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Applications of Artificial Intelligence in Industrial Engineering
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Applications of Artificial Intelligence in Industrial Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 11193

Special Issue Editors

Prof. Dr. Ke Yan

E-Mail Website
Guest Editor
Department of Mechanical and Electrical Engineering, Hunan University, Changsha 410082, China
Interests: big data privacy protection; intelligent buildings; information-based building management systems; energy consumption prediction
Special Issues, Collections and Topics in MDPI journals
Dr. Vincent Gan

E-Mail Website
Guest Editor
Department of the Built Environment, National University of Singapore, Singapore 119077, Singapore
Interests: AI; data-driven design; parametric structural optimization social media account
Special Issues, Collections and Topics in MDPI journals
Dr. Liangliang Jiang

E-Mail Website
Guest Editor
Chemical and Petroleum Department, University of Calgary, Calgary Alberta, AB T2N 1N4, Canada
Interests: clean coal technologies; hydrogen production; geological CO2 and H2 storage; geothermal energy; sustainable unconventional recoveries

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI), machine learning (ML), and data analytics technologies are slowly changing the traditional ways of handling industrial engineering applications and problems. Examples of AI-empowered engineering applications include autonomous vehicle/robots, smart building/city design, the Internet of Things (IoT), smart facility management, smart building maintenance, etc., where AI and ML techniques are involved in difference stages of various engineering applications.

Although AI and ML have great capabilities in modern engineering applications, there are still obstacles to adopting modern AI technologies for new and emerging engineering problems due to the uncertainty and unclear performance of AI. On one hand, it is well known that a well-implemented AI-enabled sensing system is a hidden and key factor for a successful and efficient engineering project. On the other hand, it is difficult to completely address the physical and mathematical interpretations in AI algorithms. While a significant amount of work and research has already been undertaken to address the existing AI issues in the current industrial engineering stage, these concerns continue to change in response to new AI technology developments and clients’ demand trends.

This Special Issue intends to provide an international forum for researchers to exchange up-to-date outcomes on AI, ML, and data analytics with their various applications to address the concerns in industrial engineering problems. These three exciting research areas (AI, ML, and data analytics) have attracted extensive research interests over the last decades, both from the AI methodology research community and industrial research groups. With the emergence of novel methods and systems, recent progress in these three areas is yet to be investigated and studied. Therefore, this Special Issue aims to satisfy this requirement, which will have great significance and a profound impact on society, including machine learning-enhanced engineering solutions in smart cities, automation and robotics solutions in engineering management, sensing and big data systems, computer and networks, human–computer interactions, and so on.

This Special Issue on the Application of Artificial Intelligence in Industrial Engineering solicits topics (among others) as follows:

  • The integration of AI in industrial engineering problems;
  • Information modeling for smart city;
  • Automatic/robotic device;
  • Human–computer interactions in industrial engineering;
  • AI in engineering management technologies;
  • Recent developments in Internet of things (IoTs) technology.
  • Big data analysis for industrial engineering applications.

Prof. Dr. Ke Yan
Dr. Vincent Gan
Dr. Liangliang Jiang
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 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. Applied Sciences 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

  • AI algorithms
  • machine learning
  • data analytics
  • industrial engineering

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

Published Papers (5 papers)

