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Computers
Special Issues
Operations Research: Trends and Applications
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Operations Research: Trends and Applications

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 8 May 2026 | Viewed by 1360

Special Issue Editors

Dr. Óscar Oliveira

E-Mail Website
Guest Editor
CIICESI, School of Management and Technology, Porto Polytechnic, 4610-156 Felgueiras, Portugal
Interests: optimization; ETL/data integration; data quality
Special Issues, Collections and Topics in MDPI journals
Dr. Eliana Costa e Silva

E-Mail Website
Guest Editor
ESTG—School of Management and Technology, P.PORTO—Polytechnic of Porto, CIICESI—Center for Research and Innovation in Business Sciences and Information Systems, Rua do Curral, Casa do Curral, Margaride, 4610-156 Felgueiras, Portugal
Interests: robotic; optimization; multivariate data analysis and industrial mathematics applications
Special Issues, Collections and Topics in MDPI journals
Dr. Dorabela Gamboa

E-Mail Website
Guest Editor
CIICESI, School of Management and Technology, Porto Polytechnic, 4610-156 Felgueiras, Portugal
Interests: operations research; industrial engineering

Special Issue Information

Dear Colleagues,

Operations research (OR) is at the forefront of innovation, providing critical tools and methodologies to enhance decision making, efficiency, and resource allocation across industries. As organizations face increasingly complex challenges, new trends in OR are emerging, driven by advances in technology and the increasing demand for sustainable and data-driven solutions.

This Special Issue welcomes scientific contributions that propose new, innovative, and original approaches in optimization and OR, with a focus on practical applications and theoretical advancements. We aim to create a platform for academics and practitioners to share their latest findings and experiences.

This Special Issue particularly seeks articles that cover topics including, but not limited to, the following:

  • Artificial intelligence and machine learning in OR;
  • Quantum computing for complex optimization problems;
  • Real-time and dynamic optimization in industry and services;
  • Digital twins and simulation-based optimization;
  • Sustainable and green optimization approaches;
  • Privacy-preserving and federated optimization;
  • Healthcare, logistics, manufacturing, and financial applications;
  • The integration of OR with business intelligence and analytics;
  • Emerging methods: metaheuristics, stochastic and robust optimization.

We look forward to receiving your contributions, highlighting the latest trends, innovative methodologies, and effective applications in the evolving field of optimization and operational research.

Dr. Óscar Oliveira
Dr. Eliana Costa e Silva
Dr. Dorabela Gamboa
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. Computers 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

  • optimization algorithms
  • data analytics
  • predictive modeling
  • heuristic methods
  • multi-objective optimization
  • supply chain optimization
  • smart systems
  • Industry 4.0
  • IoT (Internet of Things) in OR
  • decision support systems
  • computational intelligence
  • big data analytics
  • deep learning in OR
  • resilience and robustness in OR
  • network optimization
  • cloud computing in OR

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

Published Papers (3 papers)

