采用Gurobi(python)求解优化问题的主要结构如下：
1.引入Gurobi环境：from gurobipy import *
2.创建Gurobi模型：Model()
3.声明变量：Model.addVar()
4.输入约束：Model.addConstr()
5.输入目标函数：Model.addConstr()
6.模型求解：Model.optimize().
eg:求解如下数学模型
求解代码如下：
from gurobipy import *
# 模型名称
Dcx = Model()
# 声明变量
x1 = Dcx.addVar(lb = 0,ub = GRB.INFINITY, name = "x1")
x2 = Dcx.addVar(lb = 0,ub = GRB.INFINITY, name = "x2")
x3 = Dcx.addVar(lb = 0,ub = GRB.INFINITY, name = "x3")
x4 = Dcx.addVar(lb = 0,ub = GRB.INFINITY, name = "x4")
x5 = Dcx.addVar(lb = 0,ub = GRB.INFINITY, name = "x5")
# 添加约束
Dcx.addConstr(x1 + 2*x2 + x4 == 100, "CON1")
Dcx.addConstr(2*x3 + 2*x4 + x5 == 100, "CON2")
Dcx.addConstr(3*x1 + x2 + 2*x3 + 3*x5 == 100, "CON3")
# 建立目标函数
Dcx.setObjective(0*x1 + 0.1*x2 + 0.2*x3 + 0.3*x4 + 0.8*x5,GRB.MINIMIZE)
# 输出名为‘dcx’的 .lp文件
Dcx.write("dcx.lp")
# 模型求解
Dcx.optimize()
print('***************************')
print('最优解： ')
print('Z = ',Dcx.ObjVal) # 输出目标值
print('x1 =',x1.x) # 输出 X1 的值
print('x2 =',x2.x)
print('x3 =',x3.x)
print('x4 =',x4.x)
print('x5 =',x5.x)
print('***************************')
求解结果如下：
LP文件如下：
