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Mathematics
Special Issues
Advances in Mathematical Optimization in Operational Research
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Advances in Mathematical Optimization in Operational Research

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D: Statistics and Operational Research".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 496

Special Issue Editors

Prof. Dr. Ke Fu

E-Mail Website
Guest Editor
School of Business, Sun Yat-sen University, Guangzhou 510275, China
Interests: operations; supply chain management; inventory management; sustainable operations
Special Issues, Collections and Topics in MDPI journals
Dr. Jiayan Xu

E-Mail Website
Guest Editor
School of Business, Sun Yat-sen University, Guangzhou 510275, China
Interests: supply chain; operations

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue on “Advances in Mathematical Optimization in Operational Research”, to be published in Mathematics. This Special Issue aims to provide a platform for researchers, practitioners, and academics to present their latest findings and advancements in the field of mathematical optimization and its applications in operational research.

Mathematical optimization has long been a cornerstone of operational research, enabling the efficient allocation of resources, the optimization of complex systems, and the development of robust decision-making processes. With the increasing complexity and interconnectedness of modern systems, the need for innovative optimization techniques and their effective applications have never been more critical.

We invite original research articles and reviews on topics related (but not limited) to the following:

  • Optimization in logistics and supply chain management;
  • Energy systems optimization;
  • Healthcare operations optimization;
  • Transportation and traffic optimization;
  • Manufacturing and production optimization;
  • Real-world applications of operational research;
  • Integration of optimization with other fields such as data science, engineering, and economics.

Submission Guidelines:

  • All submissions must be original and not under consideration for publication elsewhere (except conference proceedings papers).
  • Manuscripts should be well formatted and written in good English.
  • A detailed guide for authors is available on the Mathematics website.
  • Submission deadline: 31 December 2025

We look forward to receiving your contributions and to showcasing the latest advancements in mathematical optimization in operational research. Together, we can drive forward the development of this critical field and contribute to solving real-world challenges through innovative optimization techniques.

Prof. Dr. Ke Fu
Dr. Jiayan Xu
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. 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
  • operational research
  • operations and supply chain management
  • real-world applications

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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

31 pages, 7290 KiB  
Article
Freight Rate Decisions in Shipping Logistics Service Supply Chains Considering Blockchain Adoption Risk Preferences
by Yujing Chen, Jiao Mo and Bin Yang
Mathematics 2025, 13(15), 2339; https://doi.org/10.3390/math13152339 - 22 Jul 2025
Viewed by 83
Abstract
This paper explores the strategic implications of technological adoption within shipping logistics service supply chains, with a particular focus on blockchain technology (BCT). When integrating new technologies, supply chain stakeholders evaluate associated risks alongside complexity, profitability, and operational challenges, which influence their strategic [...] Read more.
This paper explores the strategic implications of technological adoption within shipping logistics service supply chains, with a particular focus on blockchain technology (BCT). When integrating new technologies, supply chain stakeholders evaluate associated risks alongside complexity, profitability, and operational challenges, which influence their strategic behaviors. Anchored in the concept of technology trust, this study examines how different risk preferences affect BCT adoption decisions and freight rate strategies. A game-theoretic model is constructed using a mean-variance utility framework to analyze interactions between shipping companies and freight forwarders under three adoption scenarios: no adoption (NN), partial adoption (BN), and full adoption (BB). The results indicate that risk-seeking agents are more likely to adopt BCT early but face greater freight rate volatility in the initial stages. As the technology matures, strategic variability declines and the influence of adaptability on pricing becomes less pronounced. In contrast, risk-neutral and risk-averse participants tend to adopt more conservatively, resulting in slower but more stable pricing dynamics. These findings offer new insights into how technology trust and risk attitudes shape strategic decisions in digitally transforming supply chains. The study also provides practical implications for differentiated pricing strategies, BCT adoption incentives, and collaborative policy design among logistics stakeholders. Full article
(This article belongs to the Special Issue Advances in Mathematical Optimization in Operational Research)
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20 pages, 732 KiB  
Article
On Minimizing Influences Under Multi-Attribute Models
by Bo-Yao Wang
Mathematics 2025, 13(13), 2064; https://doi.org/10.3390/math13132064 - 21 Jun 2025
Viewed by 178
Abstract
In classical transferable-utility models, components typically participate in an all-or-nothing manner and are evaluated under a single criterion. This study generalizes such models by allowing each component to engage through multiple acting measures and by incorporating multiple evaluating attributes simultaneously. We introduce two [...] Read more.
In classical transferable-utility models, components typically participate in an all-or-nothing manner and are evaluated under a single criterion. This study generalizes such models by allowing each component to engage through multiple acting measures and by incorporating multiple evaluating attributes simultaneously. We introduce two influence-based assessments, the stable min value and the minimal self-stable value, to evaluate fair assessments of minimal impact across multi-attribute multi-choice environments. These values are rigorously defined via axiomatic characterizations grounded in minimal influence behavior, where coalitions select activity levels that jointly minimize systemic effects. A key theoretical contribution is the identification of a unique, 0-normalized, and efficient multi-attribute potential function corresponding to the minimal self-stable value. The proposed framework enables structured and interpretable evaluation of influence in complex cooperative systems with heterogeneous participation and conflicting objectives. Full article
(This article belongs to the Special Issue Advances in Mathematical Optimization in Operational Research)
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