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Research

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30 pages, 5437 KiB  
Article
A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity
by Liwen Guo, Zhihong Kang, Shuaiwei Ding, Xuehao Yuan, Haitong Yang, Meng Zhang and Shuoliang Wang
Appl. Sci. 2024, 14(21), 9913; https://doi.org/10.3390/app14219913 - 29 Oct 2024
Cited by 1 | Viewed by 1012
Abstract
Given that more and more oil reservoirs are reaching the high water cut stage during water flooding, the construction of an advanced algorithmic model for identifying inter-well connectivity is crucial to improve oil recovery and extend the oilfield service life cycle. This study [...] Read more.
Given that more and more oil reservoirs are reaching the high water cut stage during water flooding, the construction of an advanced algorithmic model for identifying inter-well connectivity is crucial to improve oil recovery and extend the oilfield service life cycle. This study proposes a state variable-based dynamic capacitance (SV-DC) model that integrates artificial intelligence techniques with dynamic data and geological features to more accurately identify inter-well connectivity and its evolution. A comprehensive sensitivity analysis was performed on single-well pairs and multi-well groups regarding the permeability amplitude, the width of the high permeable channel, change, and lasting period of injection pressure. In addition, the production performance of multi-well groups, especially the development of ineffective circulation channels and their effects on reservoir development, are studied in-depth. The results show that higher permeability, wider permeable channels, and longer injection pressure maintenance can significantly enhance inter-well connectivity coefficients and reduce time-lag coefficients. Inter-well connectivity in multi-well systems is significantly affected by well-group configuration and inter-well interference effects. Based on the simulation results, the evaluation index of ineffective circulation channels is proposed and applied to dozens of well groups. These identified ineffective circulation channel changing patterns provide an important basis for optimizing oil fields’ injection and production strategies through data-driven insights and contribute to improving oil recovery. The integration of artificial intelligence enhances the ability to analyze complex datasets, allowing for more precise adjustments in field operations. This paper’s research ideas and findings can be confidently extended to other engineering scenarios, such as geothermal development and carbon dioxide storage, where AI-based models can further refine and optimize resource management and operational strategies. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Industrial Engineering)
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15 pages, 2191 KiB  
Article
YOLO-ESL: An Enhanced Pedestrian Recognition Network Based on YOLO
by Feilong Wang, Xiaobing Yang and Juan Wei
Appl. Sci. 2024, 14(20), 9588; https://doi.org/10.3390/app14209588 - 21 Oct 2024
Cited by 4 | Viewed by 1601
Abstract
Pedestrian detection is a critical task in computer vision; however, mainstream algorithms often struggle to achieve high detection accuracy in complex scenarios, particularly due to target occlusion and the presence of small objects. This paper introduces a novel pedestrian detection algorithm, YOLO-ESL, based [...] Read more.
Pedestrian detection is a critical task in computer vision; however, mainstream algorithms often struggle to achieve high detection accuracy in complex scenarios, particularly due to target occlusion and the presence of small objects. This paper introduces a novel pedestrian detection algorithm, YOLO-ESL, based on the YOLOv7 framework. YOLO-ESL integrates the ELAN-SA module, designed to enhance feature extraction, with the LGA module, which improves feature fusion. The ELAN-SA module optimizes the flexibility and efficiency of small object feature extraction, while the LGA module effectively integrates multi-scale features through local and global attention mechanisms. Additionally, the CIOUNMS algorithm addresses the issue of target loss in cases of high overlap, improving boundary box filtering. Evaluated on the VOC2012 pedestrian dataset, YOLO-ESL achieved an accuracy of 93.7%, surpassing the baseline model by 3.0%. Compared to existing methods, this model not only demonstrates strong performance in handling occluded and small object detection but also remarkable robustness and efficiency. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Industrial Engineering)
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22 pages, 6645 KiB  
Article
Automated Car Damage Assessment Using Computer Vision: Insurance Company Use Case
by Sergio A. Pérez-Zarate, Daniel Corzo-García, Jose L. Pro-Martín, Juan A. Álvarez-García, Miguel A. Martínez-del-Amor and David Fernández-Cabrera
Appl. Sci. 2024, 14(20), 9560; https://doi.org/10.3390/app14209560 - 19 Oct 2024
Viewed by 4242
Abstract
Automated car damage detection using computer vision techniques has been studied using several datasets, but real cases for insurance companies are usually dependent on private methods and datasets. Furthermore, there are no metrics or standardized processes that describe the situation in which the [...] Read more.
Automated car damage detection using computer vision techniques has been studied using several datasets, but real cases for insurance companies are usually dependent on private methods and datasets. Furthermore, there are no metrics or standardized processes that describe the situation in which the company analyzes the customer’s images, the models used for the inference, and the results. We perform extensive experiments to show that our proposal, an ensemble of 10 deep learning detectors based on YOLOv5, improves the state-of-the-art not only in terms of typical metrics but also in terms of inference speed, allowing scalability to thousands of instances per minute. A comparison with YOLOv8 is carried out, showing the differences between both ensembles. Furthermore, a dataset called TartesiaDS, labeled under the supervision of professional appraisers from insurance companies, is available to the community for evaluation of future proposals. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Industrial Engineering)
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25 pages, 6336 KiB  
Article
Effective Strategies for Enhancing Real-Time Weapons Detection in Industry
by Ángel Torregrosa-Domínguez, Juan A. Álvarez-García, Jose L. Salazar-González and Luis M. Soria-Morillo
Appl. Sci. 2024, 14(18), 8198; https://doi.org/10.3390/app14188198 - 12 Sep 2024
Cited by 1 | Viewed by 2414
Abstract
Gun violence is a global problem that affects communities and individuals, posing challenges to safety and well-being. The use of autonomous weapons detection systems could significantly improve security worldwide. Despite notable progress in the field of weapons detection closed-circuit television-based systems, several challenges [...] Read more.
Gun violence is a global problem that affects communities and individuals, posing challenges to safety and well-being. The use of autonomous weapons detection systems could significantly improve security worldwide. Despite notable progress in the field of weapons detection closed-circuit television-based systems, several challenges persist, including real-time detection, improved accuracy in detecting small objects, and reducing false positives. This paper, based on our extensive experience in this field and successful private company contracts, presents a detection scheme comprising two modules that enhance the performance of a renowned detector. These modules not only augment the detector’s performance but also have a low negative impact on the inference time. Additionally, a scale-matching technique is utilised to enhance the detection of weapons with a small aspect ratio. The experimental results demonstrate that the scale-matching method enhances the detection of small objects, with an improvement of +13.23 in average precision compared to the non-use of this method. Furthermore, the proposed detection scheme effectively reduces the number of false positives (a 71% reduction in the total number of false positives) of the baseline model, while maintaining a low inference time (34 frames per second on an NVIDIA GeForce RTX-3060 card with a resolution of 720 pixels) in comparison to the baseline model (47 frames per second). Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Industrial Engineering)
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Review

