我正在将CPLEX求解器与python 3.6一起使用来求解数学编程模型。我曾经在旧计算机上执行此操作，现在在新计算机上重新安装了cplex却没有问题，但是当我尝试运行最初运行时没有错误的模型时，现在总是会遇到相同的错误，例如Traveling Salesman问题：
TypeError Traceback (most recent call last)
~\Dropbox\CPLEX\TSP_MTZ\TSP.py in <module>
137
138
--> 139 TSP(4)
~\Dropbox\CPLEX\TSP_MTZ\TSP.py in TSP(N)
38 for j in range(N):
39 x_varobj.append(float(c[i,j]))
---> 40 Model.variables.add(obj = x_varobj,lb = x_varlb,ub = x_varub,types = x_vartypes,names = x_varnames)
41
42 u_vars=list(np.array(["u("+str(i)+")" for i in range(0,N)]))
c:\users\healh\.conda\envs\py36\lib\site-packages\cplex\_internal\_subinterfaces.py in add(self,obj,lb,ub,types,names,columns)
454 columns)
455 return self._add_iter(self.get_num,self._add,--> 456 obj,columns)
457
458 def delete(self,*args):
c:\users\healh\.conda\envs\py36\lib\site-packages\cplex\_internal\_baseinterface.py in _add_iter(getnumfun,addfun,*args,**kwargs)
39 """non-public"""
40 old = getnumfun()
---> 41 addfun(*args,**kwargs)
42 return range(old,getnumfun())
43
c:\users\healh\.conda\envs\py36\lib\site-packages\cplex\_internal\_subinterfaces.py in _add(self,columns)
376 if columns == []:
377 CPX_PROC.newcols(self._env._e,self._cplex._lp,--> 378 types,names)
379 else:
380 with CPX_PROC.chbmatrix(columns,self._cplex._env_lp_ptr,c:\users\healh\.conda\envs\py36\lib\site-packages\cplex\_internal\_procedural.py in newcols(env,lp,xctype,colname)
965 status = CR.CPXXnewcols(
966 env,ccnt,c_obj,c_lb,c_ub,--> 967 xctype,colname)
968 check_status(env,status)
969
c:\users\healh\.conda\envs\py36\lib\site-packages\cplex\_internal\_pycplex.py in CPXXnewcols(env,py_obj,py_lb,py_ub,colname)
1783
1784 def CPXXnewcols(env: 'CPXCENVptr',lp: 'CPXLPptr',ccnt: 'CPXDIM',py_obj: 'double const *',py_lb: 'double const *',py_ub: 'double const *',xctype: 'char const *',colname: 'char const *const *') -> "int":
-> 1785 return _pycplex_platform.CPXXnewcols(env,colname)
1786
1787 def CPXXaddcols(env: 'CPXCENVptr',nzcnt: 'CPXNNZ',py_matbeg: 'CPXNNZ const *',colname: 'char const *const *') -> "int":
TypeError: not a list
我的代码如下：
import time
import numpy as np
import cplex
from cplex import Cplex
from cplex.exceptions import CplexError
import sys
import networkx as nx
import matplotlib.pyplot as plt
from openpyxl import Workbook
import xlrd
def TSP(N):
wb = Workbook()
ws = wb.active
book = xlrd.open_workbook('C.xlsx') #LECTURA DE PARÁMETROS.
