import pulp

z = [2, 3, 1]

a = [[1, 4, 2], [3, 2, 0]]

b = [8, 6]

aeq = [[1,2,4]]

beq = [101]

#确定最大化最小化问题，最大化只需将Min改成Max即可

m = pulp.LpProblem(sense=pulp.LpMinimize)

# 定义三个变量放到列表中

x = [pulp.LpVariable(f'x{i}', lowBound=0) for i in [1, 2, 3]]

#定义目标函数，lopDot可以将两个列表的对应位相乘再相加

#相当于z[0]*x[0]+....

m += pulp.lpDot(z,x)

#设置不等式约束条件

for i in range(len(a)):

    m += pulp.lpDot(a[i], x) >= b[i]

#设置等式约束条件

for i in range(len(aeq)):

    m += pulp.lpDot(aeq[i],x) == beq[i]

#求解

m.solve()

#输出结果

print(f'优化结果：{pulp.value(m.objective)}')

print(f'参数取值：{[pulp.value(var) for var in x]}')

import numpy as np

import pulp as pl

def main():

    ProbLp=pl.LpProblem("ProbLp",sense=pl.LpMaximize)

    print(ProbLp.name)

    x1=pl.LpVariable('x1',lowBound=0,upBound=None,cat='Integer')

    x2=pl.LpVariable('x2',lowBound=0,upBound=2,cat='integer')

    ProbLp+=(x1+4*x2)

    ProbLp+=(-2*x1+3*x2<=3)

    ProbLp+=(x1+2*x2<=8)

    ProbLp.solve()

    print("Shan Status:", pl.LpStatus[ProbLp.status])  # 输出求解状态

    for v in ProbLp.variables():

        print(v.name, "=", v.varValue)  # 输出每个变量的最优值

    print("F(x) =", pl.value(ProbLp.objective))  # 输出最优解的目标函数值

if __name__ =='__main__':

    main()

ProbLp

Shan Status: Optimal

x1 = 4.0

x2 = 2.0

F(x) = 12.0