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64 pages, 6191 KiB  
Review
Techniques and Models for Addressing Occupational Risk Using Fuzzy Logic, Neural Networks, Machine Learning, and Genetic Algorithms: A Review and Meta-Analysis
by Chris Mitrakas, Alexandros Xanthopoulos and Dimitrios Koulouriotis
Appl. Sci. 2025, 15(4), 1909; https://doi.org/10.3390/app15041909 - 12 Feb 2025
Cited by 1 | Viewed by 1025
Abstract
This article aims to present a structured literature review that utilizes computational intelligence techniques, specifically fuzzy logic, neural networks, genetic algorithms, and machine learning, to assist in the assessment of workplace risk from human factors. The general aim is to highlight the existing [...] Read more.
This article aims to present a structured literature review that utilizes computational intelligence techniques, specifically fuzzy logic, neural networks, genetic algorithms, and machine learning, to assist in the assessment of workplace risk from human factors. The general aim is to highlight the existing literature on the subject, while the specific goal of the research is to attempt to answer research questions that emerge after the review and classification of the literature, which are aspects that have not previously been addressed. The methodology for retrieving relevant articles involved a keyword search in the Scopus database. The results from the search were filtered based on the selected criteria. The research spans a 40-year period, from 1984 to 2024. After filtering, 296 articles relevant to the topic were identified. Statistical analysis highlights fuzzy systems as the technique with the highest representation (163 articles), followed by neural networks (81 articles), with machine learning and genetic algorithms ranking next (25 and 20 articles, respectively). The main conclusions indicate that the primary sectors utilizing these techniques are industry, transportation, construction, and cross-sectoral models and techniques that are applicable to multiple occupational fields. An additional finding is the reasoning behind researchers’ preference for fuzzy systems over neural networks, primarily due to the availability or lack of accident databases. The review also highlighted gaps in the literature requiring further research. The assessment of occupational risk continues to present numerous challenges, and the future trend suggests that fuzzy systems and machine learning may be prominent. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Industrial Engineering)
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