Juan Aparicio

3.6k total citations
147 papers, 2.6k citations indexed

About

Juan Aparicio is a scholar working on Management Science and Operations Research, Economics and Econometrics and Control and Systems Engineering. According to data from OpenAlex, Juan Aparicio has authored 147 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 120 papers in Management Science and Operations Research, 64 papers in Economics and Econometrics and 22 papers in Control and Systems Engineering. Recurrent topics in Juan Aparicio's work include Efficiency Analysis Using DEA (109 papers), Optimization and Mathematical Programming (21 papers) and Economic and Environmental Valuation (18 papers). Juan Aparicio is often cited by papers focused on Efficiency Analysis Using DEA (109 papers), Optimization and Mathematical Programming (21 papers) and Economic and Environmental Valuation (18 papers). Juan Aparicio collaborates with scholars based in Spain, Poland and United States. Juan Aparicio's co-authors include Jesús T. Pastor, José L. Zofío, Inmaculada Sirvent, José L. Ruiz, Miriam Esteve, Fernando Borrás, Magdalena Kapelko, José Manuel Cordero Ferrera, Joe Zhu and Fernando Vidal Giménez and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and European Journal of Operational Research.

In The Last Decade

Juan Aparicio

135 papers receiving 2.5k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Juan Aparicio Spain 30 1.8k 1.1k 377 354 192 147 2.6k
Victor V. Podinovski United Kingdom 29 2.6k 1.5× 1.6k 1.5× 318 0.8× 412 1.2× 82 0.4× 69 3.2k
Peter Bogetoft Denmark 27 1.8k 1.0× 1.1k 1.1× 149 0.4× 160 0.5× 334 1.7× 116 2.7k
José L. Ruiz Spain 24 1.7k 1.0× 843 0.8× 183 0.5× 491 1.4× 43 0.2× 41 2.0k
Niels Christian Petersen Denmark 13 3.1k 1.8× 1.8k 1.7× 373 1.0× 622 1.8× 84 0.4× 33 3.9k
Mehdi Toloo Czechia 33 2.1k 1.2× 688 0.6× 176 0.5× 713 2.0× 62 0.3× 105 2.5k
Junfei Chu China 26 1.5k 0.8× 1.0k 1.0× 632 1.7× 169 0.5× 64 0.3× 61 2.1k
Xiaoyang Zhou China 28 923 0.5× 503 0.5× 153 0.4× 260 0.7× 152 0.8× 104 2.4k
Gholam R. Amin Iran 25 1.6k 0.9× 604 0.6× 144 0.4× 460 1.3× 44 0.2× 80 2.1k
Inmaculada Sirvent Spain 21 1.7k 0.9× 780 0.7× 177 0.5× 473 1.3× 40 0.2× 34 1.9k

Countries citing papers authored by Juan Aparicio

Since Specialization
Citations

This map shows the geographic impact of Juan Aparicio's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Juan Aparicio with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Juan Aparicio more than expected).

Fields of papers citing papers by Juan Aparicio

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Juan Aparicio. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Juan Aparicio. The network helps show where Juan Aparicio may publish in the future.

Co-authorship network of co-authors of Juan Aparicio

This figure shows the co-authorship network connecting the top 25 collaborators of Juan Aparicio. A scholar is included among the top collaborators of Juan Aparicio based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Juan Aparicio. Juan Aparicio is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Aparicio, Juan, Magdalena Kapelko, Juan F. Monge, & José L. Zofío. (2025). Enhancing the Benefit of the Doubt model through ‘Ensemble-DEA’: achieving the Sustainable Development Goals. Expert Systems with Applications. 296. 129010–129010.
2.
Charles, Vincent, et al.. (2025). Enhanced efficiency assessment in manufacturing: Leveraging machine learning for improved performance analysis. Omega. 134. 103300–103300. 2 indexed citations
3.
Aparicio, Juan & Daniel Santín González. (2025). The standard total factor productivity index and its decomposition. European Journal of Operational Research. 329(3). 981–1003.
4.
Aparicio, Juan, et al.. (2024). Measuring environmental inefficiency through machine learning: An approach based on efficiency analysis trees and by-production technology. European Journal of Operational Research. 321(2). 529–542. 2 indexed citations
5.
Aparicio, Juan, et al.. (2024). The game of agents in transportation problems. Annals of Operations Research. 356(1). 165–187.
6.
Pastor, Jesús T., José L. Zofío, Juan Aparicio, & Fernando Borrás. (2023). The standard reverse approach for decomposing economic inefficiency. Journal of the Operational Research Society. 75(4). 647–659. 2 indexed citations
7.
Aparicio, Juan, José L. Zofío, & Jesús T. Pastor. (2023). Decomposing Economic Efficiency into Technical and Allocative Components: An Essential Property. Journal of Optimization Theory and Applications. 197(1). 98–129. 12 indexed citations
8.
Aparicio, Juan, et al.. (2023). Ranking the Importance of Variables in a Nonparametric Frontier Analysis Using Unsupervised Machine Learning Techniques. Mathematics. 11(11). 2590–2590. 1 indexed citations
9.
Esteve, Miriam, et al.. (2022). Random Forests and the measurement of super-efficiency in the context of Free Disposal Hull. European Journal of Operational Research. 304(2). 729–744. 44 indexed citations
10.
Alcaraz, Javier, et al.. (2022). Weight profiles in cross-efficiency evaluation based on hypervolume maximization. Socio-Economic Planning Sciences. 82. 101270–101270. 3 indexed citations
11.
Aparicio, Juan, et al.. (2022). Performance Evaluation of Decision-Making Units Through Boosting Methods in the Context of Free Disposal Hull: Some Exact and Heuristic Algorithms. International Journal of Information Technology & Decision Making. 24(8). 2435–2464. 8 indexed citations
12.
Aparicio, Juan, et al.. (2021). Introducing a functional framework for integrating the empirical evidence about higher education institutions’ functions and capabilities: A literature review. Journal of Entrepreneurship Management and Innovation. 17(1). 231–267. 7 indexed citations
13.
Zhu, Qingyuan, Juan Aparicio, Feng Li, Jie Wu, & Gang Kou. (2021). Determining closest targets on the extended facet production possibility set in data envelopment analysis: Modeling and computational aspects. European Journal of Operational Research. 296(3). 927–939. 41 indexed citations
14.
Charles, Vincent, Juan Aparicio, & Joe Zhu. (2021). Data science for better productivity. Journal of the Operational Research Society. 72(5). 971–974. 2 indexed citations
15.
Charles, Vincent, Juan Aparicio, & Joe Zhu. (2019). The curse of dimensionality of decision-making units: A simple approach to increase the discriminatory power of data envelopment analysis. European Journal of Operational Research. 279(3). 929–940. 108 indexed citations
16.
Aparicio, Juan, Juan F. Monge, & Jesús T. Pastor. (2012). New Centralized Resource Allocation DEA Models under Constant Returns to Scale 1. 28(2). 110–130. 1 indexed citations
17.
Valero, S., et al.. (2010). Analysis of different testing parameters in Self-Organizing Maps for short-term load demand forecasting in Spain. 1–6. 3 indexed citations
18.
Pastor, Jesús T. & Juan Aparicio. (2010). Distance Functions and Efficiency Measurement. Indian Economic Review. 45(2). 193–231. 3 indexed citations
19.
Valero, S., et al.. (2010). Comparative analysis of self organizing maps vs. multilayer perceptron neural networks for short-term load forecasting. 1–5. 12 indexed citations
20.
Aparicio, Juan. (2007). Una introducción al análisis envolvente de datos. 23(1). 12–17. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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