Ignored upper bound set on variable
回答済みHi,
I have a set of variables that are semi-continuous with an upper and lower bound placed.
x = model.addVars(times,zones, name = "x", ub=ub, lb = lb, vtype= 'S')
ub and lb are defined in a big list prior to this line. I found out that at least the upper bound was being ignored with no warning thrown after looking through the results and seeing x values being much too high. Why is it getting ignored?
As additional info, ub is set as
ub = {"R1":epcompile["R1"].max(), ....
and I have manually checked the max value of epcompile['R1'] and ub and they line up but the results of the optimization break this bound exceedingly.
Thanks for any help,
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Gurobi Staff
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Hi Nicolas,
Could you post a minimal working example to make the issue reproducible as described in Posting to the Community Forum?
Best regards,
Jaromił1 -
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Hi,
Sorry it has taken a while to respond. I was away and it would take me time to make a minimal working example. I have made one now and I hope you can solve my issues.
from gurobipy import *
import numpy as np
import pandas as pd
import numpy as np
zones = ['R1','SO1','MO1','LO3','H4']
times = [str(i) for i in range(1,1440)]
epcompile = pd.read_excel('miniwork.xlsx', index_col=0)
cum_epcompile= epcompile.cumsum()
ub = {"R1":20.75, "SO1":epcompile["SO1"].max(),"MO1":27.933,
"LO3":epcompile["LO3"].max(),"H4":epcompile["H4"].max()}
lb = {"R1":0.8, "SO1":0.36,"MO1":1.0,"LO3":9.7,"H4":13.59}
model = Model('flex')
#Variables
x = model.addVars(times,zones, name = "x", ub=ub, lb = lb, vtype= 'S')
e = model.addVars(times,zones, name = "e", vtype = 'C')
cumm = model.addVars(times,zones, name = "cumm",vtype = 'C')
peak = model.addVar(name = "peak", vtype = 'C')
#Constraints
model.addConstrs(cumm[times[i], zone] == cum_epcompile.loc[cum_epcompile.index[i], zone] for zone in zones for i in range(1439))
model.addConstrs(e[times[0], zone] == cum_epcompile.loc[cum_epcompile.index[0], zone] for zone in zones)
model.addConstrs(e[time,zone] == x[times[time_index-1], zone] + e[times[time_index-1], zone] for zone in zones for time_index, time in enumerate(times)
if time != times[0])
model.addConstrs(e[time,zone] >= (0.98)*cumm[time,zone] for time in times for zone in zones
if time != times[0])
model.addConstrs(e[time,zone] <= 200+(1.02)*cumm[time,zone] for time in times for zone in zones
if time != times[0])
model.addConstrs(x[time,zone] >= x[times[time_index-1],zone]-0.2*ub[zone] for zone in zones for time_index, time in enumerate(times)
if time != times[0])
model.addConstrs(x[time,zone] <= x[times[time_index-1],zone]+0.2*ub[zone] for zone in zones for time_index, time in enumerate(times)
if time != times[0])
model.addConstrs(peak >= sum(x[time, zone] for zone in zones) for time in times)
model.addConstrs(e[times[1438],zone] <=1.0001*cumm[times[1438],zone] for zone in zones)
#Objective
model.setObjective(peak, GRB.MINIMIZE)
model.Params.timeLimit = 800
model.optimize()An excel sheet is needed for the data that is used. I don't see a way of simplifying it feasibly. Is there a way I can email it. It needs to follow the data for one of the constraints.
After the optimization is solved I can see that the solution goes against BOTH the upper and lower bounds of the 'x' variable. 'x' is a semi-continuous variable, meaning it can be 0 or between the upper and lower bound (my understanding). Using the following code it can be seen it does not stay between the bounds ub and lb.
matrix_agg = [[x[a,b].X for a in times] for b in zones]
agg = pd.DataFrame(matrix_agg, columns=times, index=zones)
agg.loc['aggregate', times] = agg.sum(axis=0)
with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also
print(agg.loc['LO3',times])The x values for LO3 are printed. The upper and lower bounds are 15.7 and 9.7, respectively. The values are far beyond both bounds.
Thank you for any help and please let me know how I can send the excel sheet so it can be ran.
Nico
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Small addition:
I did test out adding another constraint to x in the code
model.addConstrs(x[time,zone] <=ub[zone] for time in times for zone in zones)
This successfully constrains 'x', however, I do not know a way to place a lower bound constraint while still allowing 'x' to equal 0 as well. If the resolution cannot be found for fixing ub and lb, it would be good to know how to do this at least.
Thanks
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The indices of the \( \texttt{x} \) variables are all combinations of \( \texttt{times} \) and \( \texttt{zones} \):
x = model.addVars(times, zones, ub=ub, lb=lb, vtype='S', name='x')However, the keys for the \( \texttt{lb} \) and \( \texttt{ub} \) dictionaries you pass as arguments to Model.addVars() are \( \texttt{zones} \):
# only indexed by zones
lb = {"R1": 0.8, "SO1": 0.36,"MO1": 1.0,"LO3": 9.7,"H4": 13.59}
ub = {"R1": 20.75, "SO1": epcompile["SO1"].max(),"MO1": 27.933,
"LO3": epcompile["LO3"].max(),"H4": epcompile["H4"].max()}This isn't a supported way of setting bounds with Model.addVars(). From the Model.addVars() documentation:
You can also provide a Python
dictas the argument. In that case, the value for each variable will be pulled from the dict, using the indices argument to build the keys. For example, if the variables created by this method are indexed asx[i,j], then thedictprovided for the argument should have an entry for each possible(i,j)value.None of the \( \texttt{dict} \) keys match the indices argument, so the bounds are never properly set. To fix the issue, modify \( \texttt{lb} \) and \( \texttt{ub} \) so the keys match the indices of the \( \texttt{x} \) variables. E.g.:
lb2 = {(i, j): lb[j] for i in times for j in zones}
ub2 = {(i, j): ub[j] for i in times for j in zones}
x = model.addVars(times, zones, ub=ub2, lb=lb2, vtype='S', name='x')1 -
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Thank you so much. This worked perfectly and you explanation was great. I also want to say that from what I have seen on other posts you guys are great help in this community. I am sure everyone appreciates it!
Nico
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