Survey on the use of computational optimisation in UK engineering companies

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dc.contributor.authorTiwari, Ashutosh
dc.contributor.authorHoyos, Paula Noriega
dc.contributor.authorHutabarat, Windo
dc.contributor.authorTurner, Christopher J.
dc.contributor.authorInce, Nadir
dc.contributor.authorGan, Xiao-Peng
dc.contributor.authorPrajapat, Neha
dc.date.accessioned2016-05-04T13:28:22Z
dc.date.available2016-05-04T13:28:22Z
dc.date.issued2015-01-22
dc.description.abstractThe aim of this work is to capture current practices in the use of computational optimisation in UK engineering companies and identify the current challenges and future needs of the companies. To achieve this aim, a survey was conducted from June 2013 to August 2013 with 17 experts and practitioners from power, aerospace and automotive Original Equipment Manufacturers (OEMs), steel manufacturing sector, small- and medium-sized design, manufacturing and consultancy companies, and optimisation software vendors. By focusing on practitioners in industry, this work complements current surveys in optimisation that have mainly focused on published literature. This survey was carried out using a questionnaire administered through face-to-face interviews lasting around 2 h with each participant. The questionnaire covered 5 main topics: (i) state of optimisation in industry, (ii) optimisation problems, (iii) modelling techniques, (iv) optimisation techniques, and (v) challenges faced and future research areas. This survey identified the following challenges that the participant companies are facing in solving optimisation problems: large number of objectives and variables, availability of computing resources, data management and data mining for optimisation workflow, over-constrained problems, too many algorithms with limited help in selection, and cultural issues including training and mindset. The key areas for future research suggested by the participant companies are as follows: handling large number of variables, objectives and constraints particularly when solution robustness is important, reducing the number of iterations and evaluations, helping the users in algorithm selection and business case for optimisation, sharing data between different disciplines for multi-disciplinary optimisation, and supporting the users in model development and post-processing through design space visualisation and data mining.en_UK
dc.identifier.citationTiwari A, Hoyos PN, Hutabarat W, et al., (2015) Survey on the use of computational optimisation in UK engineering companies, CIRP Journal of Manufacturing Science and Technology, Volume 9, May 2015, pp. 57-68en_UK
dc.identifier.issn1755-5817
dc.identifier.urihttp://dx.doi.org/10.1016/j.cirpj.2015.01.003
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/9855
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 Internationalen_UK
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectComputational optimisationen_UK
dc.subjectEngineering optimisationen_UK
dc.subjectOptimisation algorithmsen_UK
dc.titleSurvey on the use of computational optimisation in UK engineering companiesen_UK
dc.typeArticleen_UK

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