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Mathematics
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Computational Optimization and Operations Research: Theory and Applications
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Computational Optimization and Operations Research: Theory and Applications

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: 30 September 2026 | Viewed by 4776

Special Issue Editors

Prof. Dr. Hsing Luh

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Guest Editor
Department of Mathematical Sciences, National Chengchi University, Taipei, Taiwan
Interests: operations research; mathematical modeling; optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Chia-Hung Wang

E-Mail Website
Guest Editor
College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
Interests: operations research; operation management; data science; artificial intelligence; intelligent computing; time series forecasting; queue; optimization

Special Issue Information

Dear Colleagues,

Computational optimization and operations research are dynamic fields that feature exciting advancements which are shaping the future of related fields, with applications across industries. Some recent developments and advanced topics include machine learning and AI integration, quantum computing, graph theory and network analysis, numerical methods for big data, healthcare operations and sustainability, and energy optimization. Many mathematical prediction models using sophisticated optimization and computational techniques have also been developed in other related fields including public health and government administration, especially for the modeling of strategic decision making and supply chain analytics. I invite you to submit your latest research to this Special Issue. Papers exploiting new developments in optimization and applied mathematics, particularly in computational optimization or operations research, are welcome.

Prof. Dr. Hsing Luh
Dr. Chia-Hung Wang
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 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

  • optimization
  • machine learning
  • neural networks
  • simulation
  • operations research
  • stochastic modeling

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Published Papers (3 papers)

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Research

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15 pages, 759 KB  
Article
Efficiency and Convergence Insights in Large-Scale Optimization Using the Improved Inexact–Newton–Smart Algorithm and Interior-Point Framework
by Neda Bagheri Renani, Maryam Jaefarzadeh and Daniel Ševčovič
Mathematics 2025, 13(22), 3657; https://doi.org/10.3390/math13223657 - 14 Nov 2025
Viewed by 453
Abstract
We present a head-to-head evaluation of the Improved Inexact–Newton–Smart (INS) algorithm against a primal–dual interior-point framework for large-scale nonlinear optimization. On extensive synthetic benchmarks, the interior-point method converges with roughly one-third fewer iterations and about one-half the computation time relative to INS, while [...] Read more.
We present a head-to-head evaluation of the Improved Inexact–Newton–Smart (INS) algorithm against a primal–dual interior-point framework for large-scale nonlinear optimization. On extensive synthetic benchmarks, the interior-point method converges with roughly one-third fewer iterations and about one-half the computation time relative to INS, while attaining marginally higher accuracy and meeting all primary stopping conditions. By contrast, INS succeeds in fewer cases under default settings but benefits markedly from moderate regularization and step-length control; in tuned regimes, its iteration count and runtime decrease substantially, narrowing yet not closing the gap. A sensitivity study indicates that interior-point performance remains stable across parameter changes, whereas INS is more affected by step length and regularization choice. Collectively, the evidence positions the interior-point method as a reliable baseline and INS as a configurable alternative when problem structure favors adaptive regularization. Full article
(This article belongs to the Special Issue Computational Optimization and Operations Research: Theory and Applications)
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12 pages, 225 KB  
Article
On Solving the Knapsack Problem with Conflicts
by Roberto Montemanni and Derek H. Smith
Mathematics 2025, 13(16), 2674; https://doi.org/10.3390/math13162674 - 20 Aug 2025
Viewed by 976
Abstract
A variant of the well-known Knapsack Problem is studied in this paper. In the classic problem, a set of items is given, with each item characterized by a weight and a profit. A knapsack of a given capacity is provided, and the problem [...] Read more.
A variant of the well-known Knapsack Problem is studied in this paper. In the classic problem, a set of items is given, with each item characterized by a weight and a profit. A knapsack of a given capacity is provided, and the problem consists of selecting a subset of items such that the total weight does not exceed the capacity of the knapsack, while the total profit is maximized. In the variation considered in the present work, pairs of items are conflicting, and cannot be selected at the same time. The resulting problem, which can be used to model several real applications, is considerably harder to approach than the classic one. In this paper, we consider a mixed-integer linear program representing the problem and we solve it with a state-of-the-art black-box software. A vast experimental procedure on the instances available from the literature, and adopted in the last decade by the community, indicates that the approach we propose achieves results comparable with, and in many cases better than, those of state-of-the-art methods, notwithstanding that the latter are typically based on more complex and problem-specific ideas and algorithms than the idea we propose. Full article
(This article belongs to the Special Issue Computational Optimization and Operations Research: Theory and Applications)
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Review

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28 pages, 1693 KB  
Review
Rethinking Metaheuristics: Unveiling the Myth of “Novelty” in Metaheuristic Algorithms
by Chia-Hung Wang, Kun Hu, Xiaojing Wu and Yufeng Ou
Mathematics 2025, 13(13), 2158; https://doi.org/10.3390/math13132158 - 1 Jul 2025
Cited by 5 | Viewed by 2890
Abstract
In recent decades, the rapid development of metaheuristic algorithms has outpaced theoretical understanding, with experimental evaluations often overshadowing rigorous analysis. While nature-inspired optimization methods show promise for various applications, their effectiveness is often limited by metaphor-driven design, structural biases, and a lack of [...] Read more.
In recent decades, the rapid development of metaheuristic algorithms has outpaced theoretical understanding, with experimental evaluations often overshadowing rigorous analysis. While nature-inspired optimization methods show promise for various applications, their effectiveness is often limited by metaphor-driven design, structural biases, and a lack of sufficient theoretical foundation. This paper systematically examines the challenges in developing robust, generalizable optimization techniques, advocating for a paradigm shift toward modular, transparent frameworks. A comprehensive review of the existing limitations in metaheuristic algorithms is presented, along with actionable strategies to mitigate biases and enhance algorithmic performance. Through emphasis on theoretical rigor, reproducible experimental validation, and open methodological frameworks, this work bridges critical gaps in algorithm design. The findings support adopting scientifically grounded optimization approaches to advance operational applications. Full article
(This article belongs to the Special Issue Computational Optimization and Operations Research: Theory and Applications)
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