senses=["E"],
rhs=[1])
# The number of clients assigned to a facility must be less than the
# capacity of the facility, and clients must be assigned to an open facility
for f in range(num_facilities):
index = [f]
value = [-capacity[f]]
for c in range(num_clients):
index.append(supply[c][f])
value.append(1.0)
capacity_constraint = cplex.SparsePair(ind=index, val=value)
model.linear_constraints.add(lin_expr=[capacity_constraint],
senses=["L"],
rhs=[0])
# Our objective is to minimize cost. Fixed and variable costs
# have been set when variables were created.
model.objective.set_sense(model.objective.sense.minimize)
# Solve
try:
model.solve()
except CplexSolverError, e:
print "Exception raised during solve: " + e
datafile = defaultfile
if len(sys.argv) < 2:
print "Default data file : " + datafile
else:
datafile = sys.argv[1]
else:
prompt = """Enter the path to a data file:
The path must be entered as a string; e.g.
"../../../examples/data/etsp.dat".
Enter "" to use the default file.\n"""
user_file = input(prompt)
print "hello" + user_file
if user_file != "":
datafile = user_file
else:
datafile = defaultfile
activityOnAResource, duration, jobDueDate, jobEarlinessCost, jobTardinessCost = read_dat_file(datafile)
nbJob = len(jobDueDate)
nbResource = len(activityOnAResource[1])
def starttime(job, res):
return job * nbJob + res
etsp(datafile)
print "Default data file : " + datafile
else:
datafile = sys.argv[1]
else:
prompt = """Enter the path to a data file:
The path must be entered as a string; e.g.
"../../../examples/data/etsp.dat".
Enter "" to use the default file.\n"""
user_file = input(prompt)
print "hello" + user_file
if user_file != "":
datafile = user_file
else:
datafile = defaultfile
activityOnAResource, \
duration, \
jobDueDate, \
jobEarlinessCost, \
jobTardinessCost = \
read_dat_file(datafile)
nbJob = len (jobDueDate)
nbResource = len (activityOnAResource[1])
def starttime(job, res):
return job * nbJob + res
etsp(datafile)
def etsp(datafile):
# Read in data file. If no file name is given on the command line
# we use a default file name. The data we read is
# activityOnAResource --
# An array of arrays represents the resources
# required for each activity of a job
# duration --
# An array of arrays represents the duration required
# for each activity of a job
# jobDueDate --
# contains the due date for each job
# jobEarlinessCost --
# contains the penalty for being too early for each job
# jobTardinessCost --
# contains the penalty for being too late for each job
(activityOnAResource,
duration,
jobDueDate,
jobEarlinessCost,
jobTardinessCost) = read_dat_file(datafile)
nbJob = len(jobDueDate)
nbResource = len(activityOnAResource[1])
def starttime(job, res):
return job * nbJob + res
try:
# Build model
model = cplex.Cplex()
model.objective.set_sense(model.objective.sense.minimize)
# Add activity start time variables
nbStart = nbJob * nbResource
obj = [0.0] * nbStart
lb = [0.0] * nbStart
ub = [10000.0] * nbStart
model.variables.add(obj, lb, ub)
# State precedence constraints
# starttime(i, j) - starttime(i, j-1) >= duration(i, j-1)
for i in range(nbJob):
for j in range(1, nbResource):
ind = [starttime(i, j), starttime(i, j - 1)]
val = [1.0, -1.0]
row = [[ind, val]]
model.linear_constraints.add(
lin_expr=row, senses="G", rhs=[duration[i][j - 1]])
# Add indicator variables
nIndicatorVars = nbResource * nbJob * (nbJob - 1)
colname_ind = ["ind" + str(j + 1) for j in range(nIndicatorVars)]
obj = [0.0] * nIndicatorVars
lb = [0.0] * nIndicatorVars
ub = [1.0] * nIndicatorVars
types = ["B"] * nIndicatorVars
model.variables.add(obj, lb, ub, types, colname_ind)
