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编辑：我还添加了一个约束 1.5 来说明可能以不同的方式接近约束。

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：

在此处输入图像描述

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意图的反馈。想法是6x6上的每个（i，j）单元格网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。

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1 回答 1

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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，您必须记住，您的 K 和 T 集在某处结束，如果没有 k+1，则 k+1 的表达式不起作用。可以提到的另一点是，如果您从考虑k+1 == something的第一个 k 值开始，则 k = 2。

我希望这会有所帮助，也许有人也知道一些更聪明的东西，我对 pyomo 也很陌生。

于 2019-08-14T20:20:58.353 回答