Unable to convert argument to an expression - Objective Function Error

回答済み

Dorthe Alida Slotvik

2022年05月03日 11:37

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I have a simple model where I try to minimize total costs. I read the data from an Excel-file and define sets and parameter data from this. The error "Unable to convert argument to an expression" occurs when defining the objective function. 

 

The objective function is as follows:

obj = m.setObjective(sum(C_NB[s]*y[s] for s in Systems) + sum(C_LO[s]*y[s] for s in Systems) + sum(B*C_fuel[f]*x[f] for f in Fuels), GRB.MINIMIZE)

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• 正式なコメント

  

  Simranjit Kaur

  • Gurobi Staff

  2025年11月16日 23:38

  This post is more than three years old. Some information may not be up to date. For current information, please check the Gurobi Documentation or Knowledge Base. If you need more help, please create a new post in the community forum, or try Gurobot, our chatbot interface offering instant, expert-level support.
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  Jaromił Najman

  • Gurobi Staff

  2022年05月03日 11:45 編集済み

  Could you please provide a minimal reproducible example showing this issue. It would be best if you hard code a small example with your excel data which results in the error you describe.

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  Dorthe Alida Slotvik

  2022年05月03日 12:49 編集済み

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  The code runs when I hard code an example from the excel-data. So it seems like there is a problem in the definition of sets and parameters imported from the excel file. 

   

  The hard code:

  import pandas as pd
  import gurobipy as gp
  from gurobipy import *
  import numpy as np
  from gurobipy import GRB
  import matplotlib.pyplot as plt
  
  
  
  ## Sets ##
  Fuels = [0, 1, 2, 3]
  Systems = [0, 1, 2]
  
  ## Parameters ##
  C_NB = np.array([30, 37, 45]) #C_NB[s], data from systems
  C_fuel = np.array([30, 37, 45, 47]) #C_fuel[f], data from fuels
  C_LO = np.array([0, 0.4, 2.7]) #C_LO[s], data from systems
  B = 0.17

  ## Creating the model ##
  m = gp.Model('Model Name')
  
  ## Decision variables ##
  x = m.addVars(Fuels, vtype=GRB.BINARY, name='x')
  y = m.addVars(Systems, vtype=GRB.BINARY, name='y')

  ## Constraints ##
  
  m.addConstr((sum(x[f] for f in Fuels) == 1))
  
  m.addConstr((sum(y[s] for s in Systems) == 1))

  ## Objective function ##
  
  obj = m.setObjective(sum(C_NB[s]*y[s] for s in Systems) + sum(C_LO[s]*y[s] for s in Systems) + sum(B*C_fuel[f]*x[f] for f in Fuels), GRB.MINIMIZE)
  
  ## Solve Model ##
  m.optimize()
  m.write("solution.sol")
  
  obj = m.getObjective
  
  def printSolution():
    if m.status == GRB.OPTIMAL:
          print('\nTotal Cost: %g' % m.ObjVal)
    else:
          print('No solution')
  
  printSolution()

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  Dorthe Alida Slotvik

  2022年05月03日 12:54 編集済み

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  The code importing data from excel:

   

  import pandas as pd
  import gurobipy as gp
  from gurobipy import *
  import numpy as np
  from gurobipy import GRB
  import matplotlib.pyplot as plt

  dfF = pd.read_excel(r'C:\Users\xxx\Desktop\Progging\opt_input2.xlsx', sheet_name = 'fuels', index_col='Fuel') #place "r" before the path string to address special character, such as '\'
  dfF.index
  
  dfS = pd.read_excel(r'C:\Users\xxx\Desktop\Progging\opt_input2.xlsx', sheet_name = 'systems', index_col='Power System') #index_col=0?
  dfS.index
  
  

  ## Sets ##
  
  Fuels = [f for f in range(dfF.shape[0])]
  
  Systems = [s for s in range(dfS.shape[0])]

  ## Parameters ##
  
  C_NB1 = dfS[['Newbuild Cost']] # [mUSD]
  C_NB2 = C_NB1.values.tolist()
  C_NB = np.asarray(C_NB2)
  
  C_fuel1 = dfF[['Fuel Cost']] # [USD/MWh]
  C_fuel2 = C_fuel1.values.tolist()
  C_fuel = np.asarray(C_fuel2)
  
  C_LO1 = dfS[['Lost Opportunity Cost']] # Lost opportunity cost per 10 years[mUSD]
  C_LO2 = C_LO1.values.tolist()
  C_LO = np.asarray(C_LO2) 
  
  B = 0.17 

  ## Creating the model ##
  m = gp.Model('Model Name')
  
  ## Creating decision variables ##
  x = m.addVars(Fuels, vtype=GRB.BINARY, name='x')
  y = m.addVars(Systems, vtype=GRB.BINARY, name='y')
  
  

  ## Constraints ##
  m.addConstr((sum(x[f] for f in Fuels) == 1))
  m.addConstr((sum(y[s] for s in Systems) == 1))

  ## Objective function ##
  
  obj = m.setObjective(sum(C_NB[s]*y[s] for s in Systems) + sum(C_LO[s]*y[s] for s in Systems) + sum(B*C_fuel[f]*x[f] for f in Fuels), GRB.MINIMIZE)
  
  

  ## Solve Model ##
  
  m.optimize()
  m.write("solution.sol")
  
  obj = m.getObjective
  
  
  def printSolution():
      if m.status == GRB.OPTIMAL:
          print('\nTotal Cost: %g' % m.ObjVal)
      else:
          print('No solution')
        
  printSolution()

   

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  Jaromił Najman

  • Gurobi Staff

  2022年05月03日 14:37

  You could check whether the lists you hard coded vs lists you construct from your Excel files have the same shape. In particular, check the data structures of 

  Fuels, Systems, C_NB, C_fuel, C_LO

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  Dorthe Alida Slotvik

  2022年05月03日 14:57

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  Thank you for the help! I had added an extra [] in the definition of the parameters, which gave additional square brackets in the lists. 

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