# Add ind1 + ind2 >= 1
# ind3 + ind4 >= 1
# ind5 + ind6 >= 1
# ...
# constraints
j = nbStart
for i in range(nIndicatorVars // 2):
ind = [j, j + 1]
val = [1.0, 1.0]
row = [[ind, val]]
model.linear_constraints.add(lin_expr=row, senses="G", rhs=[1])
j = j + 2
# Add indicator constraints
# i1 = 1 <-> c1
# i2 = 1 <-> c2
index = 0
for i in range(nbResource):
e = nbJob - 1
for j in range(e):
activity1 = activityOnAResource[i][j]
for k in range(j + 1, nbJob):
activity2 = activityOnAResource[i][k]
ic_dict = {}
ic_dict["indvar"] = nbStart + index
ic_dict["lin_expr"] = cplex.SparsePair(
ind=[starttime(j, activity1), starttime(k, activity2)],
val=[1.0, -1.0])
ic_dict["rhs"] = duration[k][activity2] / 1.0
ic_dict["sense"] = "G"
ic_dict["complemented"] = 0
# ind(nbStart + index) = 1 -> ...
# starttime(j, activity1) - starttime(k, activity2) >=
# duration(k, activity2)
model.indicator_constraints.add(**ic_dict)
ic_dict["sense"] = "L"
ic_dict["complemented"] = 1
# ind(nbStart + index) = 0 -> ...
# starttime(j, activity1) - starttime(k, activity2) <=
# duration(k, activity2)
model.indicator_constraints.add(**ic_dict)
ic_dict = {}
ic_dict["indvar"] = nbStart + index + 1
ic_dict["lin_expr"] = cplex.SparsePair(
ind=[starttime(k, activity2),
starttime(j, activity1)],
val=[1.0, -1.0])
ic_dict["rhs"] = duration[j][activity1] / 1.0
ic_dict["sense"] = "G"
ic_dict["complemented"] = 0
# ind(nbStart + index) = 1 -> ...
# starttime(k, activity2) - starttime(j, activity1) >=
# duration(j, activity1)
model.indicator_constraints.add(**ic_dict)
ic_dict["sense"] = "L"
ic_dict["complemented"] = 1
# ind(nbStart + index) = 0 -> ...
# starttime(k, activity2) - starttime(j, activity1) <=
# duration(j, activity1)
model.indicator_constraints.add(**ic_dict)
index = index + 2
# Add Objective function
# each job has a cost which contains jobEarlinessCost and
# jobTardinessCost, and get the index of Earliness variables,
# Tardiness variables and Endness variables
indexOfEarlinessVar = list(range(model.variables.get_num(),
model.variables.get_num() + nbJob))
model.variables.add(obj=jobEarlinessCost)
indexOfTardinessVar = list(range(model.variables.get_num(),
model.variables.get_num() + nbJob))
model.variables.add(obj=jobTardinessCost)
# Add finished time variables
indexOfEndnessVar = list(range(model.variables.get_num(),
model.variables.get_num() + nbJob))
model.variables.add(obj=[0.0] * nbJob)
# Add constraints for each Job
# indexOfEndnessVar[i] - starttime(i, nbResource) =
# duration[i][nbResource]
for i in range(nbJob):
ind = [indexOfEndnessVar[i], starttime(i, nbResource - 1)]
val = [1.0, -1.0]
row = [[ind, val]]
model.linear_constraints.add(
lin_expr=row, senses="E", rhs=[duration[i][nbResource - 1]])
# Add constraints for each Job
# jobDueDate[i] = \
# indexOfEndnessVar[i] + indexOfEarlinessVar[i] -
# indexOfTardinessVar[i]
for i in range(nbJob):
ind = [indexOfEndnessVar[i],
indexOfEarlinessVar[i],
indexOfTardinessVar[i]]
val = [1.0, 1.0, -1.0]
row = [[ind, val]]
model.linear_constraints.add(
lin_expr=row, senses="E", rhs=[jobDueDate[i]])
model.parameters.emphasis.mip = 4
model.solve()
model.write('etsp.sav')
except CplexError as exc:
print(exc)
return
# Display solution
print("Solution status = ", model.solution.get_status())
print("Optimal Value = ", model.solution.get_objective_value())
def etsp(datafile):
"""Read in data file and solve the model.
