pulp http://pythonhosted.org/PuLP/main/basic_python_coding.html

供水问题

1问题

供水公司有三个水库分别为A,B,C向四个小区甲乙丙丁供水,A和B向所有小区供水,C仅向甲乙丙供水,水库最大供水量(千吨)

水库

A

B

C

最大供水量(千吨)

50

60

50

小区用水情况为

小区

基本用水量(千吨)

30

70

10

10

额外用水量(千吨)

50

70

20

40

水库供水收入900元/千吨,支出费用为:其他费用450/千吨,引水管理费:

由水库到小区引水费用:

元/千吨

A

160

130

220

170

B

140

130

190

150

C

190

200

230

问 如何分配水库供水量,公司才能获利最多。

2 解题思路

使引水管理费最小,则利润最高。

设:水库i向小区j的供水量为,总引水管理费为Z

目标函数为:z = 160.0*x11 + 130.0*x12 + 220.0*x13 + 170.0*x14 + 140.0*x21 + 130.0*x22 + 190.0*x23 + 150.0*x24 + 190.0*x31 + 200.0*x32 + 230.0*x33

求的值,使Z最小。

约束条件为:

python 之pulp 线性规划介绍及举例-LMLPHP

 # coding=utf-8

 from pulp import *

 def get_re():
pass def getresult(c, con): # 设置对象
prob = LpProblem('myPro', LpMinimize)
# 设置三个变量,并设置变量最小取值 x11 = LpVariable("x11", lowBound=0)
x12 = LpVariable("x12", lowBound=0)
x13 = LpVariable("x13", lowBound=0)
x14 = LpVariable("x14", lowBound=0)
x21 = LpVariable("x21", lowBound=0)
x22 = LpVariable("x22", lowBound=0)
x23 = LpVariable("x23", lowBound=0)
x24 = LpVariable("x24", lowBound=0)
x31 = LpVariable("x31", lowBound=0)
x32 = LpVariable("x32", lowBound=0)
x33 = LpVariable("x33", lowBound=0) X = [x11, x12, x13, x14, x21, x22, x23, x24, x31, x32, x33] #c = [160, 130, 220, 170, 140, 130, 190, 150, 190, 200, 230] # 目标函数
z = 0
for i in range(len(X)):
z += X[i]*c[i]
#print(z)
prob += z # 载入约束变量
prob += x11+x12+x13+x14 == con[0]# 约束条件1
prob += x21+x22+x23+x24 == con[1]
prob += x31+x32+x33 == con[2] prob += x11+x21+x31 <= con[3]
prob += x11+x21+x31 >= con[4] prob += x12 + x22 + x32 <= con[5]
prob += x12 + x22 + x32 >= con[6] prob += x13 + x23 + x33 <= con[7]
prob += x13 + x23 + x33 >= con[8]
prob += x14 + x24 <= con[9]
prob += x14 + x24 >= con[10] # 求解 status = prob.solve() print(status)
print(LpStatus[status])
print(value(prob.objective)) # 计算结果 # 显示结果
# for i in prob.variables():
# print(i.name + "=" + str(i.varValue))
for i in prob.variables():
print(i.varValue) if __name__ == '__main__':
c = [160, 130, 220, 170, 140, 130, 190, 150, 190, 200, 230]
con = [50, 60, 50, 80, 30, 140, 70, 30,10, 50, 10]
getresult(mubiao, yueshu)

输出结果:

1
Optimal
24400.0
0.0
50.0
0.0
0.0
0.0
50.0
0.0
10.0
40.0
0.0
10.0

04-30 01:11