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Artificial Intelligence in Industrial Engineering

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

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 27969

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Special Issue Editors

Prof. Dr. Kuo-Ching Ying

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Guest Editor

Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: scheduling; industrial engineering

Prof. Dr. Shih-Wei Lin

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Guest Editor

Department of Information Management, Chang Gung University, Taoyuan 33302, Taiwan
Interests: applications of meta-heuristics; big data; data mining; scheduling; vehicle routing problems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chen-Yang Cheng

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Guest Editor

Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: computer-integrated manufacturing; production scheduling system; mobile computing

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is one of the core drivers of industrial development and has produced dramatic paradigm shifts in Industrial Engineering theories and applications. Benefitting from the rapid progress of advanced technologies and high-performance computing, AI technology has experienced significant development and has attracted the attention of researchers in the Industrial Engineering community over the last decades. This Special Issue is devoted to the latest and future theoretical innovations of AI in Industrial Engineering. In this Special Issue, we also invite case studies, comprehensive reviews, and survey papers to provide a valuable reference for researchers and practitioners on the challenges, opportunities, and future development of AI in Industrial Engineering.

Prof. Dr. Kuo-Ching Ying
Prof. Dr. Shih-Wei Lin
Prof. Dr. Chen-Yang Cheng
Guest Editors

Manuscript Submission Information

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Keywords

artificial intelligence
deep learning
big data analysis
data mining
artificial neural networks
industrial engineering
ergonomics and design
production system and smart manufacturing
operations research and decision science
service systems and technology management

Published Papers (10 papers)

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Research

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24 pages, 2041 KiB

Open AccessArticle

Emotion Recognition Method for Call/Contact Centre Systems

by Mirosław Płaza, Robert Kazała, Zbigniew Koruba, Marcin Kozłowski, Małgorzata Lucińska, Kamil Sitek and Jarosław Spyrka

Appl. Sci. 2022, 12(21), 10951; https://doi.org/10.3390/app122110951 - 28 Oct 2022

Cited by 6 | Viewed by 2917

Abstract

Nowadays, one of the important aspects of research on call/contact centre (CC) systems is how to automate their operations. Process automation is influenced by the continuous development in the implementation of virtual assistants. The effectiveness of virtual assistants depends on numerous factors. One of the most important is correctly recognizing the intent of clients conversing with the machine. Recognizing intentions is not an easy process, as often the client’s actual intentions can only be correctly identified after considering the client’s emotional state. When it comes to human–machine communication, the ability of a virtual assistant to recognize the client’s emotional state would greatly improve its effectiveness. This paper proposes a new method for recognizing interlocutors’ emotions dedicated directly to contact centre systems. The developed method provides opportunities to determine emotional states in text and voice channels. It provides opportunities to explore both the client’s and the agent’s emotional states. Information about agents’ emotions can be used to build their behavioural profiles, which is also applicable in contact centres. In addition, the paper explored the possibility of emotion assessment based on automatic transcriptions of recordings, which also positively affected emotion recognition performance in the voice channel. The research used actual conversations that took place during the operation of a large, commercial contact centre. The proposed solution makes it possible to recognize the emotions of customers contacting the hotline and agents handling these calls. Using this information in practical applications can increase the efficiency of agents’ work, efficiency of bots used in CC and increase customer satisfaction. Full article

(This article belongs to the Special Issue Artificial Intelligence in Industrial Engineering)

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21 pages, 704 KiB

Open AccessArticle

BioShare: An Open Framework for Trusted Biometric Authentication under User Control

by Quan Sun, Jie Wu and Wenhai Yu

Appl. Sci. 2022, 12(21), 10782; https://doi.org/10.3390/app122110782 - 25 Oct 2022

Viewed by 1461

Abstract

Generally, biometric authentication is conducted either by mobile terminals in local-processing mode or by public servers in centralized-processing mode. In the former mode, each user has full control of his/her biometric data, but the authentication service is restricted to local mobile apps. In the latter mode, the authentication service can be opened up to network applications, but the owners have no control of their private data. It has become a difficult problem for biometric applications to provide open and trusted authentication services under user control. Existing approaches address these concerns in ad-hoc ways. In this work, we propose BioShare, a framework that provides trusted biometric authentication services to network applications while giving users full control of their biometric data. Our framework is designed around three key principles: each user has full control of his/her biometric data; biometric data is stored and processed in trusted environments to prevent privacy leaks; and the open biometric-authentication service is efficiently provided to network applications. We describe our current design and sample implementation, and illustrate how it provides an open face-recognition service with standard interfaces, combines terminal trusted environments with server enclaves, and enables each user to control his/her biometric data efficiently. Finally, we analyze the security of the framework and measure the performance of the implementation. Full article