If no file name is given on the command line we use a default file
name. The data we read is:
activityOnAResource -- An array of arrays represents the resources
required for each activity of a job
duration -- An array of arrays represents the duration
required for each activity of a job
jobDueDate -- contains the due date for each job
jobEarlinessCost -- contains the penalty for being too early for
each job
jobTardinessCost -- contains the penalty for being too late for
each job
"""
(activityOnAResource,
duration,
jobDueDate,
jobEarlinessCost,
jobTardinessCost) = read_dat_file(datafile)
nbJob = len(jobDueDate)
nbResource = len(activityOnAResource[1])
def starttime(job, res):
"""Calculates start variable indices."""
return job * nbJob + res
# Build model
model = cplex.Cplex()
model.objective.set_sense(model.objective.sense.minimize)
# Add activity start time variables
nbStart = nbJob * nbResource
obj = [0.0] * nbStart
lb = [0.0] * nbStart
ub = [10000.0] * nbStart
model.variables.add(obj, lb, ub)
# State precedence constraints
# starttime(i, j) - starttime(i, j-1) >= duration(i, j-1)
for i in range(nbJob):
for j in range(1, nbResource):
ind = [starttime(i, j), starttime(i, j - 1)]
val = [1.0, -1.0]
row = [[ind, val]]
model.linear_constraints.add(
lin_expr=row, senses="G", rhs=[duration[i][j - 1]])
# Add indicator variables
nIndicatorVars = nbResource * nbJob * (nbJob - 1)
colname_ind = ["ind" + str(j + 1) for j in range(nIndicatorVars)]
obj = [0.0] * nIndicatorVars
lb = [0.0] * nIndicatorVars
ub = [1.0] * nIndicatorVars
types = ["B"] * nIndicatorVars
model.variables.add(obj, lb, ub, types, colname_ind)
# Add ind1 + ind2 >= 1
# ind3 + ind4 >= 1
# ind5 + ind6 >= 1
# ...
# constraints
j = nbStart
for i in range(nIndicatorVars // 2):
ind = [j, j + 1]
val = [1.0, 1.0]
row = [[ind, val]]
model.linear_constraints.add(lin_expr=row, senses="G", rhs=[1])
j = j + 2
# Add indicator constraints
# i1 = 1 <-> c1
# i2 = 1 <-> c2
index = 0
for i in range(nbResource):
e = nbJob - 1
for j in range(e):
activity1 = activityOnAResource[i][j]
for k in range(j + 1, nbJob):
activity2 = activityOnAResource[i][k]
ic_dict = {}
ic_dict["indvar"] = nbStart + index
ic_dict["lin_expr"] = cplex.SparsePair(
ind=[starttime(j, activity1), starttime(k, activity2)],
val=[1.0, -1.0])
ic_dict["rhs"] = duration[k][activity2] / 1.0
ic_dict["sense"] = "G"
ic_dict["complemented"] = 0
# ind(nbStart + index) = 1 -> ...
# starttime(j, activity1) - starttime(k, activity2) >=
# duration(k, activity2)
model.indicator_constraints.add(**ic_dict)
ic_dict["sense"] = "L"
ic_dict["complemented"] = 1
# ind(nbStart + index) = 0 -> ...
# starttime(j, activity1) - starttime(k, activity2) <=
# duration(k, activity2)
model.indicator_constraints.add(**ic_dict)
ic_dict = {}
ic_dict["indvar"] = nbStart + index + 1
ic_dict["lin_expr"] = cplex.SparsePair(
ind=[starttime(k, activity2),
starttime(j, activity1)],
val=[1.0, -1.0])
ic_dict["rhs"] = duration[j][activity1] / 1.0
ic_dict["sense"] = "G"
ic_dict["complemented"] = 0
# ind(nbStart + index) = 1 -> ...
# starttime(k, activity2) - starttime(j, activity1) >=
# duration(j, activity1)
model.indicator_constraints.add(**ic_dict)
ic_dict["sense"] = "L"
