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my code doesnt work well

Answered
Jiwook Sung
Edited
  • Gurobi-versary
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  • First Question

I want to solve this problem below ( which is BCC model )

This is the code that I wrote

def solve_DEA(target, verbose=True):
    
    df_main = pd.read_csv('./df_31_1_outlier_drops_with_SAS.csv')

    inattr = df_main.columns[1:5]
    outattr = df_main.columns[5:]
    dmus = [k for k in range(66)]
    inputs = df_main.iloc[:,1:5].to_dict('index')
    outputs = df_main.iloc[:,5:].to_dict('index')
    u0 = {}
   

    ### Create LP model
    model = gp.Model('VRS_model')
    
    # Decision variables
    wout = model.addVars(outattr, name="outputWeight")
    win = model.addVars(inattr, name="inputWeight")
    u_vrs =model.addVars(dmus, name ="u_vrs")
    
    model.update()

    # Constraints
    ratios = model.addConstrs( ( gp.quicksum(outputs[h][r]*wout[r] - u_vrs[target]  for r in outattr ) 
                                - gp.quicksum(inputs[h][i]*win[i] for i in inattr ) 
                                <= 0 for h in dmus ), name='ratios' )
    
    normalization = model.addConstr((gp.quicksum(inputs[target][i]*win[i] for i in inattr )  == 1 ),
                                    name='normalization')
        
    # Objective function
    
    model.setObjective( gp.quicksum(outputs[target][r]*wout[r] - u_vrs[target] for r in outattr ), GRB.MAXIMIZE)
    
    # Run optimization engine
    if not verbose:
        model.params.OutputFlag = 0
    model.params.NonConvex = 2
    model.optimize()
    
    # Print results
    print(f"\nThe efficiency of target DMU {target} is {round(model.objVal,3)}") 
    
    print("__________________________________________________________________")
    print(f"The weights for the inputs are:")
    for i in inattr:
        print(f"For {i}: {round(win[i].x,3)} ") 
        
    print("__________________________________________________________________")
    print(f"The weights for the outputs are")
    for r in outattr:
        print(f"For {r} is: {round(wout[r].x,3)} ") 
    
    return model.objVal

But what I got from output is same as this problem below (which is CCR model)

 

def solve_DEA(target, verbose=True):
    
    df_main = pd.read_csv('./df_31_1_outlier_drops_with_SAS.csv')

    inattr = df_main.columns[1:5]
    outattr = df_main.columns[5:]
    dmus = [k for k in range(66)]
    inputs = df_main.iloc[:,1:5].to_dict('index')
    outputs = df_main.iloc[:,5:].to_dict('index')
   

    ### Create LP model
    model = gp.Model('DEA')
    
    # Decision variables
    wout = model.addVars(outattr, name="outputWeight")
    win = model.addVars(inattr, name="inputWeight")

    # Constraints
    ratios = model.addConstrs( ( gp.quicksum(outputs[h][r]*wout[r] for r in outattr ) 
                                - gp.quicksum(inputs[h][i]*win[i] for i in inattr ) 
                                <= 0 for h in dmus ), name='ratios' )
    
    normalization = model.addConstr((gp.quicksum(inputs[target][i]*win[i] for i in inattr ) == 1 ),
                                    name='normalization')
    
    # Objective function
    
    model.setObjective( gp.quicksum(outputs[target][r]*wout[r] for r in outattr ), GRB.MAXIMIZE)
    
    # Run optimization engine
    if not verbose:
        model.params.OutputFlag = 0
    model.optimize()
    
    # Print results
    print(f"\nThe efficiency of target DMU {target} is {round(model.objVal,3)}") 
    
    print("__________________________________________________________________")
    print(f"The weights for the inputs are:")
    for i in inattr:
        print(f"For {i}: {round(win[i].x,3)} ") 
        
    print("__________________________________________________________________")
    print(f"The weights for the outputs are")
    for r in outattr:
        print(f"For {r} is: {round(wout[r].x,3)} ") 

 i dont know what is wrong with my code ( or equation) 

 

these are data of my lp model

 