(This article belongs to the Special Issue Artificial Intelligence in Industrial Engineering)

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16 pages, 12058 KiB

Open AccessArticle

Remote Sensing Image Segmentation of Mariculture Cage Using Ensemble Learning Strategy

by Lewei Xu, Zhuhua Hu, Chong Zhang and Wei Wu

Appl. Sci. 2022, 12(16), 8234; https://doi.org/10.3390/app12168234 - 17 Aug 2022

Viewed by 1265

Abstract

In harbour areas, the irrational layout and high density of mariculture cages can lead to a dramatic deterioration of the culture’s ecology. Therefore, it is important to analyze and regulate the distribution of cages using intelligent analysis based on deep learning. We propose a remote sensing image segmentation method based on the Swin Transformer and ensemble learning strategy. Firstly, we collect multiple remote sensing images of cages and annotate them, while using data expansion techniques to construct a remote sensing image dataset of mariculture cages. Secondly, the Swin Transformer is used as the backbone network to extract the remote sensing image features of cages. A strategy of alternating the local attention module and the global attention module is used for model training, which has the benefit of reducing the attention computation while exchanging global information. Then, the ensemble learning strategy is used to improve the accuracy of remote sensing cage segmentation. We carry out quantitative and qualitative analyses of remote sensing image segmentation of cages at the ports of Li’an, Xincun and Potou in Hainan Province, China. The results show that our proposed segmentation scheme has significant performance improvement compared to other models. In particular, the mIoU reaches 82.34% and pixel accuracy reaches 99.71%. Full article

(This article belongs to the Special Issue Artificial Intelligence in Industrial Engineering)

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16 pages, 487 KiB

Open AccessArticle

Solving Aggregate Production Planning Problems: An Extended TOPSIS Approach

by Vincent F. Yu, Hsuan-Chih Kao, Fu-Yuan Chiang and Shih-Wei Lin

Appl. Sci. 2022, 12(14), 6945; https://doi.org/10.3390/app12146945 - 8 Jul 2022

Cited by 5 | Viewed by 2590

Abstract

Aggregate production planning (APP) was developed for solving the problem of determining production, inventory, and workforce levels to meet fluctuating demand requirements over a planning horizon. In this work, multiple objectives were considered to determine the most effective means of satisfying forecasted demand by adjusting production rates, hiring and layoffs, inventory levels, overtime work, back orders, and other controllable variables. An extended technique for order preference via the similarity ideal solution (TOPSIS) approach was developed. It was formulated to solve this complicated, multi-objective APP decision problem. Compromise (ideal solution) control minimized the measure of distance, providing which of the closest solutions has the shortest distance from a positive ideal solution (PIS) and the longest distance from a negative ideal solution (NIS). The proposed method can transform multiple objectives into two objectives. The bi-objective problem can then be solved by balancing satisfaction using a max–min operator for resolving the conflict between the new criteria based on PIS and NIS. Finally, an application example demonstrated the proposed model’s applicability to practical APP decision problems. Full article

(This article belongs to the Special Issue Artificial Intelligence in Industrial Engineering)

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13 pages, 1064 KiB

Open AccessArticle

Using the ISM Method to Analyze the Relationships between Various Contractor Prequalification Criteria

by Vincent F. Yu, Fu-Yuan Chiang, Thi Huynh Anh Le and Shih-Wei Lin

Appl. Sci. 2022, 12(8), 3726; https://doi.org/10.3390/app12083726 - 7 Apr 2022

Cited by 4 | Viewed by 2432

Abstract

Construction contractors significantly contribute to the progress and success of projects. Contractor prequalification grants tendering rights only to competent contractors. The bidding process is one of the most critical phases in the construction industry. The project leader must assess the general, technical, and financial information of the contractors to prepare an accurate proposal and select the best contractor. In this study, contractor prequalification is considered, along with the complex relationships between various criteria. ISM is a computational method that involves a qualitative and interpretive approach to solving complex problems based on structural mapping of the connections between attributes and their subsequent transformation into a multilevel structural model. Using ISM, we establish a seven-level hierarchy for various contractor prequalification criteria, which are then grouped into four clusters based on their driving power and dependence power. The result of this study shows that ISM can be used to rank complex contractor prequalification criteria and help managers select a qualified contractor in the construction project bidding process during the strategic planning phase. Full article

