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Information
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Optimization Algorithms and Their Applications
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Optimization Algorithms and Their Applications

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 5197

Special Issue Editor

Dr. Christos Gogos

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece
Interests: optimization algorithms; combinatorial optimization; scheduling; timetabling; operations research; machine learning

Special Issue Information

Dear Colleagues,

MDPI’s Information journal is introducing a new Special Issue entitled “Optimization Algorithms and Their Applications”. Original papers related to optimization algorithms and their applications will be considered for publication. This Special Issue aims to bring together researchers in the optimization algorithms and combinatorial optimization research communities to present innovative research results or novel applications. In this Special Issue, we solicit papers on various aspects of optimization algorithms from various fields such as artificial intelligence, machine learning, computer science, graphs, and novel applications of optimization on scheduling, timetabling, transportation and logistics, robotic path planning, and others to promote research activities in these fields.

Optimization problems are ubiquitous, manifesting themselves in various settings, affecting organizations (e.g., hospitals, schools, and universities), companies (e.g., transport companies, call centers, and service industries), and society at large (e.g., resource capacity planning). In this Special Issue, we welcome you to present your findings regarding the latest advances in complex optimization problems. Papers may present optimization algorithms, feature applications, novel approaches, innovative techniques, and theoretical findings on difficult optimization problems. Since such problems are computationally hard, high-performance computing approaches are welcomed and endorsed.

We welcome you to submit your most recent work in the fields of optimization algorithms, scheduling, timetabling, and their applications to this Special Issue, "Optimization Algorithms and Their Applications", in Information. Researchers from both industry and academia are warmly invited to submit either theoretical or practical research.

Dr. Christos Gogos
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. Information 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 1600 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

  • operations research
  • scheduling
  • timetabling
  • artificial intelligence
  • heuristics
  • metaheuristics
  • machine learning
  • graph algorithms
  • linear programming
  • mixed integer programming
  • constraint programming
  • educational timetabling
  • healthcare timetabling
  • employee rostering
  • social network analysis
  • urban planning and traffic management
  • path planning in autonomous systems
  • portfolio optimization
  • service industries optimization
  • parallel and distributed approaches to scheduling and timetabling

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

Published Papers (4 papers)

