Variables return wrong values
回答済みI have added my variables as seen below however instead of getting a list for each of the variables, I only get the following results:
Variable x
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C131 7000
C257 1858.76
Can someone suggest what I am doing wrong?
Thank you
m = gp.Model('OptimisationModel')
centers = np.array([(0,0), (0,1), (0,2), (1,0), (1,1), (1,2), (2,0), (2,1), (2,2)])
facilities = np.array([(0,0), (0,1), (0,2)])
# Indices for the facilities
facilitiesIndex = [*range(1,3)] #this needs to reflect the count from db
# Indices for the counties / later to be post codes/ as in GPs
countiesIndex = [*range(1,9)] #this needs to reflect count the db
#time period - 14 days
period = np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14])
periodIndex = [*range(1,14)] #this needs to reflect count the db
#minimum amount of dose made by one reactor per day (one minute run per day)
minDosePerR = 600
#max number of hours every reactor can run for (8 hours a day)
maxRunTime = 8*60
#reactor count max
rMax = 8
#county population - DEMAND
CountyPopulation = 7000
#nurse number of admissions per day
nurse_ratePerDay = 28
#max number of nurses in each clinic to administrate vaccines
nurse_max = 8
#cost per day per nurse
costPerNurse = 105
#cost to set up one reactor + storage staff and the rest for 14 days
costPerFacilitySetup = 10000
#cost of travel per mile by vehicle
costPerMile = 10
maxDr = 288600
#capacitytoStoreAtGP
capacityToStorePerGP = 399
#capacityToStoreAtHospital
capacityTOStorePerHospital = 2000
#storagecostPerUnitAtGP
storageCostPerDoseGP = 0.90
#StoragecostPerUnitAtHospital
storageCostPerDoseHospital = 0.79
budget = 200000
# Compute key parameters of MIP model formulation
num_facilities = len(facilities)
num_customers = len(centers)
num_period = len(period)
cartesian_prod_fc = list(product(range(num_facilities), range(num_customers)))
#Select whether facility is selected to be a factory
select = m.addVars(num_facilities, vtype=GRB.BINARY, name='Select')
#number of reactors at facility f at period t [1,.....f-1]
rn = m.addVars(num_facilities, vtype=GRB.CONTINUOUS)
#number of allocated doses from facility f for center c at period t [[num_customers lenght],....,[num_customers lenght-1]]
allocList = m.addVars(num_period,num_customers, vtype=GRB.CONTINUOUS)
#number of nurses at location c [[num_customers lenght],....,[num_customers lenght-1]]
ncList = m.addVars(num_period,num_customers, vtype=GRB.CONTINUOUS)
m.addConstr((allocList.sum() <= CountyPopulation), name='demandConstraints')
m.addConstr(((allocList.sum()*0.69) +
(ncList.sum()*105) +
(rn.sum()* maxRunTime*0.70) +
(rn.sum()* 10000)
<= budget), name='budgetConstraints')
# for f in facilitiesIndex:
# m.addConstrs((rn[i] <= rMax for i in range(num_facilities)), name='Setup2ship')
m.setObjective((rn.sum()*maxRunTime*600)-
(allocList.sum())-
(ncList.sum()*28), GRB.MINIMIZE)
m.optimize()
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正式なコメント
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Gurobi Staff
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I solved your model and it looks like all variables are equal to zero except for the two in your printout:
C131 7000
C257 1.8587619047619048e+03It looks like you are probably using Model.printAttr('X') to view your variable solution values. For brevity, this function prints all non-zero variable values. This is why you are only seeing the two, non-zero values --- rather than the full list of variables.
You can confirm by viewing the values of your full variable list. This can be done in a few different ways. My two other favorites are:
- Write a SOL file by running the following after you optimize the model:
m.write('results.sol') - Creating a dataframe with the results by running the following after you optimize:
import pandas as pd
# Store Variable info
varInfo = [(v.varName, v.X, v.LB, v.UB) for v in m.getVars() ]
df = pd.DataFrame(varInfo)
df.columns=['Variable Name','Solution Value', 'LB','UB']
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@..., thank you for your response. Yes makes sense. Any suggestion why they are zero? Because they shouldn't be! Thank you very much
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Can you tell me a bit about why you would expect more to be non-zero? Are there specific variables you would expect to be non-zero?
From what I can tell, within each of the variable tupledicts there is no differentiation between variables. This can lead to some redundancy.
For example, let's look at the \(\texttt{allocList}\) tupledict. Each one of the 100+ \(\texttt{allocList}\) variables, has the same objective coefficient: -1. Each of the \(\texttt{allocList}\) variables participates in two constraints, again each with the same coefficient:
m.addConstr((allocList.sum() <= 7000), name='demandConstraints')
m.addConstr(((allocList.sum()*0.69) +
(ncList.sum()*105) +
(rn.sum()* maxRunTime*0.70) +
(rn.sum()* 10000)
<= budget), name='budgetConstraints')Lastly, there are no upper or lower bounds on any of the variables in the \(\texttt{allocList}\) list. So, as far as the math program is concerned all of the \(\texttt{allocList}\) variables are identical. The only thing that limits them is the demand constraint. Therefore, the following solution values of \(\texttt{allocList}\) would all be equivalent:
Solution 1 Solution 2 Solution 3 allocList[0,0] 7,000 - 1,000 allocList[0,1] - 7,000 1,000 allocList[0,2] - - 1,000 allocList[0,3] - - 4,000 Total 7,000 7,000 7,000 This means that there could be MANY values of \(\texttt{allocList}\) that would satisfy the constraints and get the same objective. This makes me think there is something missing from your model that will lead to a solution that is a bit more realistic and closer to what you would expect. This could include using your three \(\texttt{Select}\) binary variables. Or, it may mean creating constraints that are associated with some of your unused constants like \(\texttt{capacityToStorePerGP}\), \(\texttt{capacityTOStorePerHospital}\), \(\texttt{nurse_ratePerDay}\), and \(\texttt{nurse_max}\).
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