ic_dict["complemented"] = 1
# ind(nbStart + index) = 0 -> ...
# starttime(k, activity2) - starttime(j, activity1) <=
# duration(j, activity1)
model.indicator_constraints.add(**ic_dict)
index = index + 2
# Add Objective function
# each job has a cost which contains jobEarlinessCost and
# jobTardinessCost, and get the index of Earliness variables,
# Tardiness variables and Endness variables
indexOfEarlinessVar = list(range(model.variables.get_num(),
model.variables.get_num() + nbJob))
model.variables.add(obj=jobEarlinessCost)
indexOfTardinessVar = list(range(model.variables.get_num(),
model.variables.get_num() + nbJob))
model.variables.add(obj=jobTardinessCost)
# Add finished time variables
indexOfEndnessVar = list(range(model.variables.get_num(),
model.variables.get_num() + nbJob))
model.variables.add(obj=[0.0] * nbJob)
# Add constraints for each Job
# indexOfEndnessVar[i] - starttime(i, nbResource) =
# duration[i][nbResource]
for i in range(nbJob):
ind = [indexOfEndnessVar[i], starttime(i, nbResource - 1)]
val = [1.0, -1.0]
row = [[ind, val]]
model.linear_constraints.add(
lin_expr=row, senses="E", rhs=[duration[i][nbResource - 1]])
# Add constraints for each Job
# jobDueDate[i] = \
# indexOfEndnessVar[i] + indexOfEarlinessVar[i] -
# indexOfTardinessVar[i]
for i in range(nbJob):
ind = [indexOfEndnessVar[i],
indexOfEarlinessVar[i],
indexOfTardinessVar[i]]
val = [1.0, 1.0, -1.0]
row = [[ind, val]]
model.linear_constraints.add(
lin_expr=row, senses="E", rhs=[jobDueDate[i]])
model.parameters.emphasis.mip.set(
model.parameters.emphasis.mip.values.hidden_feasibility)
try:
model.solve()
except CplexSolverError as cse:
# For the Community Edition, this model exceeds problem size
# limits. The following demonstrates how to handle a specific
# error code.
if cse.args[2] == error_codes.CPXERR_RESTRICTED_VERSION:
print("The current problem is too large for your version of "
"CPLEX. Reduce the size of the problem.")
return
else:
raise
model.write('etsp.sav')
# Display solution
print("Solution status = ", model.solution.get_status())
print("Optimal Value = ", model.solution.get_objective_value())
def facility(datafile, bendersopt):
"""Solve capacitated facility location problem."""
# Read in data file. If no file name is given on the command line
# we use a default file name. The data we read is
# fixedcost -- a list/array of facility fixed cost
# cost -- a matrix for the costs to serve each client by each
# facility
# capacity -- a list/array of facility capacity
fixedcost, cost, capacity = read_dat_file(datafile)
num_locations = len(fixedcost)
num_clients = len(cost)
# Create the modeler/solver.
cpx = cplex.Cplex()
# Create variables. We have variables
# open_[j] if location j is open.
# supply[i][j]] how much client i is supplied from location j
open_ = list(
cpx.variables.add(obj=fixedcost,
lb=[0] * num_locations,
ub=[1] * num_locations,
types=["B"] * num_locations))
supply = [None] * num_clients
for i in range(num_clients):
# Objective: Minimize the sum of fixed costs for using a location
# and the costs for serving a client from a specific
# location.
supply[i] = list(
cpx.variables.add(obj=cost[i],
lb=[0.0] * num_locations,
ub=[1.0] * num_locations))
# Constraint: Each client i must be assigned to exactly one location:
# sum(j in nbLocations) supply[i][j] == 1 for each i in nbClients
for i in range(num_clients):
cpx.linear_constraints.add(lin_expr=[
cplex.SparsePair(ind=supply[i], val=[1.0] * num_locations)
],
senses=["E"],
rhs=[1.0])
# Constraint: For each location j, the capacity of the location must
# be respected:
# sum(i in nbClients) supply[i][j] <= capacity[j] * open_[j]
for j in range(num_locations):
ind = [supply[i][j] for i in range(num_clients)] + [open_[j]]
val = [1.0] * num_clients + [-capacity[j]]
cpx.linear_constraints.add(
lin_expr=[cplex.SparsePair(ind=ind, val=val)],
senses=["L"],
rhs=[0.0])
# Setup Benders decomposition if required.
if bendersopt == ANNO_BENDERS:
# We specify the structure for doing a Benders decomposition by
# telling CPLEX which variables are in the master problem using
# annotations. By default variables are assigned value
# CPX_BENDERS_MASTERVALUE+1 and thus go into the workers.
# Variables open_[j] should go into the master and therefore
# we assign them value CPX_BENDERS_MASTER_VALUE.
mastervalue = cpx.long_annotations.benders_mastervalue
idx = cpx.long_annotations.add(
name=cpx.long_annotations.benders_annotation,
defval=mastervalue + 1)
objtype = cpx.long_annotations.object_type.variable
cpx.long_annotations.set_values(idx, objtype,
[(open_[x], mastervalue)
for x in range(num_locations)])
print("Solving with explicit Benders decomposition.")
