Journal Information
Advanced Engineering Informatics (AEI)
http://www.journals.elsevier.com/advanced-engineering-informatics/
Impact Factor:
5.603
Publisher:
Elsevier
ISSN:
1474-0346
Viewed:
16042
Tracked:
14
Call For Papers
Advanced computing methods and related technologies are changing the way engineers interact with the information infrastructure. Explicit knowledge representation formalisms and new reasoning techniques are no longer the sole territory of computer science. For knowledge-intensive tasks in engineering, a new philosophy and body of knowledge called Engineering Informatics is emerging.

Advanced Engineering Informatics solicits research papers with particular emphases both on 'knowledge' and 'engineering applications'. As an international Journal, original papers typically:

• Report progress in the engineering discipline of applying methods of engineering informatics.
• Have engineering relevance and help provide the scientific base to make engineering decision-making more reliable, spontaneous and creative.
• Contain novel research that demonstrates the science of supporting knowledge-intensive engineering tasks.
• Validate the generality, power and scalability of new methods through vigorous evaluation, preferably both qualitatively and quantitatively.

In addition, the Journal welcomes high quality review articles that summarise, compare, and evaluate methodologies and representations that are proposed for the field of engineering informatics. Similarly, summaries and comparisons of full-scale applications are welcomed, particularly those where scientific shortcomings have hindered success. Typically, such papers have expanded literature reviews and discussion of findings that reflect mastery of the current body of knowledge and propose novel additions to contemporary research.

Papers missing explicit representation and use of knowledge, such as those describing soft computing techniques, mathematical optimization methods, pattern recognition techniques, and numerical computation methods, do not normally qualify for publication in the Journal. Papers must illustrate contributions using examples of automating and supporting knowledge intensive tasks in artifacts-centered engineering fields such as mechanical, manufacturing, architecture, civil, electrical, transportation, environmental, and chemical engineering. Papers that report application of an established method to a new engineering subdomain will qualify only if they convincingly demonstrate noteworthy new power, generality or scalability in comparison with previously reported validation results. Finally, papers that discuss software engineering issues only are not in the scope of this journal.
Last updated by Dou Sun in 2022-01-29
Special Issues
Special Issue on Industrial Knowledge Graph-enabled Cognitive Intelligence-Driven Mass Personalization
Submission Date: 2022-07-31

Mass Personalization is a prevailing business trend of offering bespoke products and services for each individual customer, manufactured and delivered with mass production efficiency. Facilitated by the cutting-edge information and communication technologies involved in the digital transformation, the high volume, velocity, variety, veracity, and value (5V) big data and industrial knowledge assets generated in mass personalization can be exploited to create value and continuously meet the dynamic customer requirements [1,2]. Industrial Knowledge Graph (IKG), a cost-effective tool for organizing and managing these massive heterogeneous virtual assets, has demonstrated promising prospects in manifold industrial business scenarios [3,4]. Evolved from the ordinary knowledge graph, IKG strengthens the capability to deal with the domain-specific time-series data stream and synthesize multi-aspect stakeholder's empirical knowledge, which further enables the whole production system to timely and correctly cognize, interpret, and respond to humans’ requirements, behaviors, and instructions (i.e., cognitive intelligence). In this context, an IKG-based, cognitive intelligence-driven paradigm for mass personalization is foreseeable, of which the stored knowledge can flow smoothly through diverse and dispersed products/services, industrial information systems, and stakeholders to satisfy individual customer requirements and innovate engineering solutions, via rational or perceptual-based cognitive computing methods [5,6]. The human-machine mutual trust is also established in this paradigm, as abundant “know-why” knowledge can be provided for automatically generating “know-how” decisions, which bridges the semantic gap between artificial intelligence and human intelligence [7,8]. Nevertheless, there still lies a big gap to achieve this paradigm. Even though IKGs have been gradually recognized as the core for the next-generation industrial management information systems [9], the majority of practitioners only treat them as an updated medium for passively providing necessary industrial information, while neglecting their values in proactively predicting potential relations [10]. Moreover, due to the high requirements on the correctness and reliability of knowledge extracting, fusing, reasoning, and managing in industrial business scenarios, there is a lack of successful practices of integrating IKGs with the workflows and co-creating business values together with humans [11]. To this end, as an emerging and promising research topic, this Special Issue is dedicated to present the state-of-the-art and methodologies, tools, systems, and practical applications to enable the readiness and realization of IKG-enabled cognitive mass personalization. The topics of the Special Issue include, but are not limited to the following ones: • Review of IKG in manufacturing and production • IKG-based models for cognitive mass personalization • Graph and hypergraph-embedding theories and industrial applications • IKG-aided smart product-service systems/ecosystems design & development • IKG-enabled requirement management and concept evaluation • IKG-enabled explainable engineering solution recommendation and decision-making • IKG-enabled production planning and maintenance scheduling • IKG-enabled process modeling, simulation, and control • IKG-enabled engineering and project management • Semantic-based infrastructure underlying human-machine collaboration • Human-IKG knowledge synthesis and co-evolvement • IKG-supported implementations for Knowledge-as-a-Service • IKG-based integration platform for cognitive mass personalization • Case studies on cognitive mass personalization Proposed Schedule - Submission open: 01 Dec 2021 - Paper submission deadline: 31 Jul 2022 - First round review results: 31 Oct 2022 - Second round review results: 31 Jan 2023 - Notification of final decision: 28 Feb 2023 Guest editors: Dr. Xinyu Li (Managing Guest Editor)College of Mechanical Engineering, Donghua University, China E-mail: lixinyu@dhu.edu.cn Dr. Pai Zheng Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China Email: Pai.zheng@polyu.edu.hk Dr. Zhenghui Sha J. Mike Walker Department of Mechanical Engineering, The University of Texas at Austin, US E-mail: zsha@austin.utexas.edu Dr. Dazhong Wu Department of Mechanical and Aerospace Engineering, University of Central Florida, US E-mail: dazhong.wu@ucf.edu Dr. Ying Liu Department of Mechanical Engineering at the School of Engineering, Cardiff University, UK E-mail: Liuy81@cardiff.ac.uk
Last updated by Dou Sun in 2022-01-29
Special Issue on Transdisciplinary engineering research and practices focusing on digital transformations of complex systems
Submission Date: 2023-03-31

