Decision making (DM), i.e., multi-criteria decision-making (MCDM) or multi-criteria decision analysis (MCDA) is a sub-discipline of operations research (OR) that clearly analyses multiple conflicting criteria in decision-making problems and considers the details as much as possible. Data envelopment analysis (DEA) is a widely used nonparametric linear programming method for assessing the efficiency and productivity of decision-making units (DMUs). The theory and applications of DEA are widespread and very diverse. This is compared or combined with other disciplines or subjects in the field such as truncated, tobit, or ordinary regression analysis, analytic hierarchy process, cluster analysis, principal component analysis, factor analysis, multi-criteria decision analysis, time series analysis, and fuzzy sets. The assessment of alternatives is a difficult and complex task due to several kinds of variables that become related to specific decisions including environmental, social, physical, organizational social, and professional criteria in many real-world problems.

Over the past few years, researchers have developed several DM and DEA (DMDEA) methods to help decision-makers with the analysis and solution of decision-making problems in various challenging real-world problems. As a result of these various interactions, both the model structure of the DMDEA and the application areas of the DMDEA have been expanded, and derivative models such as free disposal hull (FDH) and stochastic frontier analysis (SFA) have emerged. With regard to model structure, there are many other studies which focus on directional distance measurement, Russell measure, and slack-based measure, etc. which contain interpretations related to the analytical significance of DEA and efficiency decompositions. DMDEA is widely used in the area of energy, service sector, education, banking, commerce, agriculture, and so forth as an application for measuring efficiency and inefficiency. Depending on the structure of the data, different models are proposed for fuzzy, uncertain, or other types of data such as negative, categorical, qualitative, or interval data. In addition, studies and benchmarking of tests and methods that will measure the validity and reliability of these models have an important place in DEA research. Moreover, interdisciplinary research is required to address this.

Consequently, this Special Issue aims to attract research focusing on DMDEA and related fields. We welcome original research and review papers which aim to address future challenges and trends in DMDEA.

Potential topics include but are not limited to the following:

• Application of DMDEA in business, management and industrial engineering
• Decision analysis and data envelopment analysis and uncertainty in data envelopment analysis
• Target setting in data envelopment analysis and new modeling in DMDEA
• Efficiency analysis for manufacturing and engineered systems
• Measurement of performance in emerging economies and new leaders
• Operations management and industrial engineering, supply chain and logistics management
• Transportation systems and management and information systems and e-business
• Forecasting and predictive analytics
• Reliability and maintenance engineering
• Big data and analytics, performance analysis, computational intelligence
• Neural networks and deep learning algorithms
• Fuzzy set and its extensions, fuzzy optimization
• Competitiveness index
• Measurement of environmental efficiency
• Transportation and logistics insights through DMDEA