elif bendersopt == AUTO_BENDERS:
# Let CPLEX automatically decompose the problem. In the case of
# a capacitated facility location problem the variables of the
# master problem should be the integer variables. By setting the
# Benders strategy parameter to Full, CPLEX will put all integer
# variables into the master, all continuous varibles into a
# subproblem, and further decompose that subproblem, if possible.
cpx.parameters.benders.strategy.set(
cpx.parameters.benders.strategy.values.full)
print("Solving with automatic Benders decomposition.")
elif bendersopt == NO_BENDERS:
print("Solving without Benders decomposition.")
else:
raise ValueError("invalid bendersopt argument")
# Solve and display solution
cpx.solve()
print("Solution status =", cpx.solution.get_status_string())
print("Optimal value:", cpx.solution.get_objective_value())
tol = cpx.parameters.mip.tolerances.integrality.get()
values = cpx.solution.get_values()
for j in [
x for x in range(num_locations) if values[open_[x]] >= 1.0 - tol
]:
print("Facility {0} is open, it serves clients {1}".format(
j, " ".join([
str(x) for x in range(num_clients)
if values[supply[x][j]] >= 1.0 - tol
])))
for v in range(cut.variables.get_num()):
print(" Cut" + str(v) + " = " + str(cut.solution.get_values(v)))
if __name__ == "__main__":
# Input data. If no file is given on the command line then use a
# default file name. The data read is
# width - the width of the the roll,
# size - the sie of each strip,
# amount - the demand for each strip.
datafile = "../../../examples/data/cutstock.dat"
if len(sys.argv) < 2:
print("Default data file : " + datafile)
else:
datafile = sys.argv[1]
width, size, amount = read_dat_file(datafile)
# Setup cutting optimization (master) problem.
# This is the problem to which columns will be added in the loop
# below.
cut = cplex.Cplex()
cutcons = list(range(len(amount))) # constraint indices
cutvars = list(range(len(size))) # variable indices
cut.variables.add(obj = [1] * len(cutvars))
# Add constraints. They have empty left-hand side initially. The
# left-hand side is filled in the next loop.
cut.linear_constraints.add(lin_expr = [SparsePair()] * len(cutcons),
senses = ["G"] * len(cutcons),
rhs = amount)
for v in cutvars:
def admipex8(datadir, from_table, lazy, use_callback):
"""Solve a facility location problem with cut callbacks or lazy
constraints.
"""
# Read in data file. The data we read is
# fixedcost -- a list/array of facility fixed cost
# cost -- a matrix for the costs to serve each client by each
# facility
# pylint: disable=unbalanced-tuple-unpacking
fixedcost, cost, _ = read_dat_file(datadir + '/' + 'facility.dat')
# Create the model
locations = list(range(len(fixedcost)))
clients = list(range(len(cost)))
cpx = cplex.Cplex()