NAME x_1 x_2 x_3 x_4 x_5 x_6 y_1
18 0.337957 0.026104 0.03177 0.166129 0.836921 0.025097 0.000467
19 1 0.799197 0.969743 1 0.836921 0.025097 8.23E-05
20 0 0.236948 0.122542 0.225806 0.836921 0.025097 0.000151
21 0 0.056225 0.122542 0.090323 0.836921 0.025097 4.71E-05
44 0.631641 0.160643 0.077156 0.072258 0.327234 0.443382 0
51 0.347107 0.036145 0.092284 0.096774 0.347578 0.311622 0.000284
6 0.209563 0.196787 0.077156 0.012903 0.169214 0.862265 0.005062
7 0.727568 0.046185 0.228442 0.085484 0.169214 0.862265 0.000513
16 0.061246 0 0.122542 0.413497 0.63972 0.123992 0.000146
22 0.020661 0.37751 0.213313 0.167742 0.823897 0.046908 2.54E-05
23 0.01771 0.196787 0.107413 0.125161 0.823897 0.046908 0.000105
24 0.005903 0.518072 0.288956 0.145161 0.823897 0.046908 2.11E-06
25 0.01771 0.313253 0.742814 0.212903 0.809042 0.042725 2.28E-05
26 0.27745 0.287149 0.213313 0.274194 0.809042 0.042725 4.05E-06
53 0.596222 0.698795 0.016641 0.2 0.373735 0.371377 0.001455
8 0.324675 0.337349 0.319213 0.383226 1 0.072005 0.462689
27 0.079693 0.206827 0.183056 0.004032 0.806243 0.01225 4.54E-05
28 0.042798 0.34739 0.273828 0.009623 0.806243 0.01225 4.50E-05
29 0.070838 0.34739 0.379728 0.126842 0.806243 0.01225 2.42E-05
30 0.005903 0.096386 0.100227 0.16129 0.806243 0.01225 0.000133
31 0.566706 1 0.440242 0.677419 0.806243 0.01225 0.00011
40 0.035419 0.538153 0.364599 0.064516 0.543272 0.279952 0.000129
0 0.024351 0.299197 0.167731 0.141935 0.329279 0.429638 5.63E-05
1 0.024351 0.337349 0.183056 0.080645 0.329279 0.429638 5.02E-05
2 0.005903 0.427711 0.030257 0 0.329279 0.429638 0.000225
3 0.011806 0.598394 0.288956 0.235484 0.329279 0.429638 6.78E-05
4 0.024351 0.879518 0.515885 0.387097 0.329279 0.429638 6.85E-05
9 0.289256 0.267068 0.440242 0.110403 0.249839 0.390798 0.000104
11 0.815821 0.457831 0.001513 0.274194 0.643488 0.239618 0.000267
55 0.036895 0.297189 0.288956 0.096774 0 1 2.03E-05
57 0.636806 0.096386 0.13767 0.145161 0.788159 0.072304 0.000121
5 0.005903 0.146586 0.1059 0.032258 0.266093 0.63161 9.38E-05
33 0.047963 0.457831 0.203253 0.548387 0.710549 0.02928 4.77E-06
34 0.01771 0.698795 1 0.422581 0.710549 0.02928 3.59E-05
35 0.153926 0.066265 0.145234 0.032258 0.710549 0.02928 1.96E-06
36 0.116588 0.136546 0.13767 0.548387 0.710549 0.02928 8.87E-06
37 0.079693 0.497992 0.13767 0.145161 0.710549 0.02928 1.83E-05
41 0.631641 0.497992 0.288956 0.267742 0.419591 0.401554 0.000135
42 0.005903 0.196787 0.591528 0.122903 0.419591 0.401554 0.000219
58 0.024351 0.056225 0.258699 0.513871 0.493434 0.272483 0.000319
12 0.665289 0.317269 0.379728 0.306452 0.623466 0.208844 0.015387
54 0.144628 0.166667 0.34947 0.195161 0.283854 0.373768 0.000281
38 0.079693 0.648594 0.440242 0.315435 0.795264 0 3.00E-05
39 0.318772 0.548193 0.288956 0.29129 0.795264 0 4.38E-05
13 0.061246 0.046185 0 0 0.55479 0.226471 6.07E-05
47 0.011806 0.120482 0.426626 0.096774 0.189882 0.310128 8.45E-05
59 0.301063 0.216867 0.742814 0.630174 0.727234 0.150881 1
60 0.43595 0.146586 0.24357 0.225806 0.567815 0.25366 3.53E-06
45 0.324675 0.156627 0.281392 0.064516 0.33746 0.453839 2.04E-05
48 0.063164 0.899598 0.288956 0.258065 0.251346 0.18046 0.000145
43 0.005903 0.365462 0.319213 0.387097 0.386006 0.494771 8.00E-05
0

Comments

3 comments

Date Votes
  • Official comment
    Simranjit Kaur
    • Gurobi Staff
    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.
  • Jaromił Najman
    • Gurobi Staff

    Please note that your code is not reproducible because a minimal set of data is missing.

    It looks like

    ( gp.quicksum(outputs[h][r]*wout[r] - u_vrs[target]  for r in outattr ) 

    should be

    ( gp.quicksum(outputs[h][r]*wout[r]  for r in outattr )  + u_vrs[target]

    in your \(\texttt{ratios}\) constraints. The same applies to your \(\texttt{normalization}\) constraint.

    It is best to write out the LP file

    model.write("myModel.lp")

    to check whether the constraints look what they should like. This is the easiest way to debug issues like this one.

    1
  • Jiwook Sung
    • Gurobi-versary
    • First Comment
    • First Question

    i update the post so now you could access the data that i use

    and i solve the problem thank you!

    0

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