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python - 在项目选择问题中创建约束

问题描述

这就是我正在处理“项目选择”问题的数据框：

Return   Sector    Investment Project_name  
0.290    Solar     228376120  Solar1   
0.07     Solar     70021891   Solar2   
0.25     Wind      6467237    Eolico1  
0.3      Wind      417713440  Eolico2  
0.16     Wind      377494250  Eolico3  
0.28     Wind      230345712  Eolico4  
0.29     CGHPCHBIO 35476862   CGH1     
0.26     CGHPCHBIO 60671402   CGH2     
0.07     CGHPCHBIO 349544333  PCH1     
0.12     CGHPCHBIO 425442985  PCH2     
0.29     CGHPCHBIO 66292734   PCH3     
0.15     CGHPCHBIO 300677487  PCH4     
0.25     CGHPCHBIO 409144798  Biomassa1
0.19     CGHPCHBIO 184123496  Biomassa2
0.08     CGHPCHBIO 61835863   Biomassa3

我的目标是：

Maximize the "Return"

我的限制是：

这就是我到目前为止所尝试的：

from pulp import *
import pandas as pd
import xlrd

#First, we create a LP problem with the method LpProblem in PuLP
prob = LpProblem("Selecao de Projetos",LpMaximize)

#Read the first rows dataset in a Pandas DataFrame
df = pd.read_excel("df.xlsx", encoding = 'unicode_escape')

#Create a list of the projects names
projects = list(df['Project_name'])

#Create a dictionary of investments for all the projects
investments = dict(zip(projects,df['Investment']))

#Create a dictionay of sectors for all the projects
sectors = dict(zip(projects,df['Sector']))

#Create a dictionary of Returns for all the projects
returns = dict(zip(projects,df['Return']))

#Create a dictionary of projects with lower bound = 0 and category continuous
project_vars = LpVariable.dicts("Project",projects,lowBound =0,cat='Continuous')

#Built the LP problem by assing the main objective function
prob += lpSum([returns[i]*project_vars[i] for i in projects])

#Add the constraints
prob += lpSum([investments[f] * project_vars[f] for f in projects]) <= 916000000
prob += lpSum([investments[f] * project_vars[f] for f in projects if sectors[f]=="Solar"]) <= lpSum([investments[f] * project_vars[f] for f in projects])*0.6
prob += lpSum([investments[f] * project_vars[f] for f in projects if sectors[f]=="Wind"]) <= lpSum([investments[f] * project_vars[f] for f in projects])*0.6
prob += lpSum([investments[f] * project_vars[f] for f in projects if sectors[f]=="CGHPCHBIO"]) <= lpSum([investments[f] * project_vars[f] for f in projects])*0.25
prob.solve()

#The status of the solution is printed to the screen
print("Status:", LpStatus[prob.status])

for v in prob.variables():
    if v.varValue>0:
        print(v.name, "=", v.varValue)

我得到的结果：

Project_CGH1 = 6.4549114
Project_Eolico1 = 84.982196
Project_Solar1 = 0.60163909

我需要的结果是：在给定约束的情况下，我会选择哪些项目（Project_name）？ 就像是：

Projects chosen   Investment
Solar1            228376120
Wind1             6467237
Wind3             377494250
CGH2              60671402
PCH4              300677487
Biomassa3         61835863

标签： pythonpandasdictionaryconstraintspulp

解决方案

欢迎来到 SO！

假设您想限制这些部门的比率，与所选总投资的百分比，那么您缺少的约束应如下所示：

prob += lpSum([investments[f] * project_vars[f] for f in projects if sectors[f]=="Solar"]) <= lpSum([investments[f] * project_vars[f] for f in projects])*0.6

同样对于您想要限制百分比的其他部门。

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