화학공학소재연구정보센터
Industrial & Engineering Chemistry Research, Vol.57, No.30, 9866-9878, 2018
Mixed-Integer Programming Approach for Dimensionality Reduction in Data Envelopment Analysis: Application to the Sustainability Assessment of Technologies and Solvents
Data Envelopment Analysis (DEA) has recently emerged as an effective method for the sustainability assessment of industrial systems. Unfortunately, sustainability studies require the evaluation of a wide range of indicators (i.e., inputs and outputs in DEA notation), which can weaken the discriminatory power of DEA and ultimately lead to results that are less meaningful and hard to interpret. Here we develop a systematic MIP-DEA approach that identifies redundant metrics that can be omitted in DEA models with minimum information loss. Our approach is based on a bilevel programming model where binary variables denote the selection of metrics while the objective functions and constraints are formulated according to the DEA principles. The capabilities of this method are illustrated through the assessment of several industrial systems evaluated according to multiple criteria, some of which are based on life cycle metrics. Our results show that our systematic approach can effectively reduce the number of variables in DEA studies. This method can also be used to enhance the discriminatory power of DEA by diminishing the number of units deemed efficient considering a maximum allowable error.