{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T19:03:18Z","timestamp":1754161398639,"version":"3.41.2"},"reference-count":28,"publisher":"Emerald","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,2,2]]},"abstract":"<jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>\u2013 The purpose of this paper is to propose novel civil aircraft cost parameters\u2019 selection method and novel cost estimation approach for civil aircraft so as to effectively simulate or forecast civil aircraft cost under poor information and small sample.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Design\/methodology\/approach<\/jats:title>\n                  <jats:p>\u2013 Based on existent cost estimation indexes, this paper summarized civil aircraft research and manufacturing cost impact index system and adopted grey relational model to select most important impact factors. Consider civil aircrafts\u2019 cost information could not be easily collected, the author must estimate their costs with limited sample and poor information. A combination model of GM (0, N) model and BP neural network algorithm is proposed. Both advantages of simulation of BP neural network algorithm and poor information generation of GM (0, N) were effectively combined. Then steps of combined model were given out. Finally, nine types of aircrafts were used to test the validity of proposed model. As comparing with the traditional multiple linear regression model and simple GM (0, N) model, results indicated that proposed model can do the work better.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Findings<\/jats:title>\n                  <jats:p>\u2013 Grey relational model can be applied for parameters\u2019 selection and combined GM (0, N) model and BP neural network algorithm can estimate aircraft\u2019s cost as well. Results show that novel combined model could get high forecasting accuracy.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Practical implications<\/jats:title>\n                  <jats:p>\u2013 Cost estimation is key problem in production management of civil aircraft. Effective cost management could promote competitiveness of aircraft manufacturing company. Proposed combined model can be applied for civil aircraft cost estimation. Similarly, it could be applied for other complex equipment cost estimation.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Originality\/value<\/jats:title>\n                  <jats:p>\u2013 The paper succeeds in proposing grey relational model for cost parameters\u2019 selection and constructing a combination model of GM (0, N) model and BP neural network algorithm. Algorithm of the proposed model was discussed and steps were given out.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1108\/gs-12-2014-0054","type":"journal-article","created":{"date-parts":[[2015,2,13]],"date-time":"2015-02-13T09:35:58Z","timestamp":1423820158000},"page":"89-104","source":"Crossref","is-referenced-by-count":9,"title":["Estimating civil aircraft\u2019s research and manufacture cost by using grey system model and neural network algorithm"],"prefix":"10.1108","volume":"5","author":[{"given":"Naiming","family":"Xie","sequence":"first","affiliation":[{"name":"Institute of Grey System Studies, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}]}],"member":"140","reference":[{"issue":"4","key":"2025072817353335400_b9","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s00163-007-0042-x","article-title":"A generic tool for cost estimating in aircraft design","volume":"18","author":"","year":"2008","journal-title":"Research in Engineering Design"},{"issue":"3","key":"2025072817353335400_b6","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1111\/j.1559-3584.2000.tb03308.x","article-title":"Advances in aircraft carrier life cycle cost analysis for acquisition and ownership decision-making","volume":"112","author":"","year":"2000","journal-title":"Naval Engineers Journal"},{"issue":"1121","key":"2025072817353335400_b8","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1017\/S000192400000467X","article-title":"Aircraft cost modelling using the genetic causal technique within a systems engineering approach","volume":"111","author":"","year":"2007","journal-title":"Aeronautical Journal"},{"issue":"3-4","key":"2025072817353335400_b7","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s00170-005-0205-8","article-title":"Modelling of aircraft manufacturing cost at the concept stage","volume":"31","author":"","year":"2006","journal-title":"International Journal of Advanced Manufacturing Technology"},{"issue":"5","key":"2025072817353335400_b13","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/S0167-6911(82)80025-X","article-title":"The control problem of grey systems","volume":"1","author":"","year":"1982","journal-title":"System & Control Letters"},{"issue":"1","key":"2025072817353335400_b20","first-page":"25","article-title":"Properties of multivariable grey model GM (1, N)","volume":"1","author":"","year":"1989","journal-title":"The Journal of Grey System"},{"issue":"2","key":"2025072817353335400_b14","first-page":"1","article-title":"Grey system theory related space","volume":"4","author":"","year":"1985","journal-title":"Fuzzy Mathematics"},{"issue":"2","key":"2025072817353335400_b15","first-page":"96","article-title":"Figure on difference information space in grey relational analysis","volume":"16","author":"","year":"2004","journal-title":"Journal of Grey System"},{"key":"2025072817353335400_b3","unstructured":"Department of Army (DOA)\n           (2002),                   Cost Analysis Manual                , Department of Army, US Army Cost and Analysis Center, Arlington, VA."},{"issue":"4","key":"2025072817353335400_b4","first-page":"121","article-title":"Partial least squares regression analysis of airframe development costs","volume":"18","author":"","year":"2001","journal-title":"Quantitative & Technical Economics Research"},{"issue":"4","key":"2025072817353335400_b25","doi-asserted-by":"crossref","first-page":"7898","DOI":"10.1016\/j.eswa.2008.11.004","article-title":"Forecasting the output of integrated circuit industry using genetic algorithm based multivariable grey optimization models","volume":"36","author":"","year":"2009","journal-title":"Expert Systems with Applications"},{"issue":"6","key":"2025072817353335400_b23","first-page":"843","article-title":"Forecasting the output of integrated circuit industry using a grey model improved by the bayesian analysis","volume":"74","author":"","year":"2006","journal-title":"Technological Forecasting and Social Change"},{"issue":"2","key":"2025072817353335400_b24","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1016\/j.eswa.2007.11.015","article-title":"Forecasting integrated circuit output using multivariate grey model and grey relational analysis","volume":"36","author":"","year":"2009","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"2025072817353335400_b21","first-page":"215","article-title":"Study on parameter estimation of GM (1, N)","volume":"15","author":"","year":"2003","journal-title":"The Journal of Grey System"},{"issue":"1","key":"2025072817353335400_b5","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1111\/j.1559-3584.1999.tb01216.x","article-title":"Performance based simulation modeling quantifies aircraft carrier life cycle cost and readiness","volume":"111","author":"","year":"1999","journal-title":"Naval Engineers Journal"},{"key":"2025072817353335400_b2","unstructured":"Large, J.\n           and Gillespie, K. 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