Multi-objective data envelopment analysis model in fuzzy dynamic environment with missing values

Abstract

Data envelopment analysis (DEA) is an important managerial tool for evaluating and improving the performance of decision making units. The existing DEA models are mostly limited to static environment using crisp data and are time-consuming and also have weak discriminating power. The aim of this work is to introduce a new fuzzy dynamic DEA model with missing values, which benefits from strengths of multi-objective modeling to overcome weakness and drawbacks of the classic DEA models. To check for quality and accuracy of the proposed model, this paper offers a comparative study to compare the discriminating power and computational efforts of the model with two problems in the literature taken as benchmarks. Also, this paper presents a real application of the fuzzy dynamic DEA model for assessing and ranking the level of performance for 56 railways around the globe using real data gathered from credible sources. The numerical case illustrates the model and the result may be used by railways to improve their performance efficiency compared to the best in the sample. Results for the comparative study and the real case reveal significant improvement in computational time and discriminating power.