Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
1.9
Journals
Computation
Special Issues
Modern Applications for Computational Methods in Applied Economics and Business...
share announcement

Modern Applications for Computational Methods in Applied Economics and Business Engineering

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Social Science".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 2672

Special Issue Editors

Dr. Antonio Martínez Raya

E-Mail Website
Guest Editor
Department of Organizational Engineering, Business Administration and Statistics, School of Aeronautical and Space Engineering, Technical University of Madrid—Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain
Interests: EU single market; airline industry; transport and logistics management; entrepreneurship and innovation; transportation and mobility; social mobility and education; sustainable economy
Dr. Manuel Uche-Soria

E-Mail Website
Guest Editor
Department of Organizational Engineering, Business Administration and Statistics, School of Computer Systems Engineering, Technical University of Madrid—Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain
Interests: data analysis applied to business; use of sensors and data for assisted living; environmental technology

Special Issue Information

Dear Colleagues,

The present era's profound social and technological changes have enormously impacted economic interaction among the various actors involved, whether individuals or businesses. Today, computation in science and engineering is concentrating significant efforts on improving understanding of issues involving big data and extensive computations, as well as complex modeling, simulation, optimization, and visualization. Specifically, computational economics is currently an emerging discipline that is called to be intensively used in discovering knowledge gaps through the tools and techniques provided by computer simulation, particularly those based on artificial intelligence, machine learning, and neural networks. This interdisciplinary research discipline combines theoretical and empirical methods to solve complex economic problems. Thus, it is not only a tool for responding to socio-economic challenges but also a way of facing global challenges concerning political systems, socioeconomic structures, and wealth distribution. Moreover, such modern techniques for computational social science can provide cutting-edge approaches in this interdisciplinary field, which has been attracting significant interest among social scientists, computer researchers, and statistical scholars alike. Therefore, this Special Issue features recent studies on computational approaches to data analysis and modeling complex problems, improving the understanding of computational social science and its engineering applications.

This Special Issue is also intricately linked to the EASN2025 conference (https://easnconference.eu). For submissions of works presented at the conference, please be aware that manuscripts’ acceptance is contingent upon submitting a certificate of attendance. Please note that the EASN2025 conference does not allow the option of virtually presenting communications to the congress.

Nevertheless, unprecedented works outside the conference sphere will always be welcomed if original manuscripts are valuable for the scientific community within the scope of this Special Issue.

The topics of this Special Issue include, but are not limited to:

  • Innovative computation in the sustainable aerospace industry;
  • Modern computation for engineering technology and applied sciences;
  • Computational intelligence for transportation and logistics towards sustainability;
  • Computation-aided studies in mobility for the intensive use of renewable fuels;
  • Computational methods and models for social studies;
  • Computer simulation applications in economics;
  • Artificial intelligence in social science;
  • Financial market prediction;
  • Social Computing.

Dr. Antonio Martínez Raya
Dr. Manuel Uche-Soria
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational economics
  • artificial intelligence
  • transportation demand modeling
  • financial market prediction
  • social computing

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

33 pages, 3547 KiB  
Article
Mapping the Intellectual Structure of Computational Risk Analytics in Banking and Finance: A Bibliometric and Thematic Evolution Study
by Sotirios J. Trigkas, Kanellos Toudas and Ioannis Chasiotis
Computation 2025, 13(7), 172; https://doi.org/10.3390/computation13070172 - 17 Jul 2025
Viewed by 217
Abstract
Modern financial practices introduce complex risks, which in turn force financial institutions to rely increasingly on computational risk analytics (CRA). The purpose of our research is to attempt to systematically explore the evolution and intellectual structure of CRA in banking using a detailed [...] Read more.
Modern financial practices introduce complex risks, which in turn force financial institutions to rely increasingly on computational risk analytics (CRA). The purpose of our research is to attempt to systematically explore the evolution and intellectual structure of CRA in banking using a detailed bibliometric analysis of the literature sourced from Web of Science from 2000 to 2025. A comprehensive search in the Web of Science (WoS) Core Collection yielded 1083 peer-reviewed publications, which we analyzed using analytical tools like VOSviewer 1.6.20 and Bibliometrix (Biblioshiny 5.0) so as to examine the dataset and uncover bibliometric characteristics like citation patterns, keyword occurrences, and thematic clustering. Our initial analysis results uncover the presence of key research clusters focusing on bankruptcy prediction, AI integration in financial services, and advanced deep learning applications. Furthermore, our findings note a transition of CRA from an emerging to an expanding domain, especially after 2019, with terms like machine learning (ML), artificial intelligence (AI), and deep learning (DL) being identified as prominent keywords and a recent shift towards blockchain, explainability, and financial stability being present. We believe that this study tries to address the need for an updated mapping of CRA, providing valuable insights for future academic inquiry and practical financial risk management applications. Full article
(This article belongs to the Special Issue Modern Applications for Computational Methods in Applied Economics and Business Engineering)
►▼ Show Figures

