Journal Information
Advanced Engineering Informatics (AEI)
Impact Factor:

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 2019-11-24
Special Issues
Special Issue on Emerging intelligent automation and optimisation methods for adaptive decision making with real-world application
Submission Date: 2020-10-31

With therecent development of robotic process automation (RPA), and artificial intelligent(AI), academics and industrial practitioners are now pursuing robust andadaptive decision making (DM) in real-life engineering applications toaccommodate the range of risk appetites and risk tolerance [1]. In state-of-the-art modelling underuncertainty and advanced data analytics, decision-makers can better managefuture uncertainty by conducting qualitative risk analysis and detecting thepossible fault of the system [2,3]. The system reliability with riskand control consideration can achieve better cognitive decision, solutionrobustness and adaptability via business process optimisation and technologyenablement. As such, untapped risk and exogenous uncertainty can inherently beformulated as a model component in DM [4]. The emerging research via RPA, AIand soft computing offers sophisticated decision analysis method, data-drivenDM and scenario analysis with regards to the consideration of decision choices,and provides benefits in numerous engineering applications, including transportsystems, air traffic control, maritime transport, smart city, supply chainnetwork design, portfolio optimisation, city logistics, inventory management, constructionand maintenance [5-8]. The emergingintelligent automation (IA) – the combination of RPA, AI and soft computing – canfurther transcends the traditional DM to achieve unprecedented level ofoperational efficiency, decision quality and system reliability. RPA allows anintelligent agent to eliminate operational errors and mimic manual routinedecisions, including rule-based, well-structured and repetitive decisioninvolving enormous data, in a digital system [9]. AI has the cognitive capabilitiesto emulate the actions of human behaviour and process unstructured data viamachine learning, natural language processing and image processing. AI acts asan agent of human-like decisions, while optimisation methods and soft computingto support better decision- making processes as if the information is providedin a timely manner. The solution robustness and system resilience allow decision-makersresolve the problem with conflicting criteria and imperfect information underuncertain environment[10]. Insights from IA drive new opportunities in providing automate DMprocesses, fault diagnosis, knowledge elicitation and solutions under complexdecision environments with the presence of uncertainty [2,11]. Stakeholders are actively exploring IA-driven approaches in adaptive DM.Achieving prefect information forsome combinatory problems is nearly impossible: the deterministic solution maynot lead to actionable insight [12]. Therefore, prompt and precise DM from advanced IA is required in orderto be agile and responsive to uncertainties and achieve high solutionrobustness and high adaptability of solution [13]. The new challenges on adaptive DM arecontinuously discussed. How can the complex data and its pattern be analysedvia IA/RPA/AI/soft computing techniques and further support the automate DMprocess in the presence of exogenous uncertainties and environmental changes?How can the capacity utilisation rate and solution robustness be measured,determined and optimised to achieve better operational flexibility andcompliance? What kinds of features and algorithm structures can adapt toenvironmental conditions and respond to disruption and alternative events andshould be considered? Topics and Themes This specialissue is expected to present and promote novel IA, RPA, AI, data-driven optimisationmethods for complex real-life engineering applications in operational andtactical decisions considering solution robustness and adaptability of disruptionin operation, with the aim of supporting the next generation of data-drivenoptimisation approaches, modelling under uncertainty and adaptive control ofDM. Research articles proposing novel algorithms and general survey articlesare also encouraged for submission if the articles fall into the scope of thespecial issue. This specialissue focuses on the following solicited topics but not limited to: Engineering application in automate real-time DM via novel IA/RPA/AI/soft computing approach. Collaborative intelligence in the context of human-machine/robot/system collaboration. Innovative efficiency, reliability and resilience modelling in disruption management. Novel AI algorithm, mathematical programming, soft computing, meta-heuristics, matheuristics, hyper-heuristics and swarm intelligence for data-driven adaptation planning with exogenous uncertainties in real-time/near-time DM. IoT-enabled collaborative decision process and control. Big data analytics, cloud-edge system, digital-twin, cyber-physical-enabled DM. Intelligent DM system under complex and dynamic contexts. IA-based planning and scheduling.
Last updated by Dou Sun in 2020-07-08
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