供应链选址优化代码示例
时间: 2023-09-09 15:06:45 浏览: 61
当涉及到供应链选址优化时,可以使用数学规划方法来解决问题。下面是一个使用Python和PuLP库实现供应链选址优化的简单代码示例:
```python
from pulp import *
# 创建问题
prob = LpProblem("Supply Chain Location", LpMinimize)
# 定义变量
locations = ["Location1", "Location2", "Location3"] # 候选选址
demand = {"Location1": 100, "Location2": 200, "Location3": 150} # 需求量
supply = {"Location1": 150, "Location2": 100, "Location3": 200} # 供应量
costs = {"Location1": {"Location1": 0, "Location2": 5, "Location3": 10},
"Location2": {"Location1": 5, "Location2": 0, "Location3": 8},
"Location3": {"Location1": 10, "Location2": 8, "Location3": 0}} # 运输成本
# 定义决策变量
var = LpVariable.dicts("Routes", (locations, locations), lowBound=0, cat='Integer')
# 定义目标函数
prob += lpSum([costs[i][j] * var[i][j] for i in locations for j in locations])
# 定义约束条件
for j in locations:
prob += lpSum([var[i][j] for i in locations]) == demand[j] # 需求满足约束
for i in locations:
prob += lpSum([var[i][j] for j in locations]) == supply[i] # 供应满足约束
# 求解问题
prob.solve()
# 打印结果
print("Total Cost = ", value(prob.objective))
for v in prob.variables():
if v.varValue > 0:
print(v.name, "=", v.varValue)
```
这段代码使用了线性规划来解决供应链选址优化问题。它考虑了候选选址、需求量、供应量以及运输成本,并通过求解线性规划问题来确定最优的供应链选址方案。请注意,这只是一个简单的示例,实际应用中可能需要考虑更多的约束条件和变量。
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