Article
Engineering, Industrial
Mathilde Drouot, Nathalie Le Bigot, Emmanuel Bricard, Jean-Louis de Bougrenet, Vincent Nourrit
Summary: A study comparing the impact of AR-based instructions with computerized instructions on assembly tasks found that AR did not improve effectiveness and increased mental workload. AR users were also less able to detect external events, which could contribute to work accidents.
APPLIED ERGONOMICS
(2022)
Article
Engineering, Industrial
Junhao Chen, Xiaoliang Jia, Qixuan He
Summary: This study proposes a novel bi-level multi-objective genetic algorithm to solve the integrated problem of assembly line balancing and part feeding. The algorithm outperforms traditional methods in terms of approximation and computational efficiency.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Information Systems
Alejandro Chacon, Pere Ponsa, Cecilio Angulo
Summary: In human-robot collaborative assembly tasks, balancing the skills of humans and robots is crucial for maximizing productivity. Through experimental research, it has been demonstrated that operators can handle both high-demanding cognitive tasks and secondary tasks involving robots, minimizing the impact on the primary task and achieving a balance of skills.
Article
Engineering, Industrial
Xiang Sun, Shunsheng Guo, Jun Guo, Baigang Du, Zhijie Yang, Kaipu Wang
Summary: This paper proposes a multi-objective hybrid production line balancing problem and designs a Pareto-based hybrid genetic simulated annealing algorithm to solve it. The effectiveness of the algorithm is verified through numerical results by comparing with other algorithms. Moreover, managerial insights are provided through a case study.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Management
Rico Walter, Philipp Schulze, Armin Scholl
Summary: This study tackles a simple assembly line balancing problem with a focus on a smoothness index SX, and optimizes it through a branch-and-bound procedure, outperforming other methods in comprehensive computational experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Engineering, Industrial
Rico Walter, Philipp Schulze
Summary: In this paper, the effectiveness of two exact algorithms in balancing workloads on assembly lines is systematically analysed, with experiments evaluating the performance of a mathematical programming solver and a combined exact branch-and-bound procedure. The study shows the equivalence of two local lower bounding arguments and proposes enhancements in both the bound and feasibility testing.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Lue Tao, Yun Dong, Weihua Chen, Yang Yang, Lijie Su, Qingxin Guo, Gongshu Wang
Summary: This study addresses a new variant of the assembly line feeding problem in automobile manufacturing, proposing a novel mathematical model and algorithm that achieve superior cost savings, solution quality, and convergence efficiency while providing decision support for managers.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Engineering, Industrial
Xuemei Liu, Xiaolang Yang, Mingliang Lei
Summary: This study utilized uncertainty theory and complexity theory to consider uncertain demand in mixed-model assembly line balancing. By introducing scenario probability and triangular fuzzy number to describe uncertain demand, and measuring station complexity based on information entropy and fuzzy entropy, a new optimization model was established. An improved genetic algorithm was applied to solve the model, and the effectiveness of the model was verified on instances of mixed-model assembly line for automobile engines.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Engineering, Industrial
Thiago Cantos Lopes, Adalberto Sato Michels, Nadia Brauner, Leandro Magatao
Summary: This paper focuses on optimizing Mixed-model assembly lines with continuous paced line control and proposes a criterion-space method for defining the Pareto front. Comparing the Pareto fronts between cycle time and line length for paced and unpaced lines allows meaningful comparisons between line controls. An industrial case study suggests that paced lines are more efficient than unpaced lines for lower cycle time ranges.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Automation & Control Systems
Weilei Shen, Qiangqiang Jiang, Yang Yang
Summary: The paper proposes a task assignment model for U-shaped production lines with collaborative tasks, optimized by minimizing the number of workers and balancing workload to increase productivity. The new algorithm improves the balance rate of U-shaped production lines and the utilization of personnel or equipment.
ASSEMBLY AUTOMATION
(2022)
Article
Computer Science, Artificial Intelligence
Zixiang Li, Mukund Nilakantan Janardhanan, S. G. Ponnambalam
Summary: This study investigates the cost-oriented robotic assembly line balancing problem, including purchasing cost and setup time optimization, by developing a mixed-integer linear programming model. The proposed IMABC algorithm introduces new employed bee phase and scout phase to enhance exploration and exploitation.
