I am trying to solve MILP in puLP (Python), and I keep getting the following error:
Traceback (most recent call last):
File "main_lp.py", line 63, in <module>
ans = solve_lp(C)
File "/home/ashwin/Documents/Williams/f2014/math317_or/project/solve_lp.py", line 36, in solve_lp
prob.solve()
File "/usr/local/lib/python2.7/dist-packages/PuLP-1.5.6-py2.7.egg/pulp/pulp.py", line 1619, in solve
status = solver.actualSolve(self, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/PuLP-1.5.6-py2.7.egg/pulp/solvers.py", line 1283, in actualSolve
return self.solve_CBC(lp, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/PuLP-1.5.6-py2.7.egg/pulp/solvers.py", line 1346, in solve_CBC
raise PulpSolverError("Pulp: Error while executing "+self.path)
pulp.solvers.PulpSolverError: Pulp: Error while executing /usr/local/lib/python2.7/dist-packages/PuLP-1.5.6-py2.7.egg/pulp/solverdir/cbc-32
For my linear programming problem, I am attempting to take sums of different vectors as the constraint, and I think I must have done that wrong somehow, because a much simpler problem works with no hitches. I have attached the code (C
is an N by N numpy
array).
def solve_lp(C):
N = len(C)
prob=LpProblem('Scheduling',LpMinimize)
X = [[LpVariable('X' + str(i+1) + str(j+1), 0, C[i,j],LpBinary)
for j in range(N)] for i in range(N)]
X = np.array(X)
X_o = [LpVariable('X0' + str(i), 0, None, LpBinary) for i in range(N)]
X_t = [LpVariable('X' + str(i) + 't', 0, None, LpBinary) for i in range(N)]
# Objective Function
ones_vec = list(np.ones(len(X_o)))
prob += lpDot(ones_vec,X_o), 'Minimize Buses'
# Constraints
for i in range(N):
row = list(X[i,:]) + [X_t[i]]
ones_vec = list(np.ones(len(row)))
prob += lpDot(ones_vec, row) == 1, 'Only one destination for ' + str(i)
for j in range(N):
col = list(X[:,j]) + [X_o[j]]
ones_vec = list(np.ones(len(col)))
prob += lpDot(ones_vec,col) == 1, 'Only one source for ' + str(j)
prob.solve()
return X, value(prob.objective)
I recently had a similar problem due to Nan inputs in the model. I had the data in a DataFrame where some the cells should not bee converted to variables to improve performance. However, upon creating the objective function and the constraints, I noticed the presence of Nan and when I changed them it worked perfectly.