Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.6
2.2
Journals
Mathematics
Special Issues
Advanced Approaches to Mathematical Programming: Exact Methods, Metaheuristics, and Machine...
share announcement

Advanced Approaches to Mathematical Programming: Exact Methods, Metaheuristics, and Machine Learning Synergies

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

Deadline for manuscript submissions: closed (20 May 2025) | Viewed by 302

Special Issue Editors

Dr. Seyed Mahdi Shavarani

E-Mail Website
Guest Editor
Kent Business School, University of Kent, Kent, UK
Interests: operations research; multi-objective optimization algorithms, optimization and decision-making tools
Dr. Mahmoud Golabi

E-Mail
Guest Editor
IRIMAS Laboratory, University of Haute-Alsace (UHA), IRIMAS UR 7499, F-68100 Mulhouse, France
Interests: optimization; metaheuristics; parallel computing; machine learning; computational geometry

Special Issue Information

Dear Colleagues,

Addressing complex real-world optimization challenges necessitates the application of advanced solution methodologies in mathematical programming. This Special Issue, titled "Advanced Approaches to Mathematical Programming: Exact Methods, Metaheuristics, and Machine Learning Synergies", aims to explore diverse methodologies and innovative integrations that move this field forward. By focusing on both individual techniques and their hybridization, this issue seeks to uncover synergies that enhance the efficiency, accuracy, and scalability of solving complex optimization problems.

The scope of this Special Issue includes exact methods, metaheuristics, machine learning techniques, and their combinations, applied to both single and multi-objective optimization problems. Integrating these methodologies opens new avenues in problem-solving by leveraging the strengths of each approach. This synergy is beneficial for addressing complex challenges in fields like logistics, finance, engineering design, and artificial intelligence.

We invite papers that delve into theoretical advancements, practical applications, and innovative integrations of exact methods, metaheuristics, and machine learning in mathematical programming. Contributions highlighting novel algorithms, case studies, comparative analyses, and interdisciplinary research that bridge these approaches are especially welcome.

Dr. Seyed Mahdi Shavarani
Dr. Mahmoud Golabi
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

  • mathematical programming
  • machine learning
  • operations research
  • metaheuristics
  • hybrid approaches
  • optimization techniques
  • multi-objective optimization
  • optimization problems
  • interdisciplinary applications

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

31 pages, 1807 KiB  
Article
Network- and Demand-Driven Initialization Strategy for Enhanced Heuristic in Uncapacitated Facility Location Problem
by Jayson Lin, Shuo Yang, Kai Huang, Kun Wang and Sunghoon Jang
Mathematics 2025, 13(13), 2138; https://doi.org/10.3390/math13132138 - 30 Jun 2025
Abstract
As network scale and demand rise, the Uncapacitated Facility Location Problem (UFLP), a classical NP-hard problem widely studied in operations research, becomes increasingly challenging for traditional methods confined to formulation, construction, and benchmarking. This work generalizes the UFLP to network setting in light [...] Read more.
As network scale and demand rise, the Uncapacitated Facility Location Problem (UFLP), a classical NP-hard problem widely studied in operations research, becomes increasingly challenging for traditional methods confined to formulation, construction, and benchmarking. This work generalizes the UFLP to network setting in light of demand intensity and network topology. A new initialization technique called Network- and Demand-Weighted Roulette Wheel Initialization (NDWRWI) has been introduced and proved to be a competitive alternative to random (RI) and greedy initializations (GI). Experiments were carried out based on the TRB dataset and compared eight state-of-the-art methods. For instance, in the ultra-large-scale Gold Coast network, the NDWRWI-based Neighborhood Search (NS) achieved a competitive optimal total cost (9,372,502), closely comparable to the best-performing baseline (RI-based: 9,189,353), while delivering superior clustering quality (Silhouette: 0.3859 vs. 0.3833 and 0.3752 for RI- and GI-based NS, respectively) and reducing computational time by nearly an order of magnitude relative to the GI-based baseline. Similarly, NDWRWI-based Variable Neighborhood Search (VNS) improved upon RI-based baseline by reducing the overall cost by approximately 3.67%, increasing clustering quality and achieving a 27% faster runtime. It is found that NDWRWI prioritizes high-demand and centrally located nodes, fostering high-quality initial solutions and robust performance across large-scale and heterogeneous networks. Full article
(This article belongs to the Special Issue Advanced Approaches to Mathematical Programming: Exact Methods, Metaheuristics, and Machine Learning Synergies)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop