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Mathematics
Special Issues
Advanced Optimization Modeling and Algorithms for Planning and Scheduling
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Advanced Optimization Modeling and Algorithms for Planning and Scheduling

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1061

Special Issue Editor

Dr. Panagiotis Oikonomou

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of Thessaly, 3rd km Lamia–Athens, 35100 Lamia, Greece
Interests: cloud/edge computing; scheduling algorithms; evolutionary techniques; computational intelligence; mathematical optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue of the journal Mathematics. With the growing complexity of systems in industries, such as manufacturing, logistics, healthcare, and energy, robust mathematical approaches and innovative algorithms are essential for achieving efficient and effective solutions. We welcome submissions addressing the advancements in optimization, algorithm design, and practical implementations that contribute to the field’s body of knowledge. Topics may include, but are not limited to, integer and linear programming, combinatorial optimization, stochastic and robust optimization, machine learning approaches for optimization, and multi-objective scheduling. We especially encourage research that demonstrates the integration of mathematical rigor with computational efficiency in computing environments encompassing cloud, edge, fog, and multi-access edge computing.

Dr. Panagiotis Oikonomou
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

  • planning and scheduling
  • mathematical programming
  • combinatorial optimization
  • stochastic and robust optimization
  • algorithm design
  • machine learning
  • multi-objective optimization
  • cloud, edge and fog computing
  • multi-access edge computing (MEC)
  • real-world applications
  • energy optimization
  • cost optimization

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Published Papers (1 paper)

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Research

17 pages, 624 KiB  
Article
Parallel Simulation Multi-Sample Task Scheduling Approach Based on Deep Reinforcement Learning in Cloud Computing Environment
by Yuhao Xiao, Yping Yao and Feng Zhu
Mathematics 2025, 13(14), 2249; https://doi.org/10.3390/math13142249 - 11 Jul 2025
Viewed by 327
Abstract
Complex scenario analysis and evaluation simulations often involve multiple sets of simulation applications with different combinations of parameters, thus resulting in high computing power consumption, which is one of the factors that limits the efficiency of multi-sample parallel simulations. Cloud computing provides considerable [...] Read more.
Complex scenario analysis and evaluation simulations often involve multiple sets of simulation applications with different combinations of parameters, thus resulting in high computing power consumption, which is one of the factors that limits the efficiency of multi-sample parallel simulations. Cloud computing provides considerable amounts of cheap and convenient computing resources, thus providing efficient support for multi-sample simulation tasks. However, traditional simulation scheduling methods do not consider the collaborative parallel scheduling of multiple samples and multiple entities under multi-objective constraints. Deep reinforcement learning methods can continuously learn and adjust their strategies through interactions with the environment, demonstrating strong adaptability in response to dynamically changing task requirements. Therefore, herein, a parallel simulation multi-sample task scheduling method based on deep reinforcement learning in a cloud computing environment is proposed. The method collects cluster load information and simulation application information as state inputs in the cloud environment, designs a multi-objective reward function to balance the cost and execution efficiency, and uses deep Q-networks (DQNs) to train agents for intelligent scheduling of multi-sample parallel simulation tasks. In a real cloud environment, the proposed method demonstrates runtime reductions of 4–11% and execution cost savings of 11–22% compared to the Round-Robin algorithm, Best Fit algorithm, and genetic algorithm. Full article
(This article belongs to the Special Issue Advanced Optimization Modeling and Algorithms for Planning and Scheduling)
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