尝试了不同的公式，但代码无法运行。

huangapple
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英文:

tried different formulation but the code won't run

问题

import pyomo.environ as pyo
import pandas as pd


model = pyo.ConcreteModel()


model.Iset = pyo.Set(initialize=range(1, 44))
model.Bset = pyo.Set(initialize=[1,2])

X = pd.read_excel("D:\\Project 2\\DebrisFlow.xlsx", sheet_name='X', header=0, index_col=0)
X=X.values
X={(i):X[i] for i in model.Iset}

Y = pd.read_excel("D:\\Project 2\\DebrisFlow.xlsx", sheet_name='Y', header=0, index_col=0)
Y=Y.values
Y={(i):Y[i] for i in model.Iset}

M = pd.read_excel("D:\\Project 2\\DebrisFlow.xlsx", sheet_name='w', header=0, index_col=0)
M=M.values
M={(i):M[i] for i in model.Iset}

model.X=pyo.Param(model.Iset,initialize=X)
model.Y=pyo.Param(model.Iset,initialize=Y)
model.M=pyo.Param(model.Iset,initialize=M)

model.e = pyo.Var(model.Iset, domain=pyo.NonNegativeReals)
model.c = pyo.Var(model.Bset, domain=pyo.Reals)
model.d = pyo.Var(model.Bset ,domain=pyo.Reals)
model.delta = pyo.Var(model.Iset, model.Bset, domain=pyo.Binary)

def obj(model):
    return sum(model.e[i] for i in model.Iset)

model.obj = pyo.Objective(rule=obj,sense=pyo.minimize)


def const1_rule(model,i,b):
    return model.Y[i]-(model.c[b]*model.X[i])-model.d[b]<=model.e[i]+model.M[i]-(model.M[i]*model.delta[i,b])

model.const1 = pyo.Constraint(model.Iset, model.Bset, rule=const1_rule)


def const2_rule(model,i,b):
    return (model.c[b]*model.X[i])+model.d[b]-model.Y[i]<=model.e[i]+model.M[i]-(model.M[i]*model.delta[i,b])

model.const2 = pyo.Constraint(model.Iset, model.Bset, rule=const2_rule)


def const3_rule(model,b):
    return pyo.inequality(0.0537, model.c[b], 0.1245)

model.const3 = pyo.Constraint(model.Bset,rule=const3_rule)

def const4_rule(model,b):
    return pyo.inequality(-26.0627, model.d[b], 10.3121)

model.const4 = pyo.Constraint(model.Bset, rule=const4_rule)

def const5_rule(model,i):
    return sum(model.delta[i, b] for b in model.Bset) == 1

model.const5 = pyo.Constraint(model.Iset, rule=const5_rule)




opt = pyo.SolverFactory('glpk')
result=opt.solve(model,'glpk')

print (str(result.solver))

print(pyo.value(model.obj))
英文:

I want to implement CLR model in pyomo.
It seems the runtime is endless because when I run it nothing will happen and just a star comes in and won't end running.
I've tried with different formulation of const5 but it doesn't work. even I've tried solving with only const1 and const2 and it worked. and solving by only const5 it works but it doesn't work when I add all together.
I would appreciate if someone can help me.
this is the mathematical model:
尝试了不同的公式，但代码无法运行。

I have the values of X,Y and in M in excel file and they are correct.
this is my code:

import pyomo.environ as pyo
import pandas as pd
model = pyo.ConcreteModel()
model.Iset = pyo.Set(initialize=range(1, 44))
model.Bset = pyo.Set(initialize=[1,2])
X = pd.read_excel(&quot;D:\\Project 2\\DebrisFlow.xlsx&quot;, sheet_name=&#39;X&#39;, header=0, index_col=0)
X=X.values
X={(i):X[i] for i in model.Iset}
Y = pd.read_excel(&quot;D:\\Project 2\\DebrisFlow.xlsx&quot;, sheet_name=&#39;Y&#39;, header=0, index_col=0)
Y=Y.values
Y={(i):Y[i] for i in model.Iset}
M = pd.read_excel(&quot;D:\\Project 2\\DebrisFlow.xlsx&quot;, sheet_name=&#39;w&#39;, header=0, index_col=0)
M=M.values
M={(i):M[i] for i in model.Iset}
model.X=pyo.Param(model.Iset,initialize=X)
model.Y=pyo.Param(model.Iset,initialize=Y)
model.M=pyo.Param(model.Iset,initialize=M)
model.e = pyo.Var(model.Iset, domain=pyo.NonNegativeReals)
model.c = pyo.Var(model.Bset, domain=pyo.Reals)
model.d = pyo.Var(model.Bset ,domain=pyo.Reals)
model.delta = pyo.Var(model.Iset, model.Bset, domain=pyo.Binary)
def obj(model):
return sum(model.e[i] for i in model.Iset)
model.obj = pyo.Objective(rule=obj,sense=pyo.minimize)
def const1_rule(model,i,b):
return model.Y[i]-(model.c[b]*model.X[i])-model.d[b]&lt;=model.e[i]+model.M[i]-(model.M[i]*model.delta[i,b])
model.const1 = pyo.Constraint(model.Iset, model.Bset, rule=const1_rule)
def const2_rule(model,i,b):
return (model.c[b]*model.X[i])+model.d[b]-model.Y[i]&lt;=model.e[i]+model.M[i]-(model.M[i]*model.delta[i,b])
model.const2 = pyo.Constraint(model.Iset, model.Bset, rule=const2_rule)
def const3_rule(model,b):
return pyo.inequality(0.0537, model.c[b], 0.1245)
model.const3 = pyo.Constraint(model.Bset,rule=const3_rule)
def const4_rule(model,b):
return pyo.inequality(-26.0627, model.d[b], 10.3121)
model.const4 = pyo.Constraint(model.Bset, rule=const4_rule)
def const5_rule(model,i):
return sum(model.delta[i, b] for b in model.Bset) == 1
model.const5 = pyo.Constraint(model.Iset, rule=const5_rule)
opt = pyo.SolverFactory(&#39;glpk&#39;)
result=opt.solve(model,&#39;glpk&#39;)
print (str(result.solver))
print(pyo.value(model.obj))

