Operations Research: Trends and Applications

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

Deadline for manuscript submissions: 8 May 2026 | Viewed by 544

Special Issue Editors


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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

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

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

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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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Published Papers (1 paper)

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Review

22 pages, 1566 KiB  
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 357
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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