空モデルを取得するpyomo

Question

私の人生にとって、私はpyomoプログラムを実行することができません。

私はPythonのファイルを持っている：あなたはソートの他に1から物事をマップする、これがどのように動作するかの私の理解から

set warehouses := warehouseone warehousetwo warehousethree warehousefour; 
set stores := storeone storetwo storethree storefour storefive storesix; 

param cost: 
           storeone storetwo storethree storefour storefive storesix := 
    warehouseone    23  12 34  25  27 16  
    warehousetwo    29  24 43  35  28 19  
    warehousethree    43  31 52  36  30 21  
    warehousefour    54  36 54  46  34 27; 



param m := 4 ; 
param n := 6 ; 

param: a:= 
warehouseone 15 
warehousetwo 25 
warehousethree 40 
warehousefour 70; 


param: b := 
warehouseone 10 
warehousetwo 5 
warehousethree 7 
warehousefour 4; 


param: d := 
storeone  45 
storetwo  120 
storethree 165 
storefour 214 
storefive 64 
storesix  93; 

：

from pyomo.environ import * 
#pyomo solve --solver=glpk diet.py diet.dat 
model = AbstractModel() 

# Foodss 

model.m = Param(within=NonNegativeIntegers) 
model.n = Param(within=NonNegativeIntegers) 

model.warehouses = RangeSet(1, model.m) 
model.stores = RangeSet(1, model.n) 




model.cost = Param(model.warehouses,model.stores) 
model.a = Param(model.warehouses) 
model.b = Param(model.warehouses) 
model.d = Param(model.stores) 
model.amounts = Var(model.warehouses, model.stores, within = NonNegativeIntegers) 
model.pprint() 






# Minimize the cost of food that is consumed 
def cost_rule(model): 
    return sum(
     model.cost[n,i] * model.amounts[n,i] 
     for n in model.warehouses 
     for i in model.stores 
    ) 
model.cost = Objective(rule=cost_rule) 

def minDemandRule(store, model): 
    return sum(model.a[i]*model.amounts[i, store] for i in model.warehouses) >= model.d[store] 
model.demandConstraint = Constraint(model.stores, rule=minDemandRule) 
# Limit the volume of food consumed 
def maxSupplyRule(warehouse,model): 
    return sum(model.amounts[warehouses,j] for j in model.stores) <= self.b[warehouse] 
model.supplyConstraint = Constraint(model.warehouses, rule=maxSupplyRule) 

プラス.datファイルを。これは私にとっては大丈夫だと思うが、私がそれを実行するときだ。 DATファイルが正しく読み込まれていませんように

pyomo solve --solver=glpk transport.py data.dat 

[ 0.00] Setting up Pyomo environment 
[ 0.00] Applying Pyomo preprocessing actions 
2 Set Declarations 
    amounts_index : Dim=0, Dimen=2, Size=0, Domain=None, Ordered=True, Bounds=None 
     Virtual 
    cost_index : Dim=0, Dimen=2, Size=0, Domain=None, Ordered=True, Bounds=None 
     Virtual 

2 RangeSet Declarations 
    stores : Dim=0, Dimen=1, Size=0, Domain=None, Ordered=True, Bounds=None 
     Not constructed 
    warehouses : Dim=0, Dimen=1, Size=0, Domain=None, Ordered=True, Bounds=None 
     Not constructed 

6 Param Declarations 
    a : Size=0, Index=warehouses, Domain=Any, Default=None, Mutable=False 
     Not constructed 
    b : Size=0, Index=warehouses, Domain=Any, Default=None, Mutable=False 
     Not constructed 
    cost : Size=0, Index=cost_index, Domain=Any, Default=None, Mutable=False 
     Not constructed 
    d : Size=0, Index=stores, Domain=Any, Default=None, Mutable=False 
     Not constructed 
    m : Size=1, Index=None, Domain=NonNegativeIntegers, Default=None, Mutable=False 
     Not constructed 
    n : Size=1, Index=None, Domain=NonNegativeIntegers, Default=None, Mutable=False 
     Not constructed 

