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• W3193236421 abstract "Abstract Flexibility analysis helps improve the expected value of engineering systems under uncertainty (economic and/or social). Designing for flexibility, however, can be challenging as a large number of design variables, parameters, uncertainty drivers, decision making possibilities and metrics must be considered. Many available techniques either rely on assumptions that are not suitable for an engineering setting, or may be limited due to computational intractability. This paper makes the case for an increased integration of Machine Learning into flexibility and real options analysis in engineering systems design to complement existing design methods. Several synergies are found and discussed critically between the fields in order to explore better solutions that may exist by analyzing the data, which may not be intuitive to domain experts. Reinforcement Learning is particularly promising as a result of the theoretical common grounds with latest methodological developments e.g. decision-rule based real options analysis. Relevance to the field of computational creativity is examined, and potential avenues for further research are identified. The proposed concepts are illustrated through the design of an example infrastructure system." @default.
• W3193236421 created "2021-08-16" @default.
• W3193236421 creator A5024958939 @default.
• W3193236421 creator A5040079108 @default.
• W3193236421 date "2021-07-27" @default.
• W3193236421 modified "2023-09-25" @default.
• W3193236421 title "THE ROLE OF MACHINE LEARNING FOR FLEXIBILITY AND REAL OPTIONS ANALYSIS IN ENGINEERING SYSTEMS DESIGN" @default.
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• W3193236421 doi "https://doi.org/10.1017/pds.2021.573" @default.
• W3193236421 hasPublicationYear "2021" @default.
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