Fuzzy efficiency evaluation in relational network data envelopment analysis: application in gas refineries

Abstract

In contrast to classical data envelopment analysis (DEA), network DEA has attention to the internal structure of a production system and reveals the relationship between the efficiency of system and efficiencies of the processes. However, the flexibility of weights and the need for crisp input and output data in the evaluation process are two major shortcomings of classical network DEA models. This paper presents a common weights approach for a relational network DEA model in a fuzzy environment to measure the efficiencies of the system and the component processes. The proposed approach first finds upper bounds on input and output weights for a given cut level and then it determines a common set of weights (CSW) for all decision-making units (DMUs). Hence, the fuzzy efficiencies of all processes and systems for all DMUs are obtained based on the resulting CSW. The developed fuzzy relational network DEA and the proposed common weights approach are illustrated with a numerical example. The obtained results confirm that the fuzzy data affects over the efficiency scores and complete ranking of DMUs. The applicability of the proposed network model is illustrated by performance evaluation of gas refineries in Iran.

Enhancing Efficiency Assessment of Egyptian Pharmaceutical Companies through the Usage of Two-Stage Uncertainty Data Envelopment Analysis Algorithm

“…[28] pioneered the use of the triangular fuzzy approach, representing FS with a triangular membership function. Building on this, subsequent studies by [35,36,7,37], and others applied fuzzy network DEA. Further expansions in the approaches to encounter the negative or zero values in inputs and outputs of the data were suggested by [38,39,40,41,42,43,44] by proposing a dynamic fuzzy network DEA approach.…”

“…Moreover, this approach allows for a thorough examination of different areas for improvement and the efficient allocation of resources, empowering decision-makers to optimize their decision-making process [6]. Moreover, DEA is a popularly employed data-driven mathematical modeling approach with broad application for performance evaluation of pharmaceutical manufacturing sector, banks, hospitals, universities, investment firms, gas companies, etc., in terms of efficiency score [7]. Although there is a lot of application for the DEA model and effective in assessing institutions' performance, the model has some drawbacks; First, the DEA model cannot identify the most critical factors influencing efficiency.…”