Article
Engineering, Mechanical
Ru Wang, Jelena Milisavljevic-Syed, Lin Guo, Yu Huang, Guoxin Wang
Summary: This paper discusses the demand for decision support in complex engineered systems design in the era of Industry 4.0, proposing an architecture for a cloud-based decision support system that manages complexity, uncertainty, and knowledge to provide systematic design guidance. Two design case studies validate the effectiveness of this system.
JOURNAL OF MECHANICAL DESIGN
(2021)
Article
Business
Alberto F. De Toni, Elena Pessot
Summary: Understanding and properly facing the increasing complexity of projects is crucial for success in project-based organisations. This paper provides insights into the interplay between project complexity and organisational learning, highlighting the need for different organisational learning processes based on the dimensions of complexity identified in literature. The study conducted in a leading shipbuilding company reveals the importance of specific behaviors and approaches for organizational learning in addressing complexity issues within projects.
JOURNAL OF BUSINESS RESEARCH
(2021)
Article
Chemistry, Multidisciplinary
Fabian Dworschak, Patricia Kuegler, Benjamin Schleich, Sandro Wartzack
Summary: Mass customization aims to meet individual requirements and attract and retain customers in design industry. Design automation, based on the reuse of product and process knowledge, has provided new opportunities for designing customized products at high speed. This approach proposes a novel knowledge representation method using semantic enrichment in CAD environments to enable design automation for mass customization, demonstrated through a bike crank customization process.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Interdisciplinary Applications
C. McPhail, H. R. Maier, S. Westra, L. van der Linden, J. H. Kwakkel
Summary: In order to assist decision making about environmental systems under deep uncertainty, researchers have introduced a generic guidance framework and software package to help identify the most robust decision alternatives. This tackles the difficulty of choosing between robustness metrics and scenarios, providing a consistent and easy-to-use approach to quantify system robustness and make robust decisions.
ENVIRONMENTAL MODELLING & SOFTWARE
(2021)
Article
Computer Science, Artificial Intelligence
Dragos Constantin Popescu, Ioan Dumitrache
Summary: This paper introduces a new modeling formalism called Hybrid Logic-Algebraic Relational Modeling, which combines logic, probabilities, numerical information, and network representations to describe the behavior, facts, and workflows in complex systems. The logical and probabilistic inference applied to the modeling environment provides valuable knowledge to designers and decision-makers to manage the complex systems.
INFORMATION FUSION
(2023)
Article
Geosciences, Multidisciplinary
Rui Hugman, John Doherty
Summary: The primary tasks of decision-support modelling are to quantify and reduce uncertainties in model predictions. This requires assimilating information from site measurements and expert knowledge. The design process involves a compromise between model complexity and parameter assignment.
FRONTIERS IN EARTH SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Huseyin Erol, Irem Dikmen, Guzide Atasoy, M. Talat Birgonul
Summary: Risk, complexity, and uncertainty are inherent components of megaprojects. This study aims to develop a holistic risk quantification approach using an ANP model to assist in assessing risks more realistically in projects.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Geosciences, Multidisciplinary
Jared D. Smith, Laurence Lin, Julianne D. Quinn, Lawrence E. Band
Summary: This paper provides guidance on evaluating model parameter uncertainty for decision-making problems. The authors use global sensitivity analysis to screen parameters and evaluate the appropriateness of using multipliers to adjust parameter values. The results suggest that decision-relevant metrics should be used for parameter screening.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2022)
Article
Computer Science, Theory & Methods
Mieszko Makuch, Maciej Malawski, Joanna Kocot, Tomasz Szepieniec
Summary: This paper presents a new workflow representation model based on directory trees. It demonstrates the implementation of the model in the EPISODES Platform and highlights its suitability for certain types of applications. The model allows efficient representation of workflows in a file system tree, enabling encapsulation, modularity, and support for interactive composition processes.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Engineering, Industrial
Terje Aven
Summary: The precautionary principle is a controversial policy for risk and safety concerns. It has been criticized for being paralyzing, unscientific, and promoting irrational fear, but also supported by some arguments. This paper aims to contribute to the discussion by investigating the principle from the perspective of contemporary risk and safety science. The paper concludes that the precautionary principle is relevant only in situations with considerable scientific uncertainties and risks, and when properly understood and implemented, it can align with decision analysis and other scientific methods.
