Question

这是我正在处理“项目选择”问题的数据框：

$args = array(
    'timeout'     => 10,
    'sslverify' => false
);

我的目标是：

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

我的约束是：

“投资”总额的限制为916000000；
“太阳能行业”不能超过总投资的 60％；
“风能领域”不能超过投资总额的 60％；和
“ CGHPCHBIO部门”不能超过投资总额的 25％

那是我到目前为止所尝试的：

Maximize the "Return"

我得到的结果：

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)

我需要的结果是：给定约束，我将选择哪个项目（Project_name）？ 像这样：

Project_CGH1 = 6.4549114
Project_Eolico1 = 84.982196
Project_Solar1 = 0.60163909

Answer 1

欢迎您！

假设您想将这些部门的比例限制为所选总投资的百分比，那么缺少的约束应该如下：

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

对于其他要限制百分比的部门也是如此。

在项目选择问题中创建约束

1 个答案: