如何在python中使用docplex设置一个4维变量?

时间:2019-04-24 02:20:33

标签: python cplex docplex

我想设置一个二进制的四维变量,例如X [a] [b] [c] [d],所以bur docplex仅具有函数binary_var_cube来设置三维。如何创建四维? 我发现有人用它来创建三维尺寸,并说可以扩展到更大的尺寸。但这没用。

binary_var_dict((a,b,c) for a in ... for b in ... for c in ...)

2 个答案:

答案 0 :(得分:2)

然后让我分享一个小例子:

from docplex.mp.model import Model

# Data

r=range(1,3)

i=[(a,b,c,d) for a in r for b in r for c in r for d in r]

print(i)

mdl = Model(name='model')

#decision variables
mdl.x=mdl.integer_var_dict(i,name="x")

# Constraint
for it in i:
    mdl.add_constraint(mdl.x[it] == it[0]+it[1]+it[2]+it[3], 'ct')

mdl.solve()

# Dislay solution
for it in i:
    print(" x ",it," --> ",mdl.x[it].solution_value); 

给出

[(1, 1, 1, 1), (1, 1, 1, 2), (1, 1, 2, 1), (1, 1, 2, 2), (1, 2, 1, 1), (1, 2, 1, 2), (1, 2, 2, 1), (1, 2, 2, 2), (2, 1, 1, 1), (2, 1, 1, 2), (2, 1, 2, 1), (2, 1, 2, 2), (2, 2, 1, 1), (2, 2, 1, 2), (2, 2, 2, 1), (2, 2, 2, 2)]
 x  (1, 1, 1, 1)  -->  4.0
 x  (1, 1, 1, 2)  -->  5.0
 x  (1, 1, 2, 1)  -->  5.0
 x  (1, 1, 2, 2)  -->  6.0
 x  (1, 2, 1, 1)  -->  5.0
 x  (1, 2, 1, 2)  -->  6.0
 x  (1, 2, 2, 1)  -->  6.0
 x  (1, 2, 2, 2)  -->  7.0
 x  (2, 1, 1, 1)  -->  5.0
 x  (2, 1, 1, 2)  -->  6.0
 x  (2, 1, 2, 1)  -->  6.0
 x  (2, 1, 2, 2)  -->  7.0
 x  (2, 2, 1, 1)  -->  6.0
 x  (2, 2, 1, 2)  -->  7.0
 x  (2, 2, 2, 1)  -->  7.0
 x  (2, 2, 2, 2)  -->  8.0

致谢

答案 1 :(得分:2)

这是丹尼尔·荣格拉斯(Daniel Junglas)的回答,几乎逐字抄录了https://developer.ibm.com/answers/questions/385771/decision-matrices-with-more-than-3-variables-for-d/

您可以使用任何Arue元组作为键来从字典访问变量:

 x = m.binary_var_dict((i, l, t, v, r)
                       for i in types
                       for l in locations
                       for t in times
                       for v in vehicles
                       for r in routes)

然后您可以使用以下命令访问变量:

for i in types:
    for l in locations:
       for t in times:
          for v in vehicles:
             for r in routes:
                print x[i, l, t, v, r]

您也可以使用:

 x = [[[[[m.binary_var('%d_%d_%d_%d_%d' % (i, l, t, v, r))
          for r in routes]
         for v in vehicles]
        for t in times]
       for l in locations]
      for i in types]

 for i in types:
    for l in locations:
       for t in times:
          for v in vehicles:
             for r in routes:
                print x[i][l][t][v][r]

但是此方法不支持稀疏维度,并且需要更多的方括号来表示。