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Mathematics
Special Issues
Intelligent Scheduling and Optimization in Smart Manufacturing
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Intelligent Scheduling and Optimization in Smart Manufacturing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D: Statistics and Operational Research".

Deadline for manuscript submissions: 20 June 2026 | Viewed by 1267

Special Issue Editor

Dr. Ziyan Zhao

E-Mail Website
Guest Editor
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: intelligent optimization decision; artificial intelligence; intelligent manufacturing; production planning; scheduling

Special Issue Information

Dear Colleagues,

Smart manufacturing has emerged as a transformative paradigm that integrates advanced information technologies, artificial intelligence, and data-driven decision-making to enhance the efficiency, benefits, flexibility, and sustainability of modern industrial systems. Among its core challenges, production scheduling and optimization play a pivotal role in orchestrating resources, operations, and logistics in complex manufacturing environments.

This Special Issue aims to provide a platform for researchers and practitioners to share recent advances, innovative methodologies, and practical applications in intelligent scheduling and optimization. We particularly welcome contributions that leverage machine learning, meta-heuristic algorithms, optimization theory, large language models, and digital twin technologies to address challenges in complex scheduling and optimization problems arising from smart manufacturing systems. Both theoretical developments and real-world case studies are encouraged to bridge the gap between academic research and industrial practice.

Topics of interest include, but are not limited to:

  • Multi-objective optimization in manufacturing systems;
  • Data-driven and knowledge-based scheduling methods;
  • Digital twins and production scheduling for complex manufacturing systems;
  • Adaptive and real-time scheduling under uncertainty;
  • Scheduling and optimization for human–machine collaboration systems;
  • Application of learning-based methods in smart manufacturing systems;
  • Intelligent operation, maintenance, control, and scheduling in complex systems;
  • Applications of meta-heuristic and evolutionary algorithms for smart manufacturing.

Dr. Ziyan Zhao
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

  • smart manufacturing
  • intelligent algorithm
  • production scheduling
  • intelligent scheduling
  • data-driven scheduling
  • real-time scheduling
  • multi-objective optimization
  • optimization

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Published Papers (2 papers)

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33 pages, 4133 KB  
Article
Low-Carbon Scheduling Optimization for Flexible Job Shop Production with a Time-of-Use Pricing Strategy and a Photovoltaic Microgrid
by Qi Lu, Chenxu Wei, Zirong Guo, Xiangang Cao, Chao Zhang and Guanghui Zhou
Mathematics 2026, 14(4), 590; https://doi.org/10.3390/math14040590 - 8 Feb 2026
Viewed by 358
Abstract
To achieve “carbon peak and carbon neutrality” in manufacturing, this paper tackles high energy consumption in flexible job shop production by developing a low-carbon scheduling optimization model with time-of-use electricity pricing, incorporating a photovoltaic microgrid. The model minimizes makespan, carbon emissions, and costs, [...] Read more.
To achieve “carbon peak and carbon neutrality” in manufacturing, this paper tackles high energy consumption in flexible job shop production by developing a low-carbon scheduling optimization model with time-of-use electricity pricing, incorporating a photovoltaic microgrid. The model minimizes makespan, carbon emissions, and costs, considering photovoltaic power uncertainty, energy storage dynamics, and time-of-use pricing. To address coupled scheduling and energy management challenges, a three-stage bilevel collaborative optimization framework is proposed, enhancing the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to develop a Collaborative MOPSO (CMOPSO). The improved algorithm features a four-layer encoding mechanism with energy factors, chaotic mapping for better global search, and adaptive mutation for population diversity. Validation using the Brandimarte benchmark demonstrates the algorithm’s robustness. Specifically, comparative experiments reveal that the proposed strategy significantly outperforms the traditional scheduling mode. While maintaining a similar makespan, the proposed method reduces production costs by 44.3% and carbon emissions by 29%. Simulations confirm that the method effectively shifts tasks to low-price periods and leverages photovoltaic energy during peak hours, supporting the manufacturing industry’s green transition. Full article
(This article belongs to the Special Issue Intelligent Scheduling and Optimization in Smart Manufacturing)
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20 pages, 1285 KB  
Article
Optimization of Process Parameters for Medium and Thick Plates to Balance Energy Saving and Mechanical Performance
by Qiang Guo, Jingjie Gao, Xinyu Liang, Lei Song, Fengwei Jing and Jin Guo
Mathematics 2025, 13(24), 3907; https://doi.org/10.3390/math13243907 - 6 Dec 2025
Viewed by 432
Abstract
As an important basic material for modern industry, the performance and production energy consumption of medium and thick plates have an important impact on engineering quality, industry technological progress and economic benefits. However, traditional process parameter adjustment relies on manual experience, which is [...] Read more.
As an important basic material for modern industry, the performance and production energy consumption of medium and thick plates have an important impact on engineering quality, industry technological progress and economic benefits. However, traditional process parameter adjustment relies on manual experience, which is difficult to meet the dual needs of efficient production and energy conservation and emission reduction. This paper focuses on the energy consumption optimization problem in the production process of medium and thick plates. Under the premise of meeting the mechanical property constraints, a data-driven process parameter optimization method is proposed. Firstly, a comprehensive energy consumption prediction model for medium and thick plates is established. Secondly, based on historical data and knowledge, a data set covering chemical composition, physical parameters and process parameters is constructed, and a mechanical property prediction model is developed to achieve the prediction of actual performance. On this basis, the energy consumption minimization problem that satisfies mechanical property constraints is modeled as a constrained optimization problem, and a data-inspired initialized particle swarm optimization algorithm is designed to improve the global search capability and local convergence efficiency. Experimental results confirm that the proposed model provides more stable and accurate prediction of mechanical properties than conventional Random Forest and XGBoost models. Furthermore, compared with standard PSO, GA, SA, and ACO algorithms, the data-inspired initialized particle swarm optimization shows faster convergence and better energy-saving performance, demonstrating the overall effectiveness and practical potential of the proposed framework. Full article
(This article belongs to the Special Issue Intelligent Scheduling and Optimization in Smart Manufacturing)
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