# Create variables.
# - used[j] If location j is used.
# - supply[c][j] Amount shipped from location j to client c. This is a
# number in [0,1] and specifies the percentage of c's
# demand that is served from location i.
# Note that we also create the objective function along with the variables
# by specifying the objective coefficient for each variable in the 'obj'
# argument.
used = cpx.variables.add(obj=fixedcost,
lb=[0] * len(locations),
ub=[1] * len(locations),
types=['B'] * len(locations),
names=['used(%d)' % (j) for j in locations])
supply = [
cpx.variables.add(obj=[cost[c][j] for j in locations],
lb=[0] * len(locations),
ub=[1] * len(locations),
types=['B'] * len(locations),
names=['supply(%d)(%d)' % (c, j) for j in locations])
for c in clients
]
# The supply for each client must sum to 1, i.e., the demand of each
# client must be met.
cpx.linear_constraints.add(lin_expr=[
cplex.SparsePair(supply[c], [1.0] * len(supply[c])) for c in clients
],
senses=['E'] * len(clients),
rhs=[1.0] * len(clients))
# Capacity constraint for each location. We just require that a single
# location cannot serve all clients, that is, the capacity of each
# location is nbClients-1. This makes the model a little harder to
# solve and allows us to separate more cuts.
if not lazy:
cpx.linear_constraints.add(lin_expr=[
cplex.SparsePair([supply[c][j] for c in clients] + [used[j]],
[1.0] * len(clients) + [-(len(clients) - 1.0)])
for j in locations
],
senses=['L'] * len(locations),
rhs=[0] * len(locations))
# Tweak some CPLEX parameters so that CPLEX has a harder time to
# solve the model and our cut separators can actually kick in.
cpx.parameters.mip.strategy.heuristicfreq.set(-1)
cpx.parameters.mip.cuts.mircut.set(-1)
cpx.parameters.mip.cuts.implied.set(-1)
cpx.parameters.mip.cuts.gomory.set(-1)
cpx.parameters.mip.cuts.flowcovers.set(-1)
cpx.parameters.mip.cuts.pathcut.set(-1)
cpx.parameters.mip.cuts.liftproj.set(-1)
cpx.parameters.mip.cuts.zerohalfcut.set(-1)
cpx.parameters.mip.cuts.cliques.set(-1)
cpx.parameters.mip.cuts.covers.set(-1)
# Setup the callback.
# We instantiate the callback object and attach the necessary data
# to it.
# We also setup the contexmask parameter to indicate when the callback
# should be called.
facilitycb = FacilityCallback(clients, locations, used, supply)
contextmask = 0
if use_callback:
contextmask |= cplex.callbacks.Context.id.relaxation
if from_table:
# Generate all disaggregated constraints and put them into a
# table that is scanned by the callback.
facilitycb.cutlhs = [
cplex.SparsePair([supply[c][j], used[j]], [1.0, -1.0])
for j in locations for c in clients
]
facilitycb.cutrhs = [0] * len(locations) * len(clients)
if lazy:
contextmask |= cplex.callbacks.Context.id.candidate
# Callback is setup attach it to the model
if contextmask:
cpx.set_callback(facilitycb, contextmask)
cpx.write('model.lp')
cpx.solve()
print('Solution status: %d' % cpx.solution.get_status())
print('Nodes processed: %d' %
cpx.solution.progress.get_num_nodes_processed())
print('Active user cuts/lazy constraints: %d' %
cpx.solution.MIP.get_num_cuts(cpx.solution.MIP.cut_type.user))
tol = cpx.parameters.mip.tolerances.integrality.get()
print('Optimal value: %f' %
cpx.solution.get_objective_value())
values = cpx.solution.get_values()
for j in [x for x in locations if values[used[x]] >= 1 - tol]:
print('Facility %d is used, it serves clients %s' % (j, ', '.join(
[str(x) for x in clients if values[supply[x][j]] >= 1 - tol])))
datafile = sys.argv[1]
else:
prompt = """Enter the path to a data file:
The path must be entered as a string; e.g.
"../../../examples/data/etsp.dat".
Enter "" to use the default file.\n"""
user_file = input(prompt)
print("hello" + user_file)
if user_file != "":
datafile = user_file
else:
datafile = defaultfile
activityOnAResource, \
duration, \
jobDueDate, \
jobEarlinessCost, \
jobTardinessCost = \
read_dat_file(datafile)
nbJob = len(jobDueDate)
nbResource = len(activityOnAResource[1])
def starttime(job, res):
return job * nbJob + res
etsp(datafile)
def admipex5(args):
# Set default arguments and parse command line
datadir = '../../../examples/data'
from_table = False
lazy = False
use_callback = True
for arg in args[1:]:
if arg.startswith('-data='):
datadir = arg[6:]
elif arg == '-table':
from_table = True
elif arg == '-lazy':
lazy = True
elif arg == '-no-cuts':
use_callback = False
else:
print('Unknown argument %s' % arg)
usage(args[0])
# Read in data file. If no file name is given on the command line
# we use a default file name. The data we read is
# fixedcost -- a list/array of facility fixed cost
# cost -- a matrix for the costs to serve each client by each
# facility
fixedcost, cost, _ = read_dat_file(datadir + '/' + 'facility.dat')
# Create the model
locations = list(range(len(fixedcost)))
clients = list(range(len(cost)))
cpx = cplex.Cplex()