(This article belongs to the Special Issue Artificial Intelligence in Industrial Engineering)

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16 pages, 4538 KiB

Open AccessArticle

Applying Deep Learning to Construct a Defect Detection System for Ceramic Substrates

by Chien-Yi Huang, I-Chen Lin and Yuan-Lien Liu

Appl. Sci. 2022, 12(5), 2269; https://doi.org/10.3390/app12052269 - 22 Feb 2022

Cited by 14 | Viewed by 2510

Abstract

Under the emerging topic of machine vision technology replacing manual examination, automatic optical inspection (AOI) technology has been adopted for the detection of defects in semi-finished/finished products and is widely used for the defect detection of printed circuit boards (PCB) in electronic industries where surface mount technology (SMT) is applied. In order to convert images from gray-scale to binary in the PCB process, a strict threshold value was set for AOI to prevent ‘escapes’, but this can lead to serious false alarm because of unwanted noises. Therefore, they tend to set up a Noise-Removal procedure after AOI, which increases the computational cost. By applying deep learning to circuit images of the ceramic substrates in AOI, this paper aimed to construct an automatic defect detection system that could also identify the categories as well as the locations of defects. This study proposed and evaluated three models with integrated structures: ResNeXt+YOLO v3, Inception v3+YOLO v3, and YOLO v3. The outcomes indicate that the defect detection system built on ResNeXt+YOLO v3 could most effectively detect standard images that had been misidentified as defects by AOI, categorize genuine defects, and find their location. The proposed method could not only increase the inspection accuracy to 99.2%, but also help decrease the cost of human resources generated by manual re-examination. Full article

(This article belongs to the Special Issue Artificial Intelligence in Industrial Engineering)

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17 pages, 637 KiB

Open AccessArticle

Application of Deep Learning to Construct Breast Cancer Diagnosis Model

by Rong-Ho Lin, Benjamin Kofi Kujabi, Chun-Ling Chuang, Ching-Shun Lin and Chun-Jen Chiu

Appl. Sci. 2022, 12(4), 1957; https://doi.org/10.3390/app12041957 - 13 Feb 2022

Cited by 6 | Viewed by 2349

Abstract

(1) Background: According to Taiwan’s ministry of health statistics, the rate of breast cancer in women is increasing annually. Each year, more than 10,000 women suffer from breast cancer, and over 2000 die of the disease. The mortality rate is annually increasing, but if breast cancer tumors are detected earlier, and appropriate treatment is provided immediately, the survival rate of patients will increase enormously. (2) Methods: This research aimed to develop a stepwise breast cancer model architecture to improve diagnostic accuracy and reduce the misdiagnosis rate of breast cancer. In the first stage, a breast cancer risk factor dataset was utilized. After pre-processing, Artificial Neural Network (ANN) and the support vector machine (SVM) were applied to the dataset to classify breast cancer tumors and compare their performances. The ANN achieved 76.6% classification accuracy, and the SVM using radial functions achieved the best classification accuracy of 91.6%. Therefore, SVM was utilized in the determination of results concerning the relevant breast cancer risk factors. In the second stage, we trained AlexNet, ResNet101, and InceptionV3 networks using transfer learning. The networks were studied using Adaptive Moment Estimation (ADAM) and Stochastic Gradient Descent with Momentum (SGDM) based optimization algorithm to diagnose benign and malignant tumors, and the results were evaluated; (3) Results: According to the results, AlexNet obtained 81.16%, ResNet101 85.51%, and InceptionV3 achieved a remarkable accuracy of 91.3%. The results of the three models were utilized in establishing a voting combination, and the soft-voting method was applied to average the prediction result for which a test accuracy of 94.20% was obtained; (4) Conclusions: Despite the small number of images in this study, the accuracy is higher compared to other literature. The proposed method has demonstrated the need for an additional productive tool in clinical settings when radiologists are evaluating mammography images of patients. Full article

