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Mathematics
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Soft Computing in Computational Intelligence and Machine Learning
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Soft Computing in Computational Intelligence and Machine Learning

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1367

Special Issue Editor

Prof. Dr. Miklós Gubán

E-Mail Website
Guest Editor
Department of Business Information Technology, Budapest University of Economics and Business, 1149 Budapest, Hungary
Interests: artificial intelligence; operations research; mathematical modeling

Special Issue Information

Dear Colleagues,

Soft computing and machine learning have brought about revolutionary changes in everyday life as well as across numerous industries. They have enabled systems not only to operate according to pre-defined rules but also to adapt, learn, and make decisions based on uncertain, noisy, or incomplete data.

In logistics, manufacturing, and services, machine learning facilitates automation and predictive analytics, while soft computing methods assist in managing complex decision-making situations.

The Special Issue titled “Soft Computing in Computational Intelligence and Machine Learning” aims to provide a comprehensive overview of both theoretical and practical research in these fields and to serve as a platform for the presentation of new findings. Particular attention is given to innovative approaches that combine mathematical modelling and optimisation in process design and evaluation tasks.

We invite researchers working in this area of artificial intelligence to contribute original research papers, review papers, and empirical studies that stimulate discussion on the topic.

Prof. Dr. Miklós Gubán
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • mathematical modelling for complex systems
  • application of soft computing methods in logistics
  • AI-based process optimisation
  • soft computing in service systems
  • machine learning for economic process evaluation

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

Published Papers (2 papers)

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Research

24 pages, 4228 KB  
Article
From Layout to Data: AI-Driven Route Matrix Generation for Logistics Optimization
by Ádám Francuz and Tamás Bányai
Mathematics 2026, 14(5), 910; https://doi.org/10.3390/math14050910 - 7 Mar 2026
Viewed by 454
Abstract
This study proposes an end-to-end mathematical framework to automatically transform warehouse layout images into optimization-ready route matrices. The objective is to convert visual spatial information into a discrete, graph-based representation suitable for combinatorial route optimization. The problem is formulated as a mapping from [...] Read more.
This study proposes an end-to-end mathematical framework to automatically transform warehouse layout images into optimization-ready route matrices. The objective is to convert visual spatial information into a discrete, graph-based representation suitable for combinatorial route optimization. The problem is formulated as a mapping from continuous image space to a structured grid representation, integrating image segmentation, graph construction, and Traveling Salesman Problem (TSP)-based routing. Synthetic warehouse layouts were generated to create labeled training data, and a U-Net convolutional neural network was trained to perform multi-class segmentation of warehouse elements. The predicted grid representation was then converted into a graph structure, where feasible cells define vertices and adjacency defines edges. Shortest path distances were computed using Breadth-First Search, and the resulting distance matrix was used to solve a TSP instance. The segmentation model achieved approximately 98% training accuracy and 95–97% validation accuracy. The generated route matrices enabled successful construction of feasible and optimal round-trip routes in all tested scenarios. The proposed framework demonstrates that warehouse layouts can be automatically transformed into discrete mathematical representations suitable for logistics optimization, reducing manual preprocessing and enabling scalable integration into digital logistics systems. Full article
(This article belongs to the Special Issue Soft Computing in Computational Intelligence and Machine Learning)
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25 pages, 2735 KB  
Article
Beyond Traditional Forecasting Methods: Evaluating LSTM Performance on Diverse Time Series
by Zoltán Baráth, Péter Veres and Ágota Bányai
Mathematics 2026, 14(5), 838; https://doi.org/10.3390/math14050838 - 1 Mar 2026
Viewed by 571
Abstract
Time series forecasting performance is strongly influenced by the structural properties of the underlying data, yet learning-based models are often applied without sufficient validation of this dependency. This study evaluates a uniformly configured Long Short-Term Memory (LSTM) model on five real-world weekly time [...] Read more.
Time series forecasting performance is strongly influenced by the structural properties of the underlying data, yet learning-based models are often applied without sufficient validation of this dependency. This study evaluates a uniformly configured Long Short-Term Memory (LSTM) model on five real-world weekly time series with different levels of periodicity, noise, and volatility. Forecasting is performed in a single-step setting using a fixed sliding window of 12 weeks under a consistent training, validation, and testing framework. Model performance is assessed using mean squared error (MSE) and the coefficient of determination R2. The results show that for well-structured series, both the LSTM model and Holt’s exponential smoothing achieve very low MSE values with R2 scores close to one, indicating excellent predictive accuracy. For other items, performance varies across methods, with either the LSTM or Holt model providing the best results depending on the data structure. These findings confirm that high forecasting accuracy can be achieved with both advanced and classical methods, and that data characteristics play a more decisive role than model complexity. Full article
(This article belongs to the Special Issue Soft Computing in Computational Intelligence and Machine Learning)
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