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Research

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46 pages, 2758 KB  
Article
Swallow Search Algorithm (SWSO): A Swarm Intelligence Optimization Approach Inspired by Swallow Bird Behavior
by Farah Sami Khoshaba, Shahab Wahhab Kareem and Roojwan Sc Hawezi
Computers 2025, 14(9), 345; https://doi.org/10.3390/computers14090345 - 22 Aug 2025
Viewed by 284
Abstract
Swarm Intelligence (SI) algorithms were applied widely in solving complex optimization problems because they are simple, flexible, and efficient. The current paper proposes a new SI algorithm, which is based on the bird-like actions of swallows, which have highly synchronized behaviors of foraging [...] Read more.
Swarm Intelligence (SI) algorithms were applied widely in solving complex optimization problems because they are simple, flexible, and efficient. The current paper proposes a new SI algorithm, which is based on the bird-like actions of swallows, which have highly synchronized behaviors of foraging and migration. The optimization algorithm (SWSO) makes use of these behaviors to boost the ability of exploration and exploitation in the optimization process. Unlike other birds, swallows are known to be so precise when performing fast directional alterations and making intricate aerial acrobatics during foraging. Moreover, the flight patterns of swallows are very efficient; they have extensive capabilities to transition between flapping and gliding with ease to save energy over long distances during migration. This allows instantaneous changes of wing shape variations to optimize performance in any number of flying conditions. The model used by the SWSO algorithm combines these biologically inspired flight dynamics into a new computational model that is aimed at enhancing search performance in rugged terrain. The design of the algorithm simulates the swallow’s social behavior and energy-saving behavior, converting it into exploration, exploitation, control mechanisms, and convergence control. In order to verify its effectiveness, (SWSO) is applied to many benchmark problems, such as unimodal, multimodal, fixed-dimension functions, and a benchmark CEC2019, which consists of some of the most widely used benchmark functions. Comparative tests are conducted against more than 30 metaheuristic algorithms that are regarded as state-of-the-art, developed so far, including PSO, MFO, WOA, GWO, and GA, among others. The measures of performance included best fitness, rate of convergence, robustness, and statistical significance. Moreover, the use of (SWSO) in solving real-life engineering design problems is used to prove (SWSO)’s practicality and generality. The results confirm that the proposed algorithm offers a competitive and reliable solution methodology, making it a valuable addition to the field of swarm-based optimization. Full article
(This article belongs to the Special Issue Operations Research: Trends and Applications)
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16 pages, 3704 KB  
Article
Optimization of Scene and Material Parameters for the Generation of Synthetic Training Datasets for Machine Learning-Based Object Segmentation
by Malte Nagel, Kolja Hedrich, Nils Melchert, Lennart Hinz and Eduard Reithmeier
Computers 2025, 14(8), 341; https://doi.org/10.3390/computers14080341 - 21 Aug 2025
Viewed by 313
Abstract
Synthetic training data is often essential for neural-network-based segmentation when real datasets are difficult or impossible to obtain. Conventional synthetic data generation relies on manually selecting scene and material parameters. This can lead to poor performance because the optimal parameters are often non-intuitive [...] Read more.
Synthetic training data is often essential for neural-network-based segmentation when real datasets are difficult or impossible to obtain. Conventional synthetic data generation relies on manually selecting scene and material parameters. This can lead to poor performance because the optimal parameters are often non-intuitive and depend heavily on the specific use case and on the objects to be segmented. This study proposes a novel, automated optimization pipeline to improve the quality of synthetic datasets for specific object segmentation tasks. Synthetic datasets are generated by varying material and scene parameters with the BlenderProc framework. These parameters are optimized with the Optuna framework to maximize the average precision achieved by models trained on this data and validated using a small real dataset. After initial single-parameter studies and subsequent multidimensional optimization, optimal scene and material parameters are identified for each object. The results demonstrate the potential of this optimization pipeline to produce synthetic training datasets that enhance neural network performance for specific segmentation tasks, offering insights into the critical role of scene design and material selection in synthetic data generation. Full article
(This article belongs to the Special Issue Operations Research: Trends and Applications)
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Review

Jump to: Research

22 pages, 1566 KB  
Review
Multi-Objective Evolutionary Algorithms in Waste Disposal Systems: A Comprehensive Review of Applications, Case Studies, and Future Directions
by Saad Talal Alharbi
Computers 2025, 14(8), 316; https://doi.org/10.3390/computers14080316 - 4 Aug 2025
Viewed by 518
Abstract
Multi-objective evolutionary algorithms (MOEAs) have emerged as powerful optimization tools for addressing the complex, often conflicting goals present in modern waste disposal systems. This review explores recent advances and practical applications of MOEAs in key areas, including waste collection routing, waste-to-energy (WTE) systems, [...] Read more.
Multi-objective evolutionary algorithms (MOEAs) have emerged as powerful optimization tools for addressing the complex, often conflicting goals present in modern waste disposal systems. This review explores recent advances and practical applications of MOEAs in key areas, including waste collection routing, waste-to-energy (WTE) systems, and facility location and allocation. Real-world case studies from cities like Braga, Lisbon, Uppsala, and Cyprus demonstrate how MOEAs can enhance operational efficiency, boost energy recovery, and reduce environmental impacts. While these algorithms offer significant advantages, challenges remain in computational complexity, adapting to dynamic environments, and integrating with emerging technologies. Future research directions highlight the potential of combining MOEAs with machine learning and real-time data to create more flexible and responsive waste management strategies. By leveraging these advancements, MOEAs can play a pivotal role in developing sustainable, efficient, and adaptive waste disposal systems capable of meeting the growing demands of urbanization and stricter environmental regulations. Full article
(This article belongs to the Special Issue Operations Research: Trends and Applications)
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