Development cost prediction of general aviation aircraft projects with parametric modeling
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摘要
The study of the development cost of general aviation aircraft is limited by small samples with many cost-driven factors. This paper investigates a parametric modeling method for prediction of the development cost of general aviation aircraft. The proposed technique depends on some principal components, acquired by utilizing P value analysis and gray correlation analysis. According to these principal components, the corresponding linear regression and BP neural network models are established respectively. The feasibility and accuracy of the P value analysis are verified by comparing results of model fitting and prediction. A sensitivity analysis related to model precision and suitability is discussed in detail. Results obtained in this study show that the proposed method not only has a certain degree of versatility, but also provides a preliminary prediction of the development cost of general aviation aircraft.
The study of the development cost of general aviation aircraft is limited by small samples with many cost-driven factors. This paper investigates a parametric modeling method for prediction of the development cost of general aviation aircraft. The proposed technique depends on some principal components, acquired by utilizing P value analysis and gray correlation analysis. According to these principal components, the corresponding linear regression and BP neural network models are established respectively. The feasibility and accuracy of the P value analysis are verified by comparing results of model fitting and prediction. A sensitivity analysis related to model precision and suitability is discussed in detail. Results obtained in this study show that the proposed method not only has a certain degree of versatility, but also provides a preliminary prediction of the development cost of general aviation aircraft.
The study of the development cost of general aviation aircraft is limited by small samples with many cost-driven factors. This paper investigates a parametric modeling method for prediction of the development cost of general aviation aircraft. The proposed technique depends on some principal components, acquired by utilizing P value analysis and gray correlation analysis. According to these principal components, the corresponding linear regression and BP neural network models are established respectively. The feasibility and accuracy of the P value analysis are verified by comparing results of model fitting and prediction. A sensitivity analysis related to model precision and suitability is discussed in detail. Results obtained in this study show that the proposed method not only has a certain degree of versatility, but also provides a preliminary prediction of the development cost of general aviation aircraft.
引文
1.Wang Y,Yin HL,Zhang S,Yu XQ.Multi-objective optimization of aircraft design for emission and cost reductions.Chin JAeronaut 2014;27(1):52-8.
2.Castagne S,Curran R,Rothwell A,Price M,Benard E,Raghunathan S.A generic tool for cost estimating in aircraft design.Res Eng Des 2008;18(4):149-62.
3.Curran R,Raghunathan S,Price M.Review of aerospace engineering cost modelling:The genetic causal approach.Prog Aerosp Sci 2004;40(8):487-534.
4.Asiedu Y,Gu P.Product life cycle cost analysis:state of the art review.Int J Prod Res 1998;36(4):883-908.
5.Scanlan J,Hill T,Marsh R,Christophe B,Dunkley M,Cleevely P.Cost modelling for aircraft design optimization.J Eng Des 2002;13(3):261-9.
6.Truitt LJ,Tarry SE.The rise and fall of general aviation:Product liability,market structure,and technological innovation.Trans J1995;34(4):52-70.
7.Wu H,Liu Y,Ding Y,Liu J.Methods to reduce direct maintenance costs for commercial aircraft.Aircraft Eng Aerospace Technol 2004;76(1):15