# Create variables.
# - used[j] If location j is used.
# - supply[c][j] Amount shipped from location j to client c. This is a
# number in [0,1] and specifies the percentage of c's
# demand that is served from location i.
# Note that we also create the objective function along with the variables
# by specifying the objective coefficient for each variable in the 'obj'
# argument.
used = cpx.variables.add(obj=fixedcost,
lb=[0] * len(locations),
ub=[1] * len(locations),
types=['B'] * len(locations),
names=['used(%d)' % (j) for j in locations])
supply = [
cpx.variables.add(obj=[cost[c][j] for j in locations],
lb=[0] * len(locations),
ub=[1] * len(locations),
types=['B'] * len(locations),
names=['supply(%d)(%d)' % (c, j) for j in locations])
for c in clients
]
# The supply for each client must sum to 1, i.e., the demand of each
# client must be met.
cpx.linear_constraints.add(lin_expr=[
cplex.SparsePair(supply[c], [1.0] * len(supply[c])) for c in clients
],
senses=['E'] * len(clients),
rhs=[1.0] * len(clients))
# Capacity constraint for each location. We just require that a single
# location cannot serve all clients, that is, the capacity of each
# location is nbClients-1. This makes the model a little harder to
# solve and allows us to separate more cuts.
if not lazy:
cpx.linear_constraints.add(lin_expr=[
cplex.SparsePair([supply[c][j] for c in clients] + [used[j]],
[1.0] * len(clients) + [-(len(clients) - 1.0)])
for j in locations
],
senses=['L'] * len(locations),
rhs=[0] * len(locations))
# Tweak some CPLEX parameters so that CPLEX has a harder time to
# solve the model and our cut separators can actually kick in.
cpx.parameters.threads.set(1)
cpx.parameters.mip.strategy.heuristicfreq.set(-1)
cpx.parameters.mip.cuts.mircut.set(-1)
cpx.parameters.mip.cuts.implied.set(-1)
cpx.parameters.mip.cuts.gomory.set(-1)
cpx.parameters.mip.cuts.flowcovers.set(-1)
cpx.parameters.mip.cuts.pathcut.set(-1)
cpx.parameters.mip.cuts.liftproj.set(-1)
cpx.parameters.mip.cuts.zerohalfcut.set(-1)
cpx.parameters.mip.cuts.cliques.set(-1)
cpx.parameters.mip.cuts.covers.set(-1)
if use_callback:
if from_table:
# Generate all disaggregated constraints and put them into a
# table that is scanned by the callback.
usercb = cpx.register_callback(CutsFromTable)
usercb.cutlhs = [
cplex.SparsePair([supply[c][j], used[j]], [1.0, -1.0])
for j in locations for c in clients
]
usercb.cutrhs = [0] * len(locations) * len(clients)
else:
usercb = cpx.register_callback(Disaggregated)
usercb.clients = clients
usercb.locations = locations
usercb.used = used
usercb.supply = supply
if lazy:
lazycb = cpx.register_callback(LazyCallback)
lazycb.clients = clients
lazycb.locations = locations
lazycb.used = used
lazycb.supply = supply
cpx.write('model.lp')
cpx.solve()
print('Solution status: %d' % cpx.solution.get_status())
print('Nodes processed: %d' %
cpx.solution.progress.get_num_nodes_processed())
print('Active user cuts/lazy constraints: %d' %
cpx.solution.MIP.get_num_cuts(cpx.solution.MIP.cut_type.user))
tol = cpx.parameters.mip.tolerances.integrality.get()
print('Optimal value: %f' %
cpx.solution.get_objective_value())
values = cpx.solution.get_values()
for j in [x for x in locations if values[used[x]] >= 1 - tol]:
print('Facility %d is used, it serves clients %s' % (j, ', '.join(
[str(x) for x in clients if values[supply[x][j]] >= 1 - tol])))