答案1

得分: 0

我认为你的模型没问题。它具有弹性约束，我没有看到任何问题。我认为无论出于什么原因，glpk 都在处理它时遇到了困难。

当我插入下面的“小数据”时，你的模型几乎可以立即用 glpk 解决。如果我增加 |I| 到约20，它会变慢，而当达到44时，似乎需要很长时间。所以你可能只是在等待求解器，这有点奇怪，因为这是一个混合整数线性规划 (MILP)，但不是很大的一个。

总之，如果你有另一个求解器，请尝试使用它。当我使用 cbc 时，对于 |I| 约为44，求解大约需要1秒。

你可以通过注释掉下面的部分来在小数据和较大数据之间切换。

# small test...  works / optimal
Y = { 1: 13, 2: 10, 3: 5}
X = { 1: 10, 2: 15, 3: 2}
M = { 1: 99, 2: 99, 3: 99}

# big test:
Y = {k: random()*100 for k in range(1, i_size)}
X = {k: random()*100 for k in range(1, i_size)}
M = {k: 200 for k in range(1, i_size)}

代码：

import pyomo.environ as pyo
from random import random

# ...（以下省略）

希望这可以帮助你解决问题。

英文:

I think your model is fine. It has elastic constraints and I don't see anything wrong with it. I think that for whatever reason glpk is just struggling with it.

When I patched in the "small data" below, your model solves almost instantly with glpk. If I go to |I| ~20 it bogs down, and 44 seems like an eternity. So you are probably just waiting on the solver, which is odd because this is a MILP, but not a large one.

Anyhow, if you have another solver, try it. When I use cbc the solve is about 1 second for |I| ~44

You can toggle between the small data and the larger stuff by commenting out sections below.

Code:

import pyomo.environ as pyo
from random import random
# import pandas as pd
i_size = 45
model = pyo.ConcreteModel()
model.Iset = pyo.Set(initialize=range(1, i_size))
model.Bset = pyo.Set(initialize=[1,2])
# X = pd.read_excel(&quot;D:\\Project 2\\DebrisFlow.xlsx&quot;, sheet_name=&#39;X&#39;, header=0, index_col=0)
# X=X.values
# X={(i):X[i] for i in model.Iset}
# Y = pd.read_excel(&quot;D:\\Project 2\\DebrisFlow.xlsx&quot;, sheet_name=&#39;Y&#39;, header=0, index_col=0)
# Y=Y.values
# Y={(i):Y[i] for i in model.Iset}
# M = pd.read_excel(&quot;D:\\Project 2\\DebrisFlow.xlsx&quot;, sheet_name=&#39;w&#39;, header=0, index_col=0)
# M=M.values
# M={(i):M[i] for i in model.Iset}
# small test...  works / optimal
Y = { 1: 13, 2: 10, 3: 5}
X = { 1: 10, 2: 15, 3: 2}
M = { 1: 99, 2: 99, 3: 99}
# big test:
Y = {k: random()*100 for k in range(1, i_size)}
X = {k: random()*100 for k in range(1, i_size)}
M = {k: 200 for k in range(1, i_size)}
model.X=pyo.Param(model.Iset,initialize=X)
model.Y=pyo.Param(model.Iset,initialize=Y)
model.M=pyo.Param(model.Iset,initialize=M)
model.e = pyo.Var(model.Iset, domain=pyo.NonNegativeReals)
model.c = pyo.Var(model.Bset, domain=pyo.Reals)
model.d = pyo.Var(model.Bset ,domain=pyo.Reals)
model.delta = pyo.Var(model.Iset, model.Bset, domain=pyo.Binary)
def obj(model):
return sum(model.e[i] for i in model.Iset)
model.obj = pyo.Objective(rule=obj,sense=pyo.minimize)
def const1_rule(model,i,b):
return model.Y[i]-(model.c[b]*model.X[i])-model.d[b]&lt;=model.e[i]+model.M[i]-(model.M[i]*model.delta[i,b])
model.const1 = pyo.Constraint(model.Iset, model.Bset, rule=const1_rule)
def const2_rule(model,i,b):
return (model.c[b]*model.X[i])+model.d[b]-model.Y[i]&lt;=model.e[i]+model.M[i]-(model.M[i]*model.delta[i,b])
model.const2 = pyo.Constraint(model.Iset, model.Bset, rule=const2_rule)
def const3_rule(model,b):
return pyo.inequality(0.0537, model.c[b], 0.1245)
model.const3 = pyo.Constraint(model.Bset,rule=const3_rule)
def const4_rule(model,b):
return pyo.inequality(-26.0627, model.d[b], 10.3121)
model.const4 = pyo.Constraint(model.Bset, rule=const4_rule)
def const5_rule(model,i):
return sum(model.delta[i, b] for b in model.Bset) == 1
model.const5 = pyo.Constraint(model.Iset, rule=const5_rule)
opt = pyo.SolverFactory(&#39;glpk&#39;)
result=opt.solve(model) #,&#39;glpk&#39;)
print (str(result.solver))
print(pyo.value(model.obj))
model.delta.display()

huangapple
  • 本文由 发表于 2023年6月25日 22:50:24
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