gurobipy：使用 python API 编写分段线性约束SOS2 约束的最佳方式

如何解决gurobipy：使用 python API 编写分段线性约束SOS2 约束的最佳方式

我通过 gurobipy（能量存储优化）制定了简单的 MILP 问题。我想引入允许进入存储和库存的最大和最小流量之间的非线性关系（存储中已经存储了多少介质）。我是通过以下丑陋的 for 循环来实现的（inventory 和 flow 是通过 gurobipy.Model.addMVar() 方法获得的一维数组变量，而 m 是我的 gurobipy.Model 类） :

# PWL - injection rate constrain
ir_x = [0.0,8000.0,12500.0]
ir_y = [5000.0,5000.0,2500.0]
for a,b in zip(inventory.tolist(),injection_rate.tolist()):
    m.addGenConstrPWL(a,b,ir_x,ir_y)
m.addConstr(flow <= injection_rate)

是否有一些聪明的方法可以在不实际使用 for 循环的情况下做到这一点？

整个工作示例：

import gurobipy as gp
import numpy as np

LEN = 10 # months
MAXIMAL_CAPACITY = 50000.0 # [MWh]
MINIMAL_CAPACITY = 0.0 # [MWh]
INITIAL_STORAGE = 0.0 # [MWh]
END_STORAGE = 0.0 # [MWh]

# create model
m = gp.Model(name="Simple Energy Storage")

# variables
flow = m.addMVar(LEN,lb=float("-inf"),name="flow")
inventory = m.addMVar(LEN,name="inventory")
injection_rate = m.addMVar(LEN,name="injection rate")
withdrawal_rate = m.addMVar(LEN,name="withdrawal rate")

# create inventory condition (cumulative sum of flow)
mat = np.tril(np.ones(shape=(LEN,LEN)))
m.addConstr(mat @ flow == inventory)

# inventory constrains
m.addConstr(inventory <= MAXIMAL_CAPACITY)
m.addConstr(inventory >= MINIMAL_CAPACITY)
m.addConstr(inventory[0] == INITIAL_STORAGE)
m.addConstr(inventory[-1] == END_STORAGE)

# PWL - injection rate constrain
ir_x = [0.0,ir_y)
m.addConstr(flow <= injection_rate)

# PWL - withdrawal rate constrain
wr_x = [0.0,6000.0,12500.0]
wr_y = [3000.0,6500.0,6500.0]
for a,withdrawal_rate.tolist()):
    m.addGenConstrPWL(a,wr_x,wr_y)
m.addConstr(flow >= -withdrawal_rate)

# objective function
m.setobjective(inventory[5],gp.GRB.MAXIMIZE)

m.optimize()