问题描述：
对于01背包问题，简历数学模型，使用python的cplex模块解决问题，找到最大解。
代码实现：
# -*- coding: utf-8 -*-
# The MIP problem solved in this example is:
#(2,6),(2,3),(6,5),(5,4),(4,6)，：：(2,1),(3,3),(6,9),(5,7),(4,5)，：：(2,2),(2,4.5),(6,7.5),(5.5,4。5),(7.4,8.6）
# Maximize 6x1 + 3 x2 + 5 x3 + 4 x4 +6 x5+ x6 +3x7 + 9 x8+7 x9+5 x10+ 2 x11 +4.5 x12+7.5 x13 + 4.5x14 +8.6x15= 价值最大化
# Subject to
# (2,6),(2,3),(6,5),(5,4),(4,6)，：：(2,1),(3,3),(6,9),(5,7),(4,5)，：：(2,2),(2,4.5),(6,7.5),(5.5,4。5),(7.4,8.6） 重量小于背包的盛重量
# 2x1 + 2 x2 + 6 x3 + 5 x4 +4 x5+ 2 x6 +3x7 + 6 x8+7 x9+5 x10+ 2 x11 +2 x12+6 x13 + 5.5 x14 +7.4x15
# -x2+ x5<=0 2号背包必须在五号背包之前放入
# Bounds 对于各个背包要么取要么不取0 1二值问题
# 0 <= x1 <= 1
# 0 <= x2 <= 1
# 0 <= x3 <= 1
# 0 <= x4 <= 1
# 0 <= x5 <= 1
# 0 <= x6 <= 1
# 0 <= x7 <= 1
# 0 <= x8 <= 1
# 0 <= x9 <= 1
# 0 <= x10 <= 1
# 0 <= x11 <= 1
# 0 <= x12 <= 1
# 0 <= x13 <= 1
# 0 <= x14 <= 1
# 0 <= x15 <= 1
# Integers
# x1,x2,x3,x4,x5，x6,x7,x8,x9,x10,x11,x12,x13,x14,x15
import cplex
from cplex.exceptions import CplexError
# (2,6),(2,3),(6,5),(5,4),(4,6)，：：(2,1),(3,3),(6,9),(5,7),(4,5)，：：(2,2),(2,4.5),(6,7.5),(5.5,4。5),(7.4,8.6）
# data common to all populateby functions
my_obj = [6.0, 3.0, 5.0, 4.0, 6.0, 1.0, 3.0, 9.0, 7.0, 5.0, 2.0, 4.5, 7.5, 4.5, 8.6]
my_ub = [1.0, 1.0, 1.0, 1.0, 1.0,1.0, 1.0, 1.0, 1.0, 1.0,1.0, 1.0, 1.0, 1.0, 1.0]
my_lb = [0.0, 0.0, 0.0, 0.0, 0.0,0.0, 0.0, 0.0, 0.0, 0.0,0.0, 0.0, 0.0, 0.0, 0.0]
my_ctype = "IIIIIIIIIIIIIII" #表示参数的类型 c应该表示数值，I表示整数
my_colnames = ["x1", "x2", "x3", "x4", "x5","x6", "x7", "x8", "x9", "x10","x11", "x12", "x13", "x14", "x15"] #表示实验的
my_rhs = [40,0,0]
my_rownames = ["r1", "r2","r3"]
my_sense = 'LLL'
def populatebyrow(prob):
prob.objective.set_sense(prob.objective.sense.maximize)
# 指要优化的目标函数是要求最大化
prob.variables.add(obj=my_obj, lb=my_lb, ub=my_ub, types=my_ctype,
names=my_colnames)
rows = [[["x1", "x2", "x3", "x4", "x5","x6", "x7", "x8", "x9", "x10","x11", "x12", "x13", "x14", "x15"], [2.0, 2.0, 6.0, 5.0, 4.0,2.0, 3.0, 6.0, 5.0, 4.0,2.0,2.0,6.0,5.5,7.4 ]],
[["x2","x5"],[-1,1]],
[["x7","x12"],[-1, 1]]
]
# 上面是背包问题所要求的，后面是所要附加的约束条件，即要求2号背包必须在五号背包之前放入
prob.linear_constraints.add(lin_expr=rows, senses=my_sense,
rhs=my_rhs, names=my_rownames)
try:
my_prob = cplex.Cplex()
handle = populatebyrow(my_prob)
my_prob.solve()
except CplexError as exc:
print(exc)
print()
# solution.get_status() returns an integer code
print("Solution status = ", my_prob.solution.get_status(), ":", end=' ')
# the following line prints the corresponding string
print(my_prob.solution.status[my_prob.solution.get_status()])
print("Solution value = ", my_prob.solution.get_objective_value())
numcols = my_prob.variables.get_num()
numrows = my_prob.linear_constraints.get_num()
slack = my_prob.solution.get_linear_slacks()
x = my_prob.solution.get_values()
print('x: ')
print(x)
结果：
实验结果