1．使用工具 PULP+GLPK

PULP 是Python PULP是用python写的建模描述语言，GLPK是线性规划工具

2.下载、安装Python、PULP 和GLPK

以Python 2.7+PULP 1.4.8 +GLPK4.49 为例

Python 下载地址：http://www.python.org/getit/

GLPK 下载地址：http://www.lupaworld.com/proj-cont-id-121212.html

PUPL 下载地址：http://www.coin-or.org/download/source/PuLP/

PUPL Windows安装：

1．解压下载好的文件（如解压到C:\Python27）

2. 点击开始—运行 键入cmd 进入命令行，键入cd C:\Python27\PuLP-1.4.8更改路径到setup.py所在的文件夹

3.键入setup.py install 安装PUPL

4.测试是否安装成功 import PULP

 pulp.pulpTestAll()

 

GLPK安装 1.解压下载好的文件（如解压到C:\glpk-4.49）

3.求解过程

Formulate the Objective Function

The objective function becomes:

min 0:013x1 + 0:008x2

The Constraints

The constraints on the variables are that they must sum to 100 and that the nutritional requirements are met:

1:000x1 + 1:000x2 = 100.0

0:100x1 + 0:200x2 >= 8.0

0:080x1 + 0:100x2 >=6.0

0:001x1 + 0:005x2 >=2.0

0:002x1 + 0:005x2 >=0.4

Solution to Simplified Problem

from pulp import *

##Create the ’prob’ variable to contain the problem data

prob = LpProblem("The Whiskas Problem",LpMinimize)

x1=LpVariable("ChickenPercent",0,None,LpInteger)

x2=LpVariable("BeefPercent",0,None, LpInteger)

prob += 0.013*x1 + 0.008*x2

## The five constraints are entered

prob += x1 + x2 == 100

prob += 0.100*x1 + 0.200*x2 >= 8.0

prob += 0.080*x1 + 0.100*x2 >= 6.0

prob += 0.001*x1 + 0.005*x2 <= 2.0

prob += 0.002*x1 + 0.005*x2 <= 0.4

# The problem data is written to an .lp file

prob.writeLP("WhiskasModel.lp")

# The problem is solved using PuLP’s choice of Solver

prob.solve(“C:\\glpk-4.49\\w32\\glpsol.exe”)

# prob.solve()也可以，或者将C:\glpk-4.49\w32\glpsol.exe加入到path 用#prob.solve(glpk())

# The status of the solution is printed to the screen

print "Status:", LpStatus[prob.status]

for v in prob.variables():

    print v.name, "=", v.varValue

print "Total Cost of Ingredients per can = ", value(prob.objective)

 

-----------------------Result ---------------------------------------

Status: Optimal

ChickenPercent = 34.0

BeefPercent = 66.0

Total Cost of Ingredients per can =  0.97