maxz= 1.15x4A+1.40x2c+1.25x3в +1.06x5d 满足约束条件 X1A+X1D =100000 -1.06x1D+X2A+X2C+X2D =0 -1.15x1A-1.06 X2D +X3A +X3b +X3D =0 - 1.15x2A -1.06 X3D +X4A +X4D =0 -1.15x3A-1.06 x4D +X5D =0 X2c ≤30000 ≤40000 X3B xiA,XiB, xic, XiD ≥0 (i=1,2,3,4,5)
时间: 2023-05-21 13:06:30 浏览: 190
这是一个线性规划问题,可以使用线性规划算法求解。您可以使用Python中的PuLP、SciPy或CVXpy等库来解决这个问题。以下是PuLP库的一个示例代码:
```python
from pulp import *
# Create the problem
prob = LpProblem("Maximize Profit", LpMaximize)
# Create the variables
x1a = LpVariable("x1a", 0)
x1d = LpVariable("x1d", 0)
x2a = LpVariable("x2a", 0)
x2c = LpVariable("x2c", 0)
x2d = LpVariable("x2d", 0)
x3a = LpVariable("x3a", 0)
x3b = LpVariable("x3b", 0)
x3d = LpVariable("x3d", 0)
x4a = LpVariable("x4a", 0)
x4d = LpVariable("x4d", 0)
x5d = LpVariable("x5d", 0)
# Define the objective function
prob += 1.15 * x1a + 1.40 * x2c + 1.25 * x3b + 1.06 * x5d
# Define the constraints
prob += x1a + x1d == 100000 - 1.06 * x1d
prob += x2a + x2c + x2d == 0 - 1.15 * x1a - 1.06 * x2d
prob += x3a + x3b + x3d == 0 - 1.15 * x2a - 1.06 * x3d
prob += x4a + x4d == 0 - 1.15 * x3a - 1.06 * x4d
prob += x5d == 0
prob += x2c <= 40000
prob += x2c >= 30000
# Solve the problem
prob.solve()
# Print the results
print("Optimal Solution:")
for v in prob.variables():
print(v.name, "=", v.varValue)
print("Total Profit =", value(prob.objective))
```
其中,每个变量都表示相应材料的数量,目标函数是最大化利润,约束条件包括原材料的剩余量、生产总量以及其他限制。运行代码后,将输出最优解和总利润。
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