To resolve this difficulty, we introduce the useful notion of maximal reference set (MRS) which contains all the reference DMUs.
Model.obj=pyo.Objective(expr=model.eff, sense=pyo. In data envelopment analysis (DEA), the occurrence of multiple reference sets is a crucial issue in identifying all the reference DMUs to a given decision making unit (DMU). Model.landa = pyo.Constraint(expr=sum(model.lan for n in n) = 1) Model.input = pyo.Constraint(expr=sum(model.lan*inputs for n in n) = outputs Model.eff = pyo.Var(n,within=pyo.NonNegativeReals) Model.lan = pyo.Var(n,within=pyo.NonNegativeReals) Print('your matrix id : '.format( len(df.index),len(df.columns))) # finding the number of inputs and outputs
I try to calculate efficiency of 5 DMUS with BCC data envelopment analysis by the following code in pyomo but my code does not work and that would be My pleasure if you correct me: ( in line 39 I get error 'None of, dtype ='object')] are in the ') import pandas as pdĭf = pd.read_excel('e:\\sample.xlsx',sheet_name='data')