JOURNAL OF INTELLIGENT MANUFACTURING
(2021)
Article
Engineering, Industrial
Serena Finco, Mohammed-Amine Abdous, Martina Calzavara, Daria Battini, Xavier Delorme
Summary: This paper proposes a bi-objective manual assembly line design model to avoid excessive daily vibration exposures in occupational sectors. The model minimizes total equipment costs and vibration levels by respecting ISO 5349-1 threshold values, using the epsilon-constraint approach to find the Pareto frontier. The method can solve small and medium size instances, providing safe vibration exposure levels with low additional investment.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Neurosciences
Heng Gu, He Chen, Qunli Yao, Wenbo He, Shaodi Wang, Chao Yang, Jiaxi Li, Huapeng Liu, Xiaoli Li, Xiaochuan Zhao, Guanhao Liang
Summary: The study of mental workload and the measurement methods have attracted much attention in the field of neuroergonomics. This study investigated the changes in the brain network during a simulated piloting task of different difficulty levels. The results showed that the functional brain network underwent reconfiguration during the development of mental schema. Additionally, the efficiency of the brain network was associated with mental workload, particularly with a developed mental schema.
Article
Automation & Control Systems
Riccardo Gervasi, Matteo Capponi, Luca Mastrogiacomo, Fiorenzo Franceschini
Summary: Human-Robot Collaboration (HRC) is able to improve the quality and adaptability of production processes. An experiment was conducted to investigate the effects of prolonged HRC on user experience and performance in a repetitive assembly process. The results showed that HRC reduced physical exertion, mental effort, stress, and process defects, indicating the contribution of collaborative robotics in improving ergonomics and process quality in repetitive tasks.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Agronomy
Fulai Yan, Xiaoqiang Liu, Wenqiang Bai, Junliang Fan, Fucang Zhang, Youzhen Xiang, Xianghao Hou, Shengzhao Pei, Yulong Dai, Hualiang Zeng, Ying Wang
Summary: The scarcity of water resources and low water-nitrogen use efficiency pose significant challenges to the sustainable development of sugar beet in desert climates. A field experiment was conducted to investigate the responses of drip-fertigated sugar beet to varying water and nitrogen supplies. The results showed that the optimal combination of irrigation amount and nitrogen rate was beneficial for the sustainable production of sugar beet in desert climates.
FIELD CROPS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Meng-Hui Chen, Chin-Hung Teng, Pei-Chann Chang
ADVANCED ENGINEERING INFORMATICS
(2015)
Article
Computer Science, Interdisciplinary Applications
Chia-Yu Hsu, Pei-Chann Chang, Meng-Hui Chen
COMPUTERS & INDUSTRIAL ENGINEERING
(2015)
Article
Computer Science, Information Systems
Jie Sun, Hui Li, Pei-Chann Chang, Kai-Yu He
ENTERPRISE INFORMATION SYSTEMS
(2016)
Article
Engineering, Industrial
Alok Choudhary, Ravi Suman, Vijaya Dixit, M. K. Tiwari, Kiran Jude Fernandes, Pei-Chann Chang
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2015)
Editorial Material
Engineering, Industrial
Manoj Kumar Tiwari, Pei-Chann Chang, Alok Choudhary
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2015)
Article
Engineering, Industrial
Chia-Yu Hsu, Chin-Sheng Yang, Liang-Chih Yu, Chi-Fang Lin, Hsiu-Hsen Yao, Duan-Yu Chen, K. Robert Lai, Pei-Chann Chang
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2015)
Article
Engineering, Industrial
Anurag Tiwari, Pei-Chann Chang
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2015)
Article
Computer Science, Artificial Intelligence
Jheng-Long Wu, Liang-Chih Yu, Pei-Chann Chang
APPLIED SOFT COMPUTING
(2014)
Article
Computer Science, Artificial Intelligence
T. W. Liao, P. C. Chang, R. J. Kuo, C. -J. Liao
APPLIED SOFT COMPUTING
(2014)
Article
Engineering, Industrial
Anurag Tiwari, Pei-Chann Chang, M. K. Tiwari, Nevin John Kollanoor
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2015)
Article
Computer Science, Artificial Intelligence
Pei-Chann Chang, Meng-Hui Chen
Article
Computer Science, Artificial Intelligence
Pei-Chann Chang, Jheng-Long Wu
Proceedings Paper
Computer Science, Artificial Intelligence
Julia Tzu-Ya Weng, Jyun-Jie Lin, Yi-Cheng Chen, Pei-Chann Chang
TRENDS AND APPLICATIONS IN KNOWLEDGE DISCOVERY AND DATA MINING
(2014)
Review
Business
Chin-Sheng Yang, Cheng-Hsiung Chen, Pei-Chann Chang
INFORMATION SYSTEMS AND E-BUSINESS MANAGEMENT
(2015)
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
