Python PuLP中的混合整数线性规划不可行解

# 示例约束条件
prob += x + y <= 10
prob += x - y >= 5
prob += x >= 0
prob += y >= 0

# 示例变量
x = LpVariable("x", lowBound=0, upBound=10, cat='Integer')
y = LpVariable("y", lowBound=0, upBound=10, cat='Integer')

# 示例目标函数
prob += x + y

from pulp import *

# 创建问题实例
prob = LpProblem("Example_Problem", LpMinimize)

# 创建变量
x = LpVariable("x", lowBound=0, upBound=10, cat='Integer')
y = LpVariable("y", lowBound=0, upBound=10, cat='Integer')

# 目标函数
prob += x + y

# 约束条件
prob += x + y <= 10
prob += x - y >= 5
prob += x >= 0
prob += y >= 0

# 求解问题
prob.solve()

# 检查求解器状态
print("Status:", LpStatus[prob.status])

# 输出结果
if LpStatus[prob.status] == 'Optimal':
    print("Optimal Solution Found:")
    print("x =", x.varValue)
    print("y =", y.varValue)
else:
    print("No Optimal Solution Found")

# 创建松弛变量
slack = LpVariable("slack", lowBound=0, cat='Continuous')

# 修改约束条件
prob += x + y <= 10 + slack
prob += x - y >= 5 - slack

# 使用不同的求解器
prob.solve(PULP_CBC_CMD())
# 或者
prob.solve(GLPK_CMD())

# 启用日志输出
prob.solve(PULP_CBC_CMD(msg=True))