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

  in Special Issue   Posted on May 28, 2020

Information for the Special Issue

Submission Deadline: Sat 31 Oct 2020
Journal Impact Factor : 2.680
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 the recent development of robotic process automation (RPA) and artificial intelligent (AI), academics and industrial practitioners are now pursuing robust and adaptive decision making (DM) in real-life engineering applications to accommodate the range of risk appetites and risk tolerance [1]. In state-of-the-art modelling under uncertainty and advanced data analytics, decision-makers can better manage future uncertainty by conducting qualitative risk analysis and detecting the possible fault of the system [2, 3]. The system reliability with risk and control consideration can achieve better cognitive decision, solution robustness and adaptability via business process optimisation and technology enablement. As such, untapped risk and exogenous uncertainty can inherently be formulated as a model component in DM [4]. The emerging research via RPA, AI and soft computing offers sophisticated decision analysis method, data-driven DM and scenario analysis with regards to the consideration of decision choices, and provides benefits in numerous engineering applications, including transport systems, air traffic control, maritime transport, smart city, supply chain network design, portfolio optimisation, city logistics, inventory management, construction and maintenance [5-8].

The emerging intelligent automation (IA) – the combination of RPA, AI and soft computing – can further transcends the traditional DM to achieve unprecedented level of operational efficiency, decision quality and system reliability. RPA allows an intelligent agent to eliminate operational errors and mimic manual routine decisions, including rule-based, well-structured and repetitive decision involving enormous data, in a digital system [9]. AI has the cognitive capabilities to emulate the actions of human behaviour and process unstructured data via machine learning, natural language processing and image processing. AI acts as an agent of human-like decisions, while optimisation methods and soft computing to support better decision- making processes as if the information is provided in a timely manner. The solution robustness and system resilience allow decision-makers resolve the problem with conflicting criteria and imperfect information under uncertain environment [10]. Insights from IA drive new opportunities in providing automate DM processes, fault diagnosis, knowledge elicitation and solutions under complex decision environments with the presence of uncertainty [2, 11]. Stakeholders are actively exploring IA-driven approaches in adaptive DM. Achieving prefect information for some combinatory problems is nearly impossible: the deterministic solution may not lead to actionable insight [12]. Therefore, prompt and precise DM from advanced IA is required in order to be agile and responsive to uncertainties and achieve high solution robustness and high adaptability of solution [13].

The new challenges on adaptive DM are continuously discussed. How can the complex data and its pattern be analysed via IA/RPA/AI/soft computing techniques and further support the automate DM process 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 and compliance? What kinds of features and algorithm structures can adapt to environmental conditions and respond to disruption and alternative events and should be considered?

Topics and themes

This special issue is expected to present and promote novel IA, RPA, AI, data-driven optimisation methods for complex real-life engineering applications in operational and tactical decisions considering solution robustness and adaptability of disruption in operation, with the aim of supporting the next generation of data-driven optimisation approaches, modelling under uncertainty and adaptive control of DM. Research articles proposing novel algorithms and general survey articles are also encouraged for submission if the articles fall into the scope of the special issue.
This special issue 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 the guest editor (Dr Kam K.H. NG) with a manuscript title and abstract for an early feedback on possible correspondence with the special issue’s topics and themes.

All papers forwarded for the special issue must be submitted via Editorial Manager ® for Advanced Engineering Informatics (https://www.editorialmanager.com/advei/default.aspx). To ensure that your paper is correctly 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 in accordance with the format and guidelines described in https://www.elsevier.com/journals/advanced-engineering-informatics/1474-0346/guide-for-authors. Papers submitted to the Special Issue will be subjected to a regular thorough double-blind review process. Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by another journal.

Corresponding guest editors
Dr Carman K. M. LEE
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
Email: ckm.lee@polyu.edu.hk

Dr Kam K. H. NG
School of Mechanical and Aerospace Engineering, Nanyang Technological University, 65 Nanyang Drive, Singapore 637460, Singapore
Email: kamhung.ng@ntu.edu.sg

Dr Zhixin YANG
State Key Laboratory of Internet of Things 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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[3] C.K.M. Lee, An integrated framework for outsourcing risk 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 past decades: 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, Applied Soft 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 capacitated vehicle routing problem and re-routing strategies under time-dependent traffic congestion, Computers & Industrial Engineering, 109 (2017) 151-168.
[7] J. Jiao, Q. Xu, Z. Wu, N.-K. Ng, Coordinating product, process, and supply chain decisions: A constraint satisfaction approach, Engineering Applications of Artificial Intelligence, 22 (2009) 992-1004.
[8] J. He, C. Tan, Y. Zhang, Yard crane scheduling problem in a container terminal considering risk caused by uncertainty, 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 system identification 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 replenishment uncertainty: 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/departure delay 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 turbine generator systems using extreme learning machine, Neurocomputing, 128 (2014) 249-257.

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