CPLEX-python (3.6) 不断显示“未列出”错误

Question

我正在使用带有 python 3.6 的 CPLEX 求解器来求解数学规划模型。 我曾经在我的旧计算机上执行此操作，现在在新计算机上重新安装 cplex 没有问题，但是当我尝试运行最初运行没有错误的模型时，现在我总是遇到相同的错误，例如对于旅行推销员问题：

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, lb, ub, types, names, 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, obj, lb, ub, types, names, columns)
    376         if columns == []:
    377             CPX_PROC.newcols(self._env._e, self._cplex._lp, obj, lb, ub,
--> 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, obj, lb, ub, xctype, colname)
    965         status = CR.CPXXnewcols(
    966             env, lp, 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, lp, ccnt, py_obj, py_lb, py_ub, xctype, 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, lp, ccnt, py_obj, py_lb, py_ub, xctype, colname)
   1786
   1787 def CPXXaddcols(env: 'CPXCENVptr', lp: 'CPXLPptr', ccnt: 'CPXDIM', nzcnt: 'CPXNNZ', py_obj: 'double const *', py_matbeg: 'CPXNNZ const *', py_lb: 'double const *', py_ub: 'double 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, lb = x_varlb, ub = x_varub, types = x_vartypes, 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,x[i,j])==1.0, 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)], senses = 'E', 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, pos, 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，我以前每天都这样做，现在我遇到了这个问题，有什么提示吗？

Answer 1

问题是

x_varnames = x_vars.flatten()

将x_varnames创建为 numpy 数组，而variables.add()的names参数应为列表。

您可以通过将x_varnames定义为来解决此问题

x_varnames = x_vars.flatten().tolist()

我不确定是 CPLEX 中的更改还是 numpy 中的更改导致了此问题。