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Research

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21 pages, 1072 KiB  
Article
Community Detection Using Deep Learning: Combining Variational Graph Autoencoders with Leiden and K-Truss Techniques
by Jyotika Hariom Patil, Petros Potikas, William B. Andreopoulos and Katerina Potika
Information 2024, 15(9), 568; https://doi.org/10.3390/info15090568 - 16 Sep 2024
Viewed by 897
Abstract
Deep learning struggles with unsupervised tasks like community detection in networks. This work proposes the Enhanced Community Detection with Structural Information VGAE (VGAE-ECF) method, a method that enhances variational graph autoencoders (VGAEs) for community detection in large networks. It incorporates community structure information [...] Read more.
Deep learning struggles with unsupervised tasks like community detection in networks. This work proposes the Enhanced Community Detection with Structural Information VGAE (VGAE-ECF) method, a method that enhances variational graph autoencoders (VGAEs) for community detection in large networks. It incorporates community structure information and edge weights alongside traditional network data. This combined input leads to improved latent representations for community identification via K-means clustering. We perform experiments and show that our method works better than previous approaches of community-aware VGAEs. Full article
(This article belongs to the Special Issue Optimization Algorithms and Their Applications)
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19 pages, 373 KiB  
Article
A New Integer Model for Selecting Students at Higher Education Institutions: Preparatory Classes of Engineers as Case Study
by Soufyane Majdoub, Chakir Loqman and Jaouad Boumhidi
Information 2024, 15(9), 529; https://doi.org/10.3390/info15090529 - 2 Sep 2024
Viewed by 716
Abstract
This study addresses the challenge of selecting outstanding students at higher education institutions under multiple constraints. We propose a novel integer programming solution to manage this process, formulating it as a constrained assignment problem with a maximization objective function. This function prioritizes the [...] Read more.
This study addresses the challenge of selecting outstanding students at higher education institutions under multiple constraints. We propose a novel integer programming solution to manage this process, formulating it as a constrained assignment problem with a maximization objective function. This function prioritizes the fair selection of students while respecting criteria such as academic qualifications, required skills, and student preferences. The goal is to develop a decision support system that efficiently selects qualified students at higher education institutions within a reasonable time. The model was tested using real data from Moroccan preparatory classes, achieving important assignment rates across all student categories. Results demonstrate significance in execution time, fulfillment of student choices, and prioritization of outstanding students. This approach offers a flexible, efficient solution for managing academic merit-based selections, optimizing resource utilization, and enhancing fairness in the selection process. Full article
(This article belongs to the Special Issue Optimization Algorithms and Their Applications)
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19 pages, 404 KiB  
Article
A New Algorithm Framework for the Influence Maximization Problem Using Graph Clustering
by Agostinho Agra and Jose Maria Samuco
Information 2024, 15(2), 112; https://doi.org/10.3390/info15020112 - 14 Feb 2024
Cited by 1 | Viewed by 1703
Abstract
Given a social network modelled by a graph, the goal of the influence maximization problem is to find k vertices that maximize the number of active vertices through a process of diffusion. For this diffusion, the linear threshold model is considered. A new [...] Read more.
Given a social network modelled by a graph, the goal of the influence maximization problem is to find k vertices that maximize the number of active vertices through a process of diffusion. For this diffusion, the linear threshold model is considered. A new algorithm, called ClusterGreedy, is proposed to solve the influence maximization problem. The ClusterGreedy algorithm creates a partition of the original set of nodes into small subsets (the clusters), applies the SimpleGreedy algorithm to the subgraphs induced by each subset of nodes, and obtains the seed set from a combination of the seed set of each cluster by solving an integer linear program. This algorithm is further improved by exploring the submodularity property of the diffusion function. Experimental results show that the ClusterGreedy algorithm provides, on average, higher influence spread and lower running times than the SimpleGreedy algorithm on Watts–Strogatz random graphs. Full article
(This article belongs to the Special Issue Optimization Algorithms and Their Applications)
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Review

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19 pages, 2302 KiB  
Review
Solutions to Address the Low-Capacity Utilization Issue in Singapore’s Precast Industry
by Chen Chen and Robert Tiong
Information 2024, 15(8), 458; https://doi.org/10.3390/info15080458 - 1 Aug 2024
Viewed by 1057
Abstract
Singapore has established six Integrated Construction and Prefabrication Hubs with the goal of meeting ambitious productivity targets and building a resilient precast supply chain by 2024. These factories are equipped with high levels of mechanization and automation. However, they are currently operating far [...] Read more.
Singapore has established six Integrated Construction and Prefabrication Hubs with the goal of meeting ambitious productivity targets and building a resilient precast supply chain by 2024. These factories are equipped with high levels of mechanization and automation. However, they are currently operating far below their designed capacity due to a storage bottleneck. In land-scarce Singapore, finding large spaces for precast storage is a challenge. One possible solution is to implement a just-in-time approach. To achieve this, a systematic approach is required to plan, monitor, and control the entire supply chain effectively, utilizing various strategies, methods, and tools. This paper aims to conduct a comprehensive literature review in related areas, believing that knowledge transfer is a faster way to develop solutions to new problems. The main idea of the proposed solution is to implement an integrated supply chain system model with a central decision-maker. It is recommended that the factories take a more active role in decision-making. Establishing this integrated system relies on trust and information sharing, which can be facilitated by cutting-edge digital technologies. The results of this paper will provide valuable insights for future research aimed at completely solving this issue. Full article
(This article belongs to the Special Issue Optimization Algorithms and Their Applications)
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