小编Joh*_*nes的帖子

我想优化泵存储工厂的时间表.基本上有96个已知价格(当天的每个季度),模型应决定是否(1)泵,(2)涡轮机或(3)每个季度都不做任何事情.因此,X:-100有一些界限

首先,我尝试了以下内容:

from scipy.optimize import minimize
import numpy as np

prices=np.array([[1.5,50,30]])
xp =np.array([[1.5,50,30]])

fun = lambda x: xp* prices #here xp and prices should be matrices

cons = ({'type': 'ineq', 'fun': lambda x:  (xp*0.25)<=500},
    {'type': 'ineq', 'fun': lambda x: (xp*0.25)>=0})

bnds = ((0, None), (0, None), (0, None))

res = minimize(fun, (2, 0,0), method='SLSQP', bounds=bnds, constraints=cons)

但是,这会引发错误:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-17-15c05e084977> in <module>()
     10 bnds = ((0, None), (0, None), (0, None))
     11 
---> 12 res …