Pyomo：如何为每个 (i,j) 对编写约束

Question

编辑：我还添加了约束 1.5 来说明处理约束的不同方法。

我正在尝试在 Pyomo 中为 MxN 网格上的每个 (i,j) 对编写以下约束：

到目前为止我的代码如下，我只是希望我能得到一些关于约束定义是否正确编写以满足 intention.The 想法的反馈，即每个 (i ,j) 6x6 网格上的单元格将具有以下两个约束。

model = AbstractModel()

#Define the index sets for the grid, time horizions, and age classes:
model.Iset = RangeSet(6)
model.Jset = RangeSet(6)
model.Tset = RangeSet(7)
model.Kset = RangeSet(50)

#Define model parameters:
model.s = Param(within=NonNegativeIntegers)

#Define model variables:
model.juvenille = Var(model.Iset, model.Jset, model.Tset, model.Kset,
              within=NonNegativeReals, initialize = "some expression"

#Constraints: 

# Constraint #1
def juv_advance(model, i, j, t, k):
    return model.juvenille[i,j,t+1,k+1] == model.juvenille[i,j,t,k]*model.juvsurv

# Constraint #1.5
def juv_advance(model, t, k):
    return model.juvenille[t+1,k+1] == model.juvenille[t,k]*model.s \
           for i in model.Iset for j in model.Jset

# Constraint #2
def juv_total(model, i, j, t, k):
    return sum(model.juvenille[k] for k in range(1,50))

此外，如果有人想回答这个问题...您如何保存计算出的 j_t+1 值以用作下一个时间段的初始值。

Answer 1

我会尝试这样的事情：

model = AbstractModel()

#Define the index sets for the grid, time horizions, and age classes:
model.Iset = RangeSet(6)
model.Jset = RangeSet(6)
model.Tset = RangeSet(7)
model.Kset = RangeSet(50)

#Define model parameters:
model.s = Param(within=NonNegativeIntegers)

#Define model variables:
model.juvenille = Var(model.Iset, model.Jset, model.Tset, model.Kset,
              within=NonNegativeReals, initialize="some expression")

# As far as I see your problem in you second constraint the big J is a new variable ? 
 If that is the case than you have to create it:

model.J_big =Var(model.Iset, model.Jset, model.Tset, within=NonNegativeReals)

#Constraints: 

# Constraint #1
def juv_advance(model, i, j, t, k):
k_len = len(model.Kset)
t_len = len(model.Tset)
    if k == 1 and t == 1:
         return "some expression"
    elif t < t_len and k < k_len:
         return model.juvenille[i,j,t+1,k+1] == model.juvenille[i,j,t,k]*model.s
    else:
         return "Here has to come a statement what should happen with the last index (because if you are iterating to k=50 there is no k=51) " 


model.ConstraintNumber1 = Constraint(model.Iset, model.Jset, model.Tset, model.Kset, rule=juv_advance)


# Constraint #2
def juv_total(model, i, j, t, k):
    return model.J_big[i,k,j] == sum(model.juvenille[i,j,t,k] for k in model.Kset)

model.ConstraintNumber2 = Constraint(model.Iset, model.Jset, model.Tset, rule=juv_total)

重要的是，您不仅要定义约束规则，还要定义约束本身。此外，您还必须记住，您的 K 和 T 集合在某处结束，并且如果没有 k+1，则 k+1 的表达式不起作用。可以提及的另一点是，如果您从 k+1 == something 开始，则考虑的第一个 k 值是 k = 2。

希望对你有所帮助，也许有人也知道一些更聪明的东西，我对 pyomo 也很陌生。

Pyomo：如何为每个 (i,j) 对编写约束

Pyomo: How to write a constraint for each (i,j) pair

optimization

pyomo

operations-research