Advanced engineering informatics calls for manuscript submissions for consideration in the Special Issue featuring “Transdisciplinary engineering research and practices focusing on digital transformations of complex systems.” Digital transformation (DT) is the convergence of digital technology into various areas to bring fundamental changes, enhance critical system performance, and improve users’ or stakeholders’ satisfaction. The four essential elements of DT are (1) target entity (i.e., the organization that adopts DT); (2) scope and focus of the transformation; (3) technology adoption and manners; and (4) contexts and benefit goals of the expected change (Vial, 2019; Lee et al., 2021). Moreover, DT is the strategic adoption of digital intelligence, such as the deployment of artificial intelligence (AI), deep learning, big data analytics, cyber-physical systems (CPS), digital twins, cloud computing, edge computing, and immersive technologies (VR/AR/MR). DT for any target entity will “trigger significant changes and effectiveness to its external-market and internal-organization strategies and tactics through combinations of information, computing, communication, and connectivity technologies” (Vial, 2019; Gimpel et al., 2018; Schallmo et al., 2017). To fully obtain the benefits of DT, an appropriate DT design and development strategy is critical for its success under complex and uncertain external and internal circumstances. Moreover, how to enable the connections of external and internal systems for exchanging and interrogating information and intelligence of each other is a challenging research issue for DT of complex systems. A complex system is composed of many interacting parts, often called agents, which may exhibit collective and complex behaviors (Newman, 2011; Ladyman, 2013). Therefore, DT has great potential to drive changes for complex systems in their macro-, meso-, and/or micro-levels in various sectors, e.g., government, social, urban, industry, and enterprise realms where the wide spectrum and great challenges of advanced research in DT theories and practices are encountered (Lee et al., 2021). Further, when going into the new era of DT, advanced theories must be explored to enable different complex systems operated and managed “digitally” with complete synchronization of external circumstances, humans, devices, and sub-systems reflecting both cybernetic and physical forms. - Topics and subjects: This Special Issue aims to explore the complex systems related to these DT challenges and solutions and, thus, open calls for research papers that present “Transdisciplinary engineering research and practices focusing on digital transformations of complex systems.” The types of complex systems suitable for DT practices have been outlined in six main themes in the recent DT special issue collection (Lee et al. 2022). These example types include Smart factory (I), Sustainability and product-service system (II), Construction (III), Techno-centric (IV), Public infrastructure centric (V), and Business model-centric (VI). Authors are encouraged to refer to these recent DT publications. Research scopes and topics of the special issue include, but are not limited to, the following: New theories supporting future DT; Innovative DT models; DT industrialization and globalization; Practical DT design and implementation; Cutting edge DT knowledge management for complex systems; Intelligent technologies and applications for management or prediction of complex systems; Theoretical and practical performance models of DT for complex systems; Design and management for Macro, Middle or micro complex systems with DT models. Agent-based modelling for complex systems with DT models; The Computational complexity for complex systems with DT models; Information theory for complex systems with DT models; Adaptation and game theory for complex systems with DT models; Dynamical systems for complex systems with DT models; Social Network Analysis for complex systems with DT models; Explainable AI, responsible AI, trustworthy AI or ethical AI for DT-driven complex systems; Big data analytics-based DT for complex systems; Blockchain-based DT for complex systems; Cloud computing-based DT for complex systems; Digital twin-based DT for complex systems; Edge computing-based DT for complex systems; Metaverse-based DT for complex systems; Sustainable DT-driven complex systems; Managing risk or emergency of DT for complex systems; Complex systems with Urban DT models; Complex systems with Government DT models; Complex systems with Industry DT models; Complex systems with Enterprise DT models.
Last updated by Dou Sun in 2022-03-19
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