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Mathematics
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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 5857

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 250 words) can be sent to the Editorial Office for assessment.

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 (5 papers)

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Research

32 pages, 15510 KB  
Article
Continuous-Time Scheduling of Berths and Onshore Power Supply in Cold-Chain Logistics: A Chance-Constrained Stochastic Programming Model and RL-ALNS Algorithm
by Zheyin Zhao and Jin Zhu
Mathematics 2026, 14(8), 1292; https://doi.org/10.3390/math14081292 - 13 Apr 2026
Viewed by 341
Abstract
Amid tightening emission rules and growing cold-chain demand, ports face complex multi-objective scheduling under dual uncertainties in vessel arrivals and operations. This work develops a multi-objective chance-constrained stochastic MILP model for joint berth, QC, and OPS scheduling. Heavy-tailed operational delays are managed via [...] Read more.
Amid tightening emission rules and growing cold-chain demand, ports face complex multi-objective scheduling under dual uncertainties in vessel arrivals and operations. This work develops a multi-objective chance-constrained stochastic MILP model for joint berth, QC, and OPS scheduling. Heavy-tailed operational delays are managed via chance constraints, converting Weibull distributions to time buffers, while convex formulations allow piecewise cargo damage penalties to be computed linearly. A reinforcement learning-based adaptive large neighborhood search (RL-ALNS) algorithm is proposed to solve this NP-hard continuous-time problem, integrating a spatiotemporal decoder and an MDP-based selector to ensure microgrid limits and efficiency. Simulations demonstrate RL-ALNS’s superior Pareto convergence versus conventional heuristics. The model cuts the 95th-percentile tail risk by 46.59% and actual costs by 24.44% under mild delays, compared to deterministic scheduling. Overall, it quantifies the non-linear cost–emission–reliability trade-off, providing a robust tool for port decision-making. Full article
(This article belongs to the Special Issue Advanced Optimization Modeling and Algorithms for Planning and Scheduling)
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22 pages, 3601 KB  
Article
On Exploiting Tile Partitioning to Reduce Bitrate and Processing Time in VVC Surveillance Streams with Object Detection
by Panagiotis Belememis, Maria Koziri and Thanasis Loukopoulos
Mathematics 2026, 14(2), 368; https://doi.org/10.3390/math14020368 - 22 Jan 2026
Viewed by 466
Abstract
One of the main targets in video surveillance systems is to detect and possibly identify objects within monitoring range. This entails analyzing the video stream, by applying object detection techniques on one or more frames. Regardless of the output, the stream is usually [...] Read more.
One of the main targets in video surveillance systems is to detect and possibly identify objects within monitoring range. This entails analyzing the video stream, by applying object detection techniques on one or more frames. Regardless of the output, the stream is usually archived for future use. Real-time requirements, network bandwidth, and storage constraints play a significant role to total performance. As video resolution increases, so does the video stream size. To harness such an increase, newer video compression standards offer sophisticated coding tools that aim at reducing video size, with minimal quality loss. However, as the achievable compression ratio increases, so does the computational complexity. In this paper, we propose a methodology to reduce both bitrate and processing time of video surveillance streams whereby object detection is performed. The method takes advantage of tile partitioning, with the aim of (i) reducing the scope and the invocation frequency of the object detection module, (ii) encoding different blocks of a frame at different quality levels, depending on whether objects exist or not, and (iii) encoding and transmitting only tiles containing objects. Experimental results using the UA-DETRAC dataset and the VVenC encoder demonstrate that exploiting tile partitioning in the manner proposed in the paper results in reducing bitrate and processing time at the expense of only tiny losses in accuracy. Full article
(This article belongs to the Special Issue Advanced Optimization Modeling and Algorithms for Planning and Scheduling)
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26 pages, 624 KB  
Article
Two-Stage Analysis for Supply Chain Disruptions Considering the Trade-Off Between Profit Maximization and Adaptability
by Tomohiro Hayashida, Ichiro Nishizaki, Shinya Sekizaki and Keigo Tsukuda
Mathematics 2025, 13(24), 4017; https://doi.org/10.3390/math13244017 - 17 Dec 2025
Viewed by 664
Abstract
Considering the trade-off between profit maximization and adaptability to supply chain disruptions, we examine herein the decision-making for configuration and distribution plans in a supply chain. Supply chain disruptions are caused by facility accidents and disasters. In this work, we investigate an optimal [...] Read more.
Considering the trade-off between profit maximization and adaptability to supply chain disruptions, we examine herein the decision-making for configuration and distribution plans in a supply chain. Supply chain disruptions are caused by facility accidents and disasters. In this work, we investigate an optimal configuration and distribution plan in the supply chain with disruptions, including the opening of additional facilities while maintaining the optimum supply amounts to customers in the profit maximization plan when no such disruptions occur. Assuming the existence of uncertainties in demands and supplies, we formulate a two-stage model with a simple recourse, in which decisions on the supply chain configuration are made at the first stage. Decisions on the distribution are made at the second stage after the demands and supplies are realized. For such a configuration and distribution in the supply chain, we propose TSA-SCD (Two-Stage Analysis for Supply Chain Disruptions), a novel decision-making framework considering the trade-off between profit maximization and adaptability to supply chain disruptions. Accordingly, we perform numerical experiments with different degrees of disruptions to verify the effectiveness of the proposed decision method. Full article
(This article belongs to the Special Issue Advanced Optimization Modeling and Algorithms for Planning and Scheduling)
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30 pages, 21178 KB  
Article
Gaussian Learning-Based Pareto Evolutionary Algorithm for Parallel Machine Planning in Industrial Silicon Production
by Jinsi Zhang, Rongjuan Luo and Zuocheng Li
Mathematics 2025, 13(23), 3860; https://doi.org/10.3390/math13233860 - 2 Dec 2025
Viewed by 699
Abstract
This study focuses on a multi-objective heterogeneous parallel machine planning problem for industrial silicon smelting. Specifically, under the conflicting objectives of minimizing carbon emissions, rollover penalty costs, and load imbalance, the total production demand of industrial silicon is allocated monthly across multiple machines. [...] Read more.
This study focuses on a multi-objective heterogeneous parallel machine planning problem for industrial silicon smelting. Specifically, under the conflicting objectives of minimizing carbon emissions, rollover penalty costs, and load imbalance, the total production demand of industrial silicon is allocated monthly across multiple machines. We first establish the mathematical model of the problem accounting for real-life management requirements. To solve the model, a Gaussian learning-based Pareto evolutionary algorithm (GLPEA) is proposed. The algorithm is developed based on a nondominated sorting framework and incorporates two key innovations: (1) a generation-wise dynamic Gaussian mixture component selection strategy that adaptively fits the multimodal distribution of elite solutions, and (2) a hybrid offspring generation mechanism that integrates traditional evolutionary operators with a Gaussian sampling strategy trained on perturbed solution sets, thereby enhancing exploration capability while maintaining convergence. The effectiveness of GLPEA is validated on 40 problem instances of varying scales. Compared with NSGA-II and MOEA/D, GLPEA achieves average improvements of 5.78% and 89.23% in IGD, and 1.03% and 264.43% in HV, respectively. We make the source codes of GLPEA publicly available to facilitate future research on practical applications. Full article
(This article belongs to the Special Issue Advanced Optimization Modeling and Algorithms for Planning and Scheduling)
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17 pages, 624 KB  
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
Cited by 1 | Viewed by 2227
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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