Survey on the use of computational optimisation in UK engineering companies

Show simple item record Tiwari, Ashutosh Hoyos, Paula Noriega Hutabarat, Windo Turner, Christopher Ince, Nadir Gan, Xiao-Peng Prajapat, Neha 2016-05-04T13:28:22Z 2016-05-04T13:28:22Z 2015-01-22
dc.identifier.citation Ashutosh Tiwari, Paula Noriega Hoyos, Windo Hutabarat, Chris Turner, Nadir Ince, Xiao-Peng Gan, Neha Prajapat, Survey on the use of computational optimisation in UK engineering companies, CIRP Journal of Manufacturing Science and Technology, Volume 9, May 2015, Pages 57-68 en_UK
dc.identifier.issn 1755-5817
dc.description.abstract The 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.language.iso en en_UK
dc.publisher Elsevier en_UK
dc.rights Under a Creative Commons license Attribution 4.0 International (CC BY 4.0) You are free to: Share — copy and redistribute the material in any medium or format, Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Information: No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. en_UK
dc.subject Computational optimisation en_UK
dc.subject Engineering optimisation en_UK
dc.subject Optimisation algorithms en_UK
dc.title Survey on the use of computational optimisation in UK engineering companies en_UK
dc.type Article en_UK

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