Article
Engineering, Chemical
Bastian Bruns, Felix Herrmann, Marcus Gruenewald, Julia Riese
Summary: This study introduces a model-based approach that utilizes dynamic optimization for designing flexible process equipment to ensure feasibility during both steady state and dynamic transitions. The approach is applied to two case studies in demand-side management backgrounds, demonstrating its versatility and applicability while revealing the impact of different parameters on equipment flexibility.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2021)
Article
Business
Niloofar Nobari, Ali Mobini Dehkordi
Summary: This paper investigates the decision-making knowledge required by digital tech-enabled corporations and startups to co-create innovative digital outputs, and proposes an innovation intelligence framework. The study reveals both plannable and unplannable organizational and group level capabilities, and examines their influence on the co-creation process and collective intelligence. The findings contribute to bridging the gap between expectations and achievements in innovation co-creation.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2023)
Article
Management
Su Dong, Monica S. Johar, Ram L. Kumar
Summary: This paper focuses on hybrid workflows of private platform-enabled marketplaces, utilizing the expertise of in-house and on-demand workers for complex knowledge-intensive tasks. The primary contributions involve identifying and modeling the application of operations research in the new economy, proving its complexity, and proposing a high-quality heuristic. Mathematical programming models are formulated to consider uncertainty in task, worker, and on-demand marketplace characteristics. A simplified queuing theory-based model characterizes the OD marketplace structure, informing workforce size, composition, and pricing decisions. A Tabu-based One Period Look Ahead heuristic is proposed based on the simplified queuing theory-based model. Heuristic performance is evaluated using an upper bound and experiments based on a cyber security use case, illustrating the hybrid workforce and workflows for different market conditions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Management
Wenju Niu, Houcai Shen
Summary: This paper investigates whether manufacturers with differentiated absorptive capacity will invest in uncertain process innovation under knowledge spillovers. Game-theoretic models with bilateral investment, unilateral investment, and no-investment are developed to analyze the equilibrium outcomes. The study reveals that the intensity of knowledge spillovers plays a crucial role in manufacturers' investment decisions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Business
Constantin Bratianu, Elena-Madalina Vatamanescu, Sorin Anagnoste, Gandolfo Dominici
Summary: This study explores the influences of different knowledge fields and their dynamics on decision-making processes, emphasizing the significance of integrating emotional and spiritual knowledge for making good managerial decisions. The research highlights the importance of knowledge dynamics in decision-making within real-life business environments, surpassing the influence of rational knowledge.
MANAGEMENT DECISION
(2021)
Article
Computer Science, Artificial Intelligence
Fangyu Chen, Yongchang Wei, Hongchang Ji, Gangyan Xu
Summary: This paper introduces a dual-layer network analytical framework for evaluating standard systems in construction safety management and validates its effectiveness through a case study. The research findings suggest that key standards often encompass a wider array of risks, providing suggestions for revising construction standards.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Minghao Li, Qiubing Ren, Mingchao Li, Ting Kong, Heng Li, Huijing Tian, Shiyuan Liu
Summary: This study proposes a method using digital twin technology to construct a collision early warning system for marine piling. The system utilizes a five-dimensional model and four independently maintainable development modules to maximize its effectiveness. The pile positioning algorithm and collision early warning algorithm are capable of providing warnings for complex pile groups.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Seokhyun Ryu, Sungjoo Lee
Summary: This study proposes the use of patent information to develop a robust technology tree and applies it to the furniture manufacturing process. Through methods such as clustering analysis, semantic analysis, and association-rule mining, technological attributes and their relationships are extracted and analyzed. This approach provides meaningful information to improve the understanding of a target technology and supports research and development planning.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Shuai Ma, Kechen Song, Menghui Niu, Hongkun Tian, Yanyan Wang, Yunhui Yan
Summary: This paper proposes a feature-based domain disentanglement and randomization (FDDR) framework to improve the generalization of deep models in unseen datasets. The framework successfully addresses the appearance difference issue between training and test images by decomposing the defect image into domain-invariant structural features and domain-specific style features. It also utilizes randomly generated samples for training to further expand the training sample.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Fang Xu, Tianyu Zhou, Hengxu You, Jing Du
Summary: This study explores the impact of AR-based egocentric perspectives on indoor wayfinding performance. The results reveal that participants using the egocentric perspective demonstrate improved efficiency, reduced cognitive load, and enhanced spatial awareness in indoor navigation tasks.
