A Hybrid Genetic Algorithm-Ratio DEA Approach for Assessing Sustainable Efficiency in Two-Echelon Supply Chains

Abstract

Measuring sustainable efficiency is a wide research topic that has gained increased relevance over the course of the years, particularly in the field of supply chain management. In this paper, novel Data Envelopment Analysis—ratio data (DEA-R) models are used to assess sustainable efficiency in two-echelon supply chains based on endogenous factors. Genetic algorithms are employed to determine optimal productive weights for each echelon and the overall supply chain by taking into account the hidden correlation structures among them as expressed in non-linear multi-objective functions. A case study on 20 firefighting stations is presented to illustrate the approach proposed and its accuracy for decision-making, as long as the issues of pseudo inefficiency and over estimation of efficiency scores are mitigated. Results indicate that the method proposed is capable of reducing efficiency estimation biases due to endogenous sustainable factors by yielding overall scores lower than or equal to the product of the efficiencies of the individual stages.