Data Envelopment Analysis for Decision Support

A Special Issue of Algorithms (ISSN 1999-4893) belonging to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 2581

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Guest Editor
Department of Statistics, Federal University of Pernambuco, Recife 50740-560, Brazil
Interests: data envelopment analysis; efficiency analysis; DEA; decision support systems; benchmarking; multiple criteria decision analysis; MCDA; time series analysis; ARIMA models; spatial statistics; GIS; bibliometrics; operations research
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Special Issue Information

Dear Colleagues,

The seminal development of nonparametric radial measures for technical efficiency in scenarios with multiple inputs and outputs has become crucial to Productivity and Efficiency Analysis. These advances have strengthened the theoretical foundation of Data Envelopment Analysis (DEA), making it an effective and objective decision support tool for both scholars and practitioners. DEA is widely used as a decision support tool across various sectors of economic activity, including agriculture, education, utilities, environment, finance, healthcare, manufacturing, public administration, transport, sports, and macroeconomics. Rapid progress in algorithms and software continues to increase DEA’s practical value for strategic and operational decision-making.

This Special Issue invites contributions that advance DEA, with a focus on decision support applications and novel methods using nonparametric frontier estimations. We particularly seek submissions where the core contribution lies in novel algorithmic developments, including their design, analysis, implementation, and validation. Submissions should clearly articulate the algorithmic advancement and its impact on decision support capabilities. Submissions on theoretical and empirical progress are encouraged, including but not limited to:

  • Nonparametric frontier applications;
  • Theoretical discussions and new model developments in DEA;
  • Reviews, surveys, and meta-surveys;
  • Papers reporting new algorithms, software, or computational developments;
  • Decision support systems based on DEA and frontier methods;
  • Integration of DEA with artificial intelligence and machine learning for enhanced decision-making;
  • Applications of DEA in sustainability and ESG (environmental, social, and governance) decision support;
  • Real-time decision analytics using efficiency and productivity measures;
  • Comparative studies of decision support tools in operational research;
  • Case studies on the implementation of DEA in organizational and policy decision-making.

Dr. Thyago Celso Cavalcante Nepomuceno
Guest Editor

Manuscript Submission Information

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Keywords

  • Data Envelopment Analysis
  • efficiency analysis
  • decision support

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Published Papers (2 papers)

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21 pages, 614 KB  
Article
Environmental and Safety Performance of European Railways: An Integrated Efficiency Assessment
by Arsen Benga, María Jesús Delgado Rodríguez, Sonia de Lucas Santos and Ghina El Mir
Algorithms 2026, 19(1), 10; https://doi.org/10.3390/a19010010 - 22 Dec 2025
Cited by 1 | Viewed by 1352
Abstract
Railways play a pivotal role in advancing environmentally conscious and safe transportation systems, positioning them as a vital component of Europe’s future mobility strategy. This study tackles the complex dimensions of sustainability in railway transport by combining environmental impacts and safety considerations within [...] Read more.
Railways play a pivotal role in advancing environmentally conscious and safe transportation systems, positioning them as a vital component of Europe’s future mobility strategy. This study tackles the complex dimensions of sustainability in railway transport by combining environmental impacts and safety considerations within a single, integrated analytical framework. We extend the variable intermediate slack-based measure (VSBM) model to incorporate undesirable outputs—specifically accidents and emissions—allowing for a joint evaluation of safety and environmental performance. The revised model is applied to assess the operational efficiency of 14 European railway operators between 2010 and 2018. Compared to conventional efficiency models, our enhanced VSBM approach provides improved discriminatory power and reveals significant changes in relative efficiency rankings. By integrating safety and environmental dimensions, this study contributes a new perspective on sustainable railway performance measurement. Full article
(This article belongs to the Special Issue Data Envelopment Analysis for Decision Support)
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40 pages, 493 KB  
Systematic Review
The Evolution of Data Envelopment Analysis Models for Circular Economy Performance Assessment
by Andrey V. Lychev and Svetlana V. Ratner
Algorithms 2026, 19(7), 585; https://doi.org/10.3390/a19070585 - 16 Jul 2026
Cited by 1 | Viewed by 725
Abstract
Data Envelopment Analysis (DEA) has emerged as a major non-parametric technique for measuring efficiency in sustainability and environmental economics because it can handle multiple inputs and outputs without making explicit functional assumptions. DEA allows the simultaneous consideration of economic performance, resource utilization, environmental [...] Read more.
Data Envelopment Analysis (DEA) has emerged as a major non-parametric technique for measuring efficiency in sustainability and environmental economics because it can handle multiple inputs and outputs without making explicit functional assumptions. DEA allows the simultaneous consideration of economic performance, resource utilization, environmental impacts, and recycling results in the evaluation of the circular economy (CE). This review investigates the evolution of DEA models in the last years and the variables used to measure the CE performance. We analyze 209 peer-reviewed articles to systematically explore the evolution of DEA applications from conventional single-stage efficiency models to advanced network-based structures that better reflect the intricacy of circular systems. The review discusses the most advanced DEA approaches to date in the literature on CE assessment and uncovers specific factors that affect the choice of models in empirical studies. Finally, it points to promising directions of future research by showing interest in the development of comprehensive DEA models adapted to the specificity of the CE systems. Full article
(This article belongs to the Special Issue Data Envelopment Analysis for Decision Support)
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