如何在Python中求解分段线性规划方程？

Question

我的目标函数是： f（x）=a*500*价格类别x+b*500*价格类别y

其中pricecat_x是
1300，其中0<；x\u数量<；=250
1400，其中251<；=xï数量>；500

价格是
1420，其中0<；y\u数量<；400
1390，其中401<；y\u数量<；=500

xu数量允许范围为（250400） y数量的允许范围为（100250）

我已经写了我的代码如下。你知道吗

我的目标是优化a和b，即x和y的数量。你知道吗

我在跑下面的线路时碰到了一个路障：

 problem += - a*Q_demand*pricecat_x - b*Q_demand* pricecat_y

Traceback (most recent call last):

  File "<ipython-input-120-207ebdebfbb6>", line 1, in <module>
    problem += - a*Q_demand*pricecat_x - b*Q_demand* pricecat_y

  File "C:\Anaconda\envs\tfflow\lib\site-packages\pulp\pulp.py", line 790, in __mul__
    if other != 0:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

想知道是否有人能教你如何用Python执行分段线性规划？你知道吗

非常感谢。你知道吗

import numpy as np
from pulp import *
problem = LpProblem("Volume Aportionment", LpMaximize)

## Constants
# Supplier 1

VA_xmin = 0.5   #min VA - Supplier 1
VA_xmax = 0.8   #max VA - Supplier 1

# Supplier 2
VA_ymin = 0.2   #min VA - Supplier 2
VA_ymax = 0.5   #max VA - Supplier 2

Q_demand = 500

# Pricing Tiers
P1 = np.linspace(0, 500, 51)
pricecat_x = np.piecewise(P1, [P1 <= 250, P1 >= 251], [1324.17, 1309.17])

P2 = np.linspace(0, 500, 51)
pricecat_y = np.piecewise(P2, [P2 <= 400, P2 >= 401], [1419.09, 1394.09])

#pricecat_x = 1305
#pricecat_y = 1342

# define Variables
a = LpVariable(name = "VA1", lowBound=VA_xmin, upBound=VA_xmax, cat="Continuous")
b = LpVariable(name = "VA2", lowBound=VA_ymin, upBound=VA_ymax, cat="Continuous")

# constraints
problem += (a+b)*Q_demand >=500

# objective function
#problem += - weight_x*pricecat_x - weight_y* pricecat_y
problem += - a*Q_demand*pricecat_x - b*Q_demand* pricecat_y


print(problem)

# solving
problem.solve()

for v in problem.variables():
    print(v.name, "=", v.varValue)