Figure 1

38 pages, 2004 KiB  
Article
Effective Heuristics for Solving the Multi-Item Uncapacitated Lot-Sizing Problem Under Near-Minimal Storage Capacities
by Warut Boonphakdee, Duangrat Hirunyasiri and Peerayuth Charnsethikul
Computation 2025, 13(6), 148; https://doi.org/10.3390/computation13060148 - 13 Jun 2025
Viewed by 743
Abstract
In inventory management, storage capacity constraints complicate multi-item lot-sizing decisions. As the number of items increases, deciding how much of each item to order without exceeding capacity becomes more difficult. Dynamic programming works efficiently for a single item, but when capacity constraints are [...] Read more.
In inventory management, storage capacity constraints complicate multi-item lot-sizing decisions. As the number of items increases, deciding how much of each item to order without exceeding capacity becomes more difficult. Dynamic programming works efficiently for a single item, but when capacity constraints are nearly minimal across multiple items, novel heuristics are required. However, previous heuristics have mainly focused on inventory bound constraints. Therefore, this paper introduces push and pull heuristics to solve the multi-item uncapacitated lot-sizing problem under near-minimal capacities. First, a dynamic programming approach based on a network flow model was used to generate the initial replenishment plan for the single-item lot-sizing problem. Next, under storage capacity constraints, the push operation moved the selected replenishment quantities from the current period to subsequent periods to meet all demand requirements. Finally, the pull operation shifted the selected replenishment quantities from the current period into earlier periods, ensuring that all demand requirements were satisfied. The results of the random experiment showed that the proposed heuristic generated solutions whose performance compared well with the optimal solution. This heuristic effectively solves all randomly generated instances representing worst-case conditions, ensuring robust operation under near-minimal storage. For large-scale problems under near-minimal storage capacity constraints, the proposed heuristic achieved only small optimality gaps while requiring less running time. However, small- and medium-scale problems can be solved optimally by a Mixed-Integer Programming (MIP) solver with minimal running time. Full article
(This article belongs to the Special Issue Modern Applications for Computational Methods in Applied Economics and Business Engineering)
►▼ Show Figures

Graphical abstract

29 pages, 5696 KiB  
Article
Sliding Window-Based Randomized K-Fold Dynamic ANN for Next-Day Stock Trend Forecasting
by Jaykumar Ishvarbhai Prajapati and Raja Das
Computation 2025, 13(6), 141; https://doi.org/10.3390/computation13060141 - 8 Jun 2025
Viewed by 1289
Abstract
The integration of machine learning and stock forecasting is attracting increased curiosity owing to its growing significance. This paper presents two main areas of study: predicting pattern trends for the next day and forecasting opening and closing prices using a new method that [...] Read more.
The integration of machine learning and stock forecasting is attracting increased curiosity owing to its growing significance. This paper presents two main areas of study: predicting pattern trends for the next day and forecasting opening and closing prices using a new method that adds a dynamic hidden layer to artificial neural networks and employs a unique random k-fold cross-validation to enhance prediction accuracy and improve training. To validate the model, we are considering APPLE, GOOGLE, and AMAZON stock data. As a result, low root mean squared error (1.7208) and mean absolute error (0.9892) in both training and validation phases demonstrate the robust predictive performance of the dynamic ANN model. Furthermore, high R-values indicated a strong correlation between the experimental data and proposed model estimates. Full article
(This article belongs to the Special Issue Modern Applications for Computational Methods in Applied Economics and Business Engineering)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop