Emerging intelligent automation and optimisation methods for adaptive decision making with real-world application

  in Special Issue   Posted on June 1, 2020

Information for the Special Issue

Submission Deadline: Sat 31 Oct 2020
Journal Impact Factor : 3.879
Journal Name : Advanced Engineering Informatics
Journal Publisher:
Website for the Special Issue: https://www.journals.elsevier.com/advanced-engineering-informatics/call-for-papers/emerging-intelligent-automation-and-optimisation
Journal & Submission Website: https://www.journals.elsevier.com/advanced-engineering-informatics

Special Issue Call for Papers:

Aims and Scopes

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 [23]. 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 [211]. 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.

Submission Guideline

Authors may wish to contact theguest editor (Dr Kam K.H. NG) with a manuscript title and abstract foran early feedback on possible correspondence with the special issue’s topicsand themes.

All papers forwarded for the specialissue must be submitted via Editorial Manager ® for Advanced EngineeringInformatics  (https://www.editorialmanager.com/advei/default.aspx). To ensure that your paper iscorrectly identified for inclusion into the special issue review, please select“VSI: Emerging-IA-for-DM” under the “Article Type” of the submission.

Manuscripts should be prepared inaccordance with the format and guidelines described in https://www.elsevier.com/journals/advanced-engineering-informatics/1474-0346/guide-for-authors. Papers submitted to the SpecialIssue will be subjected to a regular thorough double-blind review process. Authorsare encouraged to submit high-quality, original work that has neither appearedin, nor is under consideration by another journal.

Corresponding Guest Editors

Dr Carman K. M. LEE

Department of Industrial and SystemsEngineering, The Hong Kong Polytechnic University, Hong Kong SAR, China

Email: ckm.lee@polyu.edu.hk

 

Dr Kam K. H. NG

School of Mechanical and AerospaceEngineering, Nanyang Technological University, 65 Nanyang Drive, Singapore637460, Singapore

Email: kamhung.ng@ntu.edu.sg

 

Dr Zhixin YANG

State Key Laboratory of Internet ofThings for Smart City & Department of Electromechanical Engineering,University of Macau, Macao, China

Email: zxyang@um.edu.mo

 

Dr Roger J. JIAO

School of Mechanical Engineering,Georgia Institute of Technology, Atlanta, GA 30332-0405, USA

Email: rjiao@gatech.edu

 

Important Dates 

Submission Opening on 31 May 2020

Submission Closing on 31 October 2020

Expected review duration: 2-3 months after submission

Acceptance notification of SI final decision: 30 June 2021

 

References

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[2] Z. Yang, X. Wang, P.K. Wong, Single andSimultaneous Fault Diagnosis With Application to a Multistage Gearbox: AVersatile Dual-ELM Network Approach, IEEE Transactions on IndustrialInformatics, 14 (2018) 5245-5255.

[3] C.K.M. Lee, An integrated framework for outsourcingrisk management, Industrial Management & Data Systems, 112 (2012)541-558.

[4] S. Cai, Z. Ma, M.J. Skibniewski, S. Bao,Construction automation and robotics for high-rise buildings over the pastdecades: A comprehensive review, Advanced Engineering Informatics, 42 (2019)100989.

[5] K.K.H. Ng, C.K.M. Lee, F.T.S. Chan, Y. Lv,Review on meta-heuristics approaches for airside operation research, AppliedSoft Computing, 66 (2018) 104-133.

[6] K.K.H. Ng, C.K.M. Lee, S.Z. Zhang, K. Wu,W. Ho, A multiple colonies artificial bee colony algorithm for a capacitatedvehicle routing problem and re-routing strategies under time-dependent trafficcongestion, Computers & Industrial Engineering, 109 (2017) 151-168.

[7] J. Jiao, Q. Xu, Z. Wu, N.-K. Ng, Coordinatingproduct, process, and supply chain decisions: A constraint satisfactionapproach, Engineering Applications of Artificial Intelligence, 22 (2009)992-1004.

[8] J. He, C. Tan, Y. Zhang, Yard cranescheduling problem in a container terminal considering risk caused byuncertainty, Advanced Engineering Informatics, 39 (2019) 14-24.

[9] R. Syed, S. Suriadi, M. Adams, W. Bandara,S.J.J. Leemans, C. Ouyang, A.H.M. ter Hofstede, I. van de Weerd, M.T. Wynn,H.A. Reijers, Robotic Process Automation: Contemporary themes and challenges,Computers in Industry, 115 (2020) 103162.

[10] R. Pasquier, I.F.C. Smith, Robust systemidentification and model predictions in the presence of systematic uncertainty,Advanced Engineering Informatics, 29 (2015) 1096-1109.

[11] D. Yang, X. Li, R.J. Jiao, B. Wang,Decision support to product configuration considering component replenishmentuncertainty: A stochastic programming approach, Decision Support Systems, 105(2018) 108-118.

[12] K.K.H. Ng, C.K.M. Lee, F.T.S. Chan, Y.Qin, Robust aircraft sequencing and scheduling problem with arrival/departuredelay using the min-max regret approach, Transportation Research Part E:Logistics and Transportation Review, 106 (2017) 115-136.

[13] P.K.Wong, Z. Yang, C.M. Vong, J. Zhong, Real-time fault diagnosis for gas turbinegenerator systems using extreme learning machine, Neurocomputing, 128 (2014)249-257.

 

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