(This article belongs to the Special Issue Artificial Intelligence in Industrial Engineering)

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13 pages, 3651 KiB

Open AccessArticle

Cube of Space Sampling for 3D Model Retrieval

by Zong-Yao Chen, Chih-Fong Tsai and Wei-Chao Lin

Appl. Sci. 2021, 11(23), 11142; https://doi.org/10.3390/app112311142 - 24 Nov 2021

Cited by 1 | Viewed by 1392

Abstract

Since the number of 3D models is rapidly increasing, extracting better feature descriptors to represent 3D models is very challenging for effective 3D model retrieval. There are some problems in existing 3D model representation approaches. For example, many of them focus on the direct extraction of features or transforming 3D models into 2D images for feature extraction, which cannot effectively represent 3D models. In this paper, we propose a novel 3D model feature representation method that is a kind of voxelization method. It is based on the space-based concept, namely CSS (Cube of Space Sampling). The CSS method uses cube space 3D model sampling to extract global and local features of 3D models. The experiments using the ESB dataset show that the proposed method to extract the voxel-based features can provide better classification accuracy than SVM and comparable retrieval results using the state-of-the-art 3D model feature representation method. Full article

(This article belongs to the Special Issue Artificial Intelligence in Industrial Engineering)

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14 pages, 1336 KiB

Open AccessArticle

Quantifying Perceived Facial Asymmetry to Enhance Physician–Patient Communications

by Shu-Yen Wan, Pei-Ying Tsai and Lun-Jou Lo

Appl. Sci. 2021, 11(18), 8398; https://doi.org/10.3390/app11188398 - 10 Sep 2021

Cited by 1 | Viewed by 1467

Abstract

In cosmetic surgery, bridging the anticipation gap between the patients and the physicians can be challenging if there lacks objective and transparent information exchange during the decision-making and surgical process. Among all factors, facial symmetry is the most important for assessing facial attractiveness. The aim of this work is to promote communications between the two parties by providing a quadruple of quantitative measurements: overall asymmetry index (oAI), asymmetry vector, classification, and confidence vector, using an artificial neural network classifier to model people’s perception acquired from visual questionnaires concerning facial asymmetry. The questionnaire results exhibit a Cronbach’s Alpha value of 0.94 and categorize the respondents’ perception of each stimulus face into perceived normal (PN), perceived asymmetrically normal (PAN), and perceived abnormal (PA) categories. The trained classifier yields an overall root mean squared error < 0.01, and its result shows that the oAI is, in general, proportional to the degree of perceived asymmetry. However, there exist faces that are difficult to classify as either PN or PAN or either PAN or PA with competing confidence values. In such cases, oAI alone is not sufficient to articulate facial asymmetry. Assisting surgeon–patient conversations with the proposed asymmetry quadruple is advised to avoid or to mitigate potential medical disputes. Full article

(This article belongs to the Special Issue Artificial Intelligence in Industrial Engineering)

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Review

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19 pages, 6115 KiB

Open AccessReview

Artificial Intelligence in the Construction Industry: Main Development Trajectories and Future Outlook

by Hsiu-Ping Chen and Kuo-Ching Ying

Appl. Sci. 2022, 12(12), 5832; https://doi.org/10.3390/app12125832 - 8 Jun 2022

Cited by 9 | Viewed by 5196

Abstract

Recent developments in artificial intelligence (AI) have greatly influenced progress in various industries. While the complexity of the construction industry makes it an essential and potential area for AI applications, there has been no analysis conducted on the main development paths for the applications of AI technologies in the construction industry. To fill this gap, this study applied the main path analysis method to investigate the evolution of AI technologies in the construction industry. This study analyzed 587 articles published between 1989 and 2021 to identify the main development trajectories of AI technologies in the construction industry and to suggest possible directions in which AI technologies can be further applied to promote progress in architectural design, engineering design, and construction services. Full article

(This article belongs to the Special Issue Artificial Intelligence in Industrial Engineering)

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