1 Var Declarations 
    amounts : Size=0, Index=amounts_index 
     Not constructed 

11 Declarations: m n warehouses stores cost_index cost a b d amounts_index amounts 
WARNING: Implicitly replacing the Component attribute cost (type=<class 'pyomo.core.base.param.IndexedParam'>) on block unknown with a new Component (type=<class 'pyomo.core.base.objective.SimpleObjective'>). 
    This is usually indicative of a modelling error. 
    To avoid this warning, use block.del_component() and block.add_component(). 
[ 0.01] Creating model 
ERROR: Constructing component 'a' from data={'warehousefour': 70, 'warehouseone': 15, 'warehousethree': 40, 'warehousetwo': 25} failed: 
    RuntimeError: Failed to set value for param=a, index=warehousefour, value=70. 
     source error message="Error setting parameter value: Index 'warehousefour' is not valid for array Param 'a'" 
[ 0.02] Pyomo Finished 
ERROR: Unexpected exception while running model: 
    Failed to set value for param=a, index=warehousefour, value=70. 
     source error message="Error setting parameter value: Index 'warehousefour' is not valid for array Param 'a'" 

は私が感じるが、私は他の例を見ると、これは、それがどのように行われるかであるので、私は少し困惑です。

Answer 1

誤ったpyomoの使用法から、誤植のコードにいくつかの問題がありました。以下は修正版です。それがあなたのために働いていて、より具体的な質問がある場合は、新しい質問を投稿してください。

ファイルdiet.py：

from pyomo.environ import * 
#pyomo solve --solver=glpk diet.py diet.dat 
model = AbstractModel() 

# Foodss 

model.warehouses = Set() 
model.stores = Set() 


model.a = Param(model.warehouses) 
model.b = Param(model.warehouses) 
model.d = Param(model.stores) 
model.cost = Param(model.warehouses, model.stores) 
model.amounts = Var(model.warehouses, model.stores, within = NonNegativeIntegers) 
model.pprint() 


# Minimize the cost of food that is consumed 
def cost_rule(model): 
    return sum(
     model.cost[n,i] * model.amounts[n,i] 
     for n in model.warehouses 
     for i in model.stores 
    ) 
model.costObjective = Objective(rule=cost_rule) 

def minDemandRule(model, store): 
    return sum(model.a[i]*model.amounts[i, store] for i in model.warehouses) >= model.d[store] 
model.demandConstraint = Constraint(model.stores, rule=minDemandRule) 

# Limit the volume of food consumed 
def maxSupplyRule(model, warehouse): 
    return sum(model.amounts[warehouse,j] for j in model.stores) <= model.b[warehouse] 
model.supplyConstraint = Constraint(model.warehouses, rule=maxSupplyRule) 

ファイルdiet.dat：

param: warehouses: 
       a b := 
    warehouseone 15 10 
    warehousetwo 25 5 
    warehousethree 40 7 
    warehousefour 70 4; 


param: stores: 
       d := 
    storeone  45 
    storetwo  120 
    storethree 165 
    storefour 214 
    storefive 64 
    storesix  93; 


param cost: 
           storeone storetwo storethree storefour storefive storesix := 
    warehouseone     23  12 34  25  27 16 
    warehousetwo     29  24 43  35  28 19 
    warehousethree    43  31 52  36  30 21 
    warehousefour    54  36 54  46  34 27; 

例の実行（私はソルバーここCLPを使用したが、校長はないのです注意してください）：

$ pyomo solve --solver=clp test.py test.dat 
[ 0.00] Setting up Pyomo environment 
[ 0.00] Applying Pyomo preprocessing actions 
4 Set Declarations 
    amounts_index : Dim=0, Dimen=2, Size=0, Domain=None, Ordered=False, Bounds=None 
     Virtual 
    cost_index : Dim=0, Dimen=2, Size=0, Domain=None, Ordered=False, Bounds=None 
     Virtual 
    stores : Dim=0, Dimen=1, Size=0, Domain=None, Ordered=False, Bounds=None 
     Not constructed 
    warehouses : Dim=0, Dimen=1, Size=0, Domain=None, Ordered=False, Bounds=None 
     Not constructed 

4 Param Declarations 
    a : Size=0, Index=warehouses, Domain=Any, Default=None, Mutable=False 
     Not constructed 
    b : Size=0, Index=warehouses, Domain=Any, Default=None, Mutable=False 
     Not constructed 
    cost : Size=0, Index=cost_index, Domain=Any, Default=None, Mutable=False 
     Not constructed 
    d : Size=0, Index=stores, Domain=Any, Default=None, Mutable=False 
     Not constructed 

1 Var Declarations 
    amounts : Size=0, Index=amounts_index 
     Not constructed 

9 Declarations: warehouses stores a b d cost_index cost amounts_index amounts 
[ 0.00] Creating model 
[ 0.02] Applying solver 
[ 0.03] Processing results 
    Number of solutions: 1 
    Solution Information 
     Gap: None 
     Status: optimal 
     Function Value: 532.113571429 
    Solver results file: results.json 
[ 0.04] Applying Pyomo postprocessing actions 
[ 0.04] Pyomo Finished 
$