ADVANCED ENGINEERING INFORMATICS
(2024)
Review
Computer Science, Artificial Intelligence
Yujie Lu, Shuo Wang, Sensen Fan, Jiahui Lu, Peixian Li, Pingbo Tang
Summary: Image-based 3D reconstruction plays a crucial role in civil engineering by bridging the gap between physical objects and as-built models. This study provides a comprehensive summary of the field over the past decade, highlighting its interdisciplinary nature and integration of various technologies such as photogrammetry, 3D point cloud analysis, semantic segmentation, and deep learning. The proposed 3D reconstruction knowledge framework outlines the essential elements, use phases, and reconstruction scales, and identifies eight future research directions. This review is valuable for scholars interested in the current state and future trends of image-based 3D reconstruction in civil engineering, particularly in relation to deep learning methods.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Hang Zhang, Wenhu Wang, Shusheng Zhang, Yajun Zhang, Jingtao Zhou, Zhen Wang, Bo Huang, Rui Huang
Summary: This paper presents a novel framework for segmenting intersecting machining features using deep reinforcement learning. The framework enhances the effectiveness of intersecting machining feature segmentation by leveraging the robust feature representation, decision-making, and automatic learning capabilities of deep reinforcement learning. Experimental results demonstrate that the proposed approach successfully addresses some existing challenges faced by several state-of-the-art methods in intersecting machining feature segmentation.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Chao Zhao, Weiming Shen
Summary: This paper proposes a semantic-discriminative augmentation-driven network for imbalanced domain generalization fault diagnosis, which enhances the model's generalization capabilities through synthesizing reliable samples and optimizing representations.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Ching-Chih Chang, Teng-Wen Chang, Hsin-Yi Huang, Shih-Ting Tsai
Summary: Ideation is the process of generating ideas through exploring visual and semantic stimuli for creative problem-solving. This process often requires changes in user goals and insights. Using pre-designed content and semantic-visual concepts for ideation can introduce uncertainty. An adaptive workflow is proposed in this study that involves extracting and summarizing semantic-visual features, using clusters of adapted information for multi-label classification, and constructing a design exploration model with visualization and exploration.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Zhen Wang, Shusheng Zhang, Hang Zhang, Yajun Zhang, Jiachen Liang, Rui Huang, Bo Huang
Summary: This research proposes a novel approach for machining feature process planning using graph convolutional neural networks. By representing part information with attribute graphs and constructing a learning model, the proposed method achieves higher accuracy and resolves current limitations in machining feature process planning.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Hong-Wei Xu, Wei Qin, Jin-Hua Hu, Yan-Ning Sun, You -Long Lv, Jie Zhang
Summary: Wafer fabrication is a complex manufacturing system, where understanding the correlation between parameters is crucial for identifying the cause of wafer defects. This study proposes a Copula network deconvolution-based framework for separating direct correlations, which involves constructing a complex network correlation diagram and designing a nonlinear correlation metric model. The proposed method enables explainable fault detection by identifying direct correlations.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Yida Hong, Wenqiang Li, Chuanxiao Li, Hai Xiang, Sitong Ling
Summary: An adaptive push method based on feature transfer is proposed to address sparsity and cold start issues in product intelligent design. By constructing a collaborative filtering algorithm model and transforming the rating model, the method successfully alleviates data sparsity and cold start problems.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Hairui Fang, Jialin An, Bo Sun, Dongsheng Chen, Jingyu Bai, Han Liu, Jiawei Xiang, Wenjie Bai, Dong Wang, Siyuan Fan, Chuanfei Hu, Fir Dunkin, Yingjie Wu
Summary: This work proposes a model for real-time fault diagnosis and distance localization on edge computing devices, achieving lightweight design and high accuracy in complex environments. It also demonstrates a high frame rate on edge computing devices, providing a novel solution for industrial practice.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Yujun Jiao, Xukai Zhai, Luyajing Peng, Junkai Liu, Yang Liang, Zhishuai Yin
Summary: This paper proposes a digital twin-based motion forecasting framework that predicts the future trajectories of workers on construction sites, accurately predicting workers' motions in potential risk scenarios.
ADVANCED ENGINEERING INFORMATICS
(2024)
Article
Computer Science, Artificial Intelligence
Ling-Zhe Zhang, Xiang-Dong Huang, Yan-Kai Wang, Jia-Lin Qiao, Shao-Xu Song, Jian-Min Wang
Summary: Time-series DBMSs based on the LSM-tree have been widely applied in various scenarios. The characteristics of time-series data workload pose challenges to efficient queries. To address issues like query latency and inaccurate range, we propose a novel compaction algorithm called Time-Tiered Compaction.
ADVANCED ENGINEERING INFORMATICS
(2024)