在Python中使用實數紙漿

Question

我使用紙漿模塊來解決我的一些優化問題。用整數，它就像一個魅力。我想知道紙漿是否可以用真正的數字工作。我認爲這被稱爲混合整數編程。

這是我爲我的項目構建的示例優化問題。

prob = pulp.LpProblem("example", pulp.LpMinimize) 

# Variable represent number of times device i is used 
n1 = pulp.LpVariable("n1", 1, 5, pulp.LpInteger) 
n2 = pulp.LpVariable("n2", 0, max_exp, pulp.LpInteger) 
n3 = pulp.LpVariable("n3", 0, max_exp, pulp.LpInteger) 
n4 = pulp.LpVariable("n4", 0, max_exp, pulp.LpInteger) 
n5 = pulp.LpVariable("n5", 0, max_exp, pulp.LpInteger) 


# The objective function that we want to minimize: the total cost 
prob += n1 * Device1[-1] + n2 * Device2[-1] + n3 * Device3[-1] + n4 * Device4[-1] + n5 * Device5[-1], "Minimize total cost" 

# Constraint that we use no more than max. devices per controller device 
prob += n2 + n3 + n4 + n5 <= n1*max_exp 

#Constraint (total_UI >= (target_AI + target_BI)) 
prob += n1 * Device1[0] + n2 * Device2[0] + n3 * Device3[0] + n4 * Device4[0] + n5 * Device5[0] >= (Target[0]+Target[2]) 

#Constraint ((total_UO + total_BO) >= target_BO) 
prob += n1 * Device1[1] + n2 * Device2[1] + n3 * Device3[1] + n4 * Device4[1] + n5 * Device5[1] + n1 * Device1[3] + n2 * Device2[3] + n3 * Device3[3] + n4 * Device4[3] + n5 * Device5[3]>= (Target[3]) 

#Constraint [total_UO + total_AO + total_BO - target_BO] >= target_AO 
prob += n1 * Device1[1] + n2 * Device2[1] + n3 * Device3[1] + n4 * Device4[1] + n5 * Device5[1] + n1 * Device1[2] + n2 * Device2[2] + n3 * Device3[2] + n4 * Device4[2] + n5 * Device5[2] + n1 * Device1[3] + n2 * Device2[3] + n3 * Device3[3] + n4 * Device4[3] + n5 * Device5[3] >= (Target[1]+Target[3])   

# Actually solve the problem, this calls GLPK so you need it installed 
prob.solve() 

讓我們假設DeviceX [-1]是一個實數。你能做出所需的改變嗎？

在此先感謝。

Answer 1

解決

其實，我沒有需要做任何事情。剛剛提供了實際的數字，它的工作。