sheet = book.sheet_by_name("C")
c=[[int(sheet.cell_value(r,c)) for c in range(sheet.ncols)] for r in range(sheet.nrows)]
c=np.matrix(c)
print("")
print("MATRIZ DE DISTANCIAS")
print("")
print(c)
print("")
print("")
print("")
Model=cplex.Cplex()
x_vars=np.array([["x("+str(i)+","+str(j)+")" for j in range(N)] for i in range(N)])
x_varnames = x_vars.flatten()
x_vartypes='B'*N*N
x_varlb = [0.0]*len(x_varnames)
x_varub = [1.0]*len(x_varnames)
x_varobj = []
for i in range(N):
for j in range(N):
x_varobj.append(float(c[i,j]))
Model.variables.add(obj = x_varobj,names = x_varnames)
u_vars=np.array(["u("+str(i)+")" for i in range(0,N)])
u_varnames=u_vars.flatten()
u_vartypes='I'*N
u_varlb=[1.0]*N
u_varub=[float(N)-1.0]*N
u_varobj=[0.0]*N
Model.variables.add(obj = u_varobj,lb = u_varlb,ub = u_varub,types = u_vartypes,names = u_varnames)
Model.objective.set_sense(Model.objective.sense.minimize)
# suma(J,x[i,j])==1.0,forall i in N
for i in range(N):
row1=[]
val1=[]
for j in range(N):
row1.append(x_vars[i,j])
val1.append(1.0)
Model.linear_constraints.add(lin_expr = [cplex.SparsePair(ind = row1,val= val1)],senses = 'E',rhs = [1.0])
# suma(i,forall j in N
for j in range(N):
row2=[]
val2=[]
for i in range(N):
row2.append(x_vars[i,j])
val2.append(1.0)
Model.linear_constraints.add(lin_expr = [cplex.SparsePair(ind = row2,val= val2)],rhs = [1.0])
#u[i]-u[j]-(N-1)x[i,ji]<=N-2,forall i in N,forall j in N,con i!=j.
for i in range(1,N):
for j in range(1,N):
if i!=j:
row3=[]
val3=[]
row3.append(u_vars[i])
val3.append(1.0)
row3.append(u_vars[j])
val3.append(-1.0)
row3.append(x_vars[i,j])
val3.append(float(N)-1.0)
Model.linear_constraints.add(lin_expr = [cplex.SparsePair(ind = row3,val= val3)],senses = 'L',rhs = [float(N)-2.0])
solution=Model.solve()
Model.write('modelo.lp')
#Model.parameters.mip.pool.relgap.set(0.6)
pool_solution=Model.populate_solution_pool()
#print(pool_solution)
def show_solution():
print("\nVARLOS FUNCION OBJETIVO - DISTANCIA MINIMIA = {}".format(Model.solution.get_objective_value()))
V=[i for i in range(N)]
E=[]
E1=[(i,j) for i in range(N) for j in range(N) if i!=j]
for i in range(0,N):
for j in range(0,N):
if(Model.solution.get_values("x("+str(i)+","+str(j)+")")!=0.0):
print("x("+str(i)+","+str(j)+")"+" = "+str(Model.solution.get_values("x("+str(i)+","+str(j)+")")))
E.append((i,j))
print("")
for i in range(0,N):
if(Model.solution.get_values("u("+str(i)+")")!=0.0):
print("u("+str(i)+")"+" = "+str(Model.solution.get_values("u("+str(i)+")")))
print("")
G=nx.DiGraph()
G.add_edges_from(E)
G.add_nodes_from(V)
pos=nx.spring_layout(G,k=0.3)
print(Model.solution.get_values("x("+str(1)+","+str(0)+")")) #OBTENER VALOR DE UNA VARIABLE.
print("ESTATUS_DE_LA_SOLUCION_ENCONTRADA:",Model.solution.get_status_string())
print("SOLUCION_PRIMAL_OPTIMA?:",Model.solution.is_primal_feasible())
#print(Model.variables.get_cols())
nx.draw_networkx_nodes(G,pos)
nx.draw_networkx_labels(G,pos)
nx.draw_networkx_edges(G,pos,edgelist=E1,edge_color='blue',width=0.3,arrows=True) # highlight elist
nx.draw_networkx_edges(G,edge_color='black',width=1.8,arrows=True) # show all edges,thin lines
# turn off axis markings
plt.axis('off')
plt.savefig('grafo_tsp.png',dpi=20)
plt.show()
show_solution()
TSP(4)
这是数据：
我真的不明白这个问题，我每天都这样做，现在有这个问题，有什么提示吗？
