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Processes
Special Issues
Intelligent Operation, Maintenance, and Scheduling of Industrial Manufacturing Processes
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Intelligent Operation, Maintenance, and Scheduling of Industrial Manufacturing Processes

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 786

Special Issue Editors

Dr. Bin Hu

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Kean University, Union, NJ 07083, USA
Interests: cybersecurity and privacy; efficient deep learning; sustainable manufacturing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jiacun Wang

E-Mail Website
Guest Editor
Department of Computer Science and Software Engineering, Monmouth University, West Long Branch, NJ 07764, USA
Interests: machine learning; software engineering; discrete event systems; formal methods; wireless networking; real-time distributed systems
Special Issues, Collections and Topics in MDPI journals
Dr. Xiwang Guo

E-Mail Website
Guest Editor
1. Information and Control Engineering College, Liaoning Petrochemical University, Fushun 113001, China
2. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: deep reinforcement learning; intelligent optimization algorithms; autonomous vehicles; detection model; artificial intelligence; intelligent manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industrial manufacturing processes, including process manufacturing and discrete manufacturing, are complex systems in the real world. Operation, maintenance, and scheduling problems are crucial to an industrial manufacturing process, affecting its efficiency, energy consumption, production costs, safety and stability. In recent years, the theory and technology of artificial intelligence have been evolving rapidly. Applications of artificial intelligence technology, like deep learning, machine learning, evolutionary computation, and heuristic algorithms, are gradually affecting all walks of life in the real world. Integrating artificial intelligence technology with specific industrial manufacturing processes to solve its intelligent operation, maintenance, and scheduling problems is an important demand of industry and a growing research field in academia. Related research can improve operation efficiency, ensure safety and stability, and save energy consumption and production costs, which can further improve the level of intelligent manufacturing in an industrial manufacturing process. While there has been some existing research on these challenges, a lot of related problems issues remain unsolved.

This Special Issue provides a platform to exchange research works, technical trends and practical experience related to fault diagnosis, process control, operation research, applied mathematics and  management science. The goal is to broaden the intelligent manufacturing research community and promote the application of artificial intelligence in industrial manufacturing processes. Topics to be covered include, but are not limited to, the following:

  • Design, control and optimization of assembly systems
  • Design, control and optimization of disassembly systems
  • Digital twin techniques in manufacturing
  • Emission control and energy saving in manufacturing
  • End-of-life product recycling
  • Formal methods in the modeling, verification and analysis of manufacturing systems, such as Petri nets, finite automata, UML, queuing theory, model checking techniques, etc.
  • Heuristic search algorithms
  • Intelligent factory
  • Real-time task allocation
  • Real-time task scheduling
  • Machine learning and reinforcement learning in manufacturing
  • Smart sensing and control
  • Smart logistics management
  • System simulation and performance evaluation
  • Sustainability manufacturing
  • Workstation load balancing in manufacturing, assembly and disassembly

Dr. Bin Hu
Prof. Dr. Jiacun Wang
Dr. Xiwang Guo
Guest Editors

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. Processes 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 2400 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

  • intelligent manufacturing
  • human–robot collaboration
  • disassembly line optimization
  • artificial intelligence and machine learning
  • smart sensing and control
  • real-time scheduling and operation
  • sustainable manufacturing
  • digital twin and system simulation
  • energy-efficient production
  • heuristic and evolutionary optimization

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

Published Papers (2 papers)

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20 pages, 3258 KB  
Article
A Q-Trend Adaptive Regulation and RUDDER Reward Redistribution Framework for Hazardous Material Routing Optimization
by Kerang Cao, Minghui Geng, Mingxu Yu, Jietan Geng, Hoekyung Jung and Han Wang
Processes 2026, 14(4), 604; https://doi.org/10.3390/pr14040604 - 9 Feb 2026
Viewed by 253
Abstract
In hazardous material transportation route planning, complex environments, stringent safety requirements, and sparse rewards often cause traditional reinforcement learning methods to converge slowly and yield unstable policies. To address these issues, this paper proposes TAR-RL (Trend-Aware and Attention-Augmented Reward Redistribution Reinforcement Learning), which [...] Read more.
In hazardous material transportation route planning, complex environments, stringent safety requirements, and sparse rewards often cause traditional reinforcement learning methods to converge slowly and yield unstable policies. To address these issues, this paper proposes TAR-RL (Trend-Aware and Attention-Augmented Reward Redistribution Reinforcement Learning), which integrates reward redistribution and adaptive exploration to improve the efficiency and robustness of path planning. Specifically, a dynamic ε-greedy strategy based on Q-value trends is introduced to adaptively adjust the exploration rate. Meanwhile, the RUDDER framework decomposes and redistributes delayed rewards to strengthen learning signals at critical decision points. Furthermore, a multi-head attention mechanism is incorporated to model long-term dependencies between states and actions, improving return prediction accuracy. An intrinsic reward mechanism encourages proactive exploration and autonomous learning. Experiments on two grid environments with different complexity levels demonstrate that TAR-RL achieves faster convergence, higher success rates, and more stable training dynamics than baseline methods. These results indicate that TAR-RL is well-suited for safety-aware path planning in high-risk routing scenarios. Full article
(This article belongs to the Special Issue Intelligent Operation, Maintenance, and Scheduling of Industrial Manufacturing Processes)
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23 pages, 1285 KB  
Article
GTO-YOLO11n: YOLOv11n-Based Efficient Target Detection in Ship Remote Sensing Imagery
by Bei Xiao, Peisheng Liu, Xiwang Guo, Bin Hu, Jiankang Ren and Yushuang Jiang
Processes 2026, 14(4), 583; https://doi.org/10.3390/pr14040583 - 7 Feb 2026
Viewed by 264
Abstract
Accurate and efficient ship detection in remote sensing imagery is a key enabler of intelligent maritime surveillance operations, supporting real-time decision-making in search and rescue, traffic management, and maritime law enforcement. However, remote ship images pose unique challenges for detection. These include densely [...] Read more.
Accurate and efficient ship detection in remote sensing imagery is a key enabler of intelligent maritime surveillance operations, supporting real-time decision-making in search and rescue, traffic management, and maritime law enforcement. However, remote ship images pose unique challenges for detection. These include densely distributed targets, complex sea-land backgrounds, large aspect ratios, diverse ship geometries, and high color similarity between ships and their surroundings. To address these issues under the computational constraints of unmanned aerial platforms, we propose GTO-YOLO11n, an enhanced YOLOv11n-based detection model tailored for efficient maritime ship sensing. First, we introduce the GatedFDConvBlock, which employs gated convolutional filtering to strengthen feature extraction for small and elongated ships while suppressing background clutter, thereby reducing missed and false detections in dense scenes. Second, we improve the C2PSA module with a dynamic multi-scale attention design, TSSABlock_DMS, to adaptively model cross-scale feature interactions and enhance robustness to complex maritime environments. Third, we replace the original detection head with OBB_ED, a parameter-sharing head that incorporates depthwise separable convolution (DSConv) and an angle prediction branch to lower model complexity while preserving high-quality localization and classification. To verify the performance of the algorithm, we were conducted on the public datasets HRSC2016, HRSC2016-MS, and ShipRSImageNet. The mAP@50 results were 95.2%, 88.3%, and 76.7%, showing improvements of 3.2%, 2.2%, and 2.6% compared to the original YOLOv11n. Full article
(This article belongs to the Special Issue Intelligent Operation, Maintenance, and Scheduling of Industrial Manufacturing Processes)
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