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Mathematics
Special Issues
New Advances of Operations Research and Analysis
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New Advances of Operations Research and Analysis

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".

Deadline for manuscript submissions: 10 April 2025 | Viewed by 1359

Special Issue Editor

Dr. Trung Hieu Tran

E-Mail Website
Guest Editor
School of Water, Energy and Environemnt, Cranfield University, Bedford MK43 0AL, UK
Interests: mathematical programming; meta-heuristic algorithms; agent-based modeling; simulation-based optimisation

Special Issue Information

Dear Colleagues,

It is my pleasure to invite you to submit an article for possible publication in a Special Issue of Mathematics (ISSN 2227-7390), a peer-reviewed, open access journal which provides an advanced forum for studies related to mathematics and is published semi-monthly online by MDPI. The topic of this Special Issue focuses on 'New Advances of Operations Research'.

Operations research (OR) has been developed and widely applied for solving practical optimization problems from manufacturing to service. Nowadays, OR is known as a powerful modelling and optimization tool to deal with global challenge issues such as the impact of climate change on human beings, infrastructure systems, etc. This Special Issue would thus contribute to the field of novel applications in OR. It aims to present original research articles and the most relevant advances in this research area that provide new insights into OR modelling techniques and algorithms. The findings from this Special Issue are important for researchers, practitioners, policy makers, decision makers, and relevant stakeholders to deliver an ideal world.

Dr. Trung Hieu Tran
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 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

  • mathematical programming
  • meta-heuristic algorithms
  • agent-based modelling
  • simulation-based optimization
  • location analysis
  • logistics and supply chain
  • transportation
  • network optimization
  • big data analytics
  • production planning and scheduling
  • stochastic optimization
  • robust optimization

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (1 paper)

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Research

27 pages, 4378 KiB  
Article
Reputation-Driven Asynchronous Federated Learning for Optimizing Communication Efficiency in Big Data Labeling Systems
by Xuanzhu Sheng, Chao Yu, Yang Zhou and Xiaolong Cui
Mathematics 2024, 12(18), 2932; https://doi.org/10.3390/math12182932 - 20 Sep 2024
Viewed by 753
Abstract
With the continuous improvement of the performance of artificial intelligence and neural networks, a new type of computing architecture-edge computing, came into being. However, when the scale of hybrid intelligent edge systems expands, there are redundant communications between the node and the parameter [...] Read more.
With the continuous improvement of the performance of artificial intelligence and neural networks, a new type of computing architecture-edge computing, came into being. However, when the scale of hybrid intelligent edge systems expands, there are redundant communications between the node and the parameter server; the cost of these redundant communications cannot be ignored. This paper proposes a reputation-based asynchronous model update scheme and formulates the federated learning scheme as an optimization problem. First, the explainable reputation consensus mechanism for hybrid intelligent labeling systems communication is proposed. Then, during the process of local intelligent data annotation, significant challenges in consistency, personalization, and privacy protection posed by the federated recommendation system prompted the development of a novel federated recommendation framework utilizing a graph neural network. Additionally, the method of information interaction model fusion was adopted to address data heterogeneity and enhance the uniformity of distributed intelligent annotation. Furthermore, to mitigate communication delays and overhead, an asynchronous federated learning mechanism was devised based on the proposed reputation consensus mechanism. This mechanism leverages deep reinforcement learning to optimize the selection of participating nodes, aiming to maximize system utility and streamline data sharing efficiency. Lastly, integrating the learned models into blockchain technology and conducting validation ensures the reliability and security of shared data. Numerical findings underscore that the proposed federated learning scheme achieves higher learning accuracy and enhances communication efficiency. Full article
(This article belongs to the Special Issue New Advances of Operations Research and Analysis)
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