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Special Issue "Decision Support Systems for Improving the Construction and Maintenance of Renewable Energy Projects"
Deadline for manuscript submissions: 20 November 2020.
Interests: fuzzy logic; fuzzy hybrid systems; machine learning; decision support systems; simulation; optimization; system dynamics; agent-based modeling; subjective knowledge; construction
Special Issues and Collections in MDPI journals
Interests: risk management; numerical simulation; decision support systems; artificial intelligence; scheduling; programming; and human resource planning
Renewable energy projects have recently gained popularity due to their low adverse environmental impacts. While the improvement of the construction and maintenance of such projects requires that project and operation managers make the right decisions in a timely fashion, the complexity and novelty of these projects leads to numerous challenges related to decision-making. Renewable energy projects involve numerous uncertain factors; these projects often require managers to coordinate many complex and dynamic processes for decision-making; and managers must consider sometimes contradictory criteria and/or objectives for decision-making. In recent years, the application of advanced modeling and computational techniques has emerged in different engineering disciplines to develop decision support systems for supporting practitioners in dealing with such challenges. This Special Issue focuses on the development and application of decision support systems for improving the construction and maintenance of renewable energy projects. It also includes extensions of selected papers from the 9th Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modelling (APARM 2020).
Papers are invited that cover, but are not limited to, the main topics of:
- Risk analysis and management for the construction of renewable energy infrastructure
- Decision-making for the construction or maintenance of renewable energy infrastructure
- Fault detection models for renewable energy infrastructure
- Simulation modeling of renewable energy infrastructure projects during construction, operation, and maintenance phases
- Artificial intelligence modeling of renewable energy infrastructure projects during construction, operation, and maintenance phases
- Decision-making for design and development of renewable energy infrastructure projects
- Health monitoring methods for the assessment of renewable energy infrastructure projects
Prof. Dr. Aminah Robinson Fayek
Dr. Nima Gerami Seresht
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Risk analysis
- computational techniques
- artificial intelligence
- machine learning
- renewable energy
- uncertainty modeling