Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.4
2.2
Journals
Mathematics
Special Issues
Intelligent Scheduling and Optimization in Smart Manufacturing
share announcement

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 2278

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

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 (4 papers)

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

Research

71 pages, 5480 KB  
Article
MTTA: Modernized Tiki-Taka Algorithm with Role Specialization for Solving Engineering Application Problems and Feature Selection
by Xiangkun Song and Jian Zhao
Mathematics 2026, 14(11), 1900; https://doi.org/10.3390/math14111900 - 29 May 2026
Viewed by 188
Abstract
With the growing complexity of engineering optimization and high-dimensional data analysis tasks, balancing global exploration and local exploitation remains a core challenge in computational intelligence. The Tiki-taka Algorithm (TTA), a football-inspired metaheuristic, is simple to implement and provides competitive baseline performance. However, it [...] Read more.
With the growing complexity of engineering optimization and high-dimensional data analysis tasks, balancing global exploration and local exploitation remains a core challenge in computational intelligence. The Tiki-taka Algorithm (TTA), a football-inspired metaheuristic, is simple to implement and provides competitive baseline performance. However, it may suffer from premature convergence, rapid loss of population diversity, and rigid search transitions when solving complex multimodal or high-dimensional problems. This paper proposes the Modernized Tiki-taka Algorithm (MTTA), which incorporates a role-specialization mechanism inspired by contemporary football tactics into the optimization framework. MTTA adopts a Logistic–Tent hybrid chaotic mapping for uniform initial population distribution, and establishes a fitness-based three-role mechanism (forwards, midfielders, defenders) with tailored update rules, achieving a smooth, adaptive balance between exploration and exploitation throughout the iteration to fundamentally overcome TTA’s inherent flaws. Comprehensive experiments on classical benchmarks, the IEEE CEC 2017 test suite, constrained engineering problems, and feature selection tasks demonstrate that MTTA achieves statistically significant superiority over TTA, classic metaheuristics, and state-of-the-art optimizers in convergence speed, solution accuracy, and robustness, providing an efficient, scalable solution for complex real-world optimization scenarios. Full article
(This article belongs to the Special Issue Intelligent Scheduling and Optimization in Smart Manufacturing)
►▼ Show Figures

Figure 1

18 pages, 5285 KB  
Article
A Multi-Objective Grey Wolf Optimizer for Heterogeneous Hybrid Flow Shop Scheduling in Mass Customization
by Xinye Liu, Hongfeng Wang and Chenxi Tang
Mathematics 2026, 14(11), 1853; https://doi.org/10.3390/math14111853 - 26 May 2026
Viewed by 291
Abstract
Against the backdrop of mass customization, research interest in hybrid flow shop scheduling for standard and customized part production has been on the rise. However, most extant studies focus on single-shop scheduling optimization, and the inter-shop coordination mechanism for heterogeneous multi-shop systems remains [...] Read more.
Against the backdrop of mass customization, research interest in hybrid flow shop scheduling for standard and customized part production has been on the rise. However, most extant studies focus on single-shop scheduling optimization, and the inter-shop coordination mechanism for heterogeneous multi-shop systems remains underexplored. This paper investigates a heterogeneous hybrid flow shop scheduling problem featuring a distributed flow shop for standardized parts and a flexible job shop for customized parts, with the dual objectives of minimizing makespan and total cost. For this problem with the core complexity of heterogeneous cross-shop production reliance and conflicting dual-objective optimization, we propose a multi-objective grey wolf optimizer (MOGWO) combined with problem-specific local search strategies. Computational experiments on a set of test instances are carried out to evaluate the MOGWO’s performance, which is further compared with four classic multi-objective evolutionary algorithms of analogous algorithmic frameworks. Experimental results confirm that the proposed algorithm achieves superior solution quality and convergence efficiency for the multi-objective heterogeneous hybrid flow shop scheduling problem under study. Full article
(This article belongs to the Special Issue Intelligent Scheduling and Optimization in Smart Manufacturing)
►▼ Show Figures

Figure 1

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 546
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)
►▼ Show Figures

Figure 1

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 575
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop