我试图使用linprog来优化以下问题(uploaded in Google Drive)。数据集本身已上载here
到目前为止,我已经用Python编写了以下实现:
import pandas as pd
import numpy as np
df = pd.read_csv('Supplier Specs.csv')
from scipy.optimize import linprog
def fromPandas(dataframe, colName):
return dataframe[[colName]].values.reshape(1,11)[0]
## A_ub * x <= b_ub
## A_eq * x == b_eq
A_eq = [1.0]*11
u_eq = [600.0] # demand
## reading the actual numbers from the pandas dataframe and then converting them to vectors
BAR = fromPandas(df, 'Brix / Acid Ratio')
acid = fromPandas(df, 'Acid (%)')
astringency = fromPandas(df, 'Astringency (1-10 Scale)')
color = fromPandas(df, 'Color (1-10 Scale)')
price = fromPandas(df, 'Price (per 1K Gallons)')
shipping = fromPandas(df, 'Shipping (per 1K Gallons)')
upperBounds = fromPandas(df, 'Qty Available (1,000 Gallons)')
lowerBounds = [0]*len(upperBounds) # list with length 11 and value 0
lowerBounds[2] = 0.4*u_eq[0] # adding the Florida tax bound
bnds = [(0,0)]*len(upperBounds) # bounds
for i in range(0,len(upperBounds)):
bnds[i] = (lowerBounds[i], upperBounds[i])
c = price + shipping # objective function coefficients
print("------------------------------------- Debugging Output ------------------------------------- \n")
print("Objective function coefficients: ", c)
print("Bounds: ", bnds)
print("Equality coefficients: ", A_eq)
print("BAR coefficients: ", BAR)
print("Astringency coefficients: ", astringency)
print("Color coefficients: ", color)
print("Acid coefficients: ", acid)
print("\n")
A_ub = [BAR, acid, astringency, color, -BAR, -acid, -astringency, -color] # coefficients for inequalities
b_ub = np.array([12.5, 1.0, 4.0, 5.5, -11.5, -0.75, 0, -4.5]) # limits for the inequalities
b_ub = b_ub * u_eq[0] # scaling the limits with the demand
xOptimized = linprog(c, A_ub, b_ub, [A_eq], u_eq, bounds=(bnds))
print(xOptimized) # the amounts of juice which we need to buy from each supplier
优化方法返回的结果是找不到可行的起点。我相信我在使用这个方法时有一个主要的错误,但是到目前为止我还不能理解它。在
帮帮忙吗?在
提前谢谢!在
编辑: 目标函数的期望值为371724
预期解向量[0,0240,0,15.8,0,0,0126.3109.7108.2]
我的猜测确实不成熟。
[A_eq]
当然是二维的。当您从这似乎是问题的症结所在。由于A_ub*x<;=b\u ub,您需要为
^{pr2}$条*x<;=12.5
以及
-条*x<;=-11.5,即
11.5<;=巴*x<;=12.5 这显然没有产生任何结果。你实际上是在找
这现在收敛了,但给出的结果与您现在在编辑中发布的预期解决方案不同。显然,你必须重新计算你的不等式参数,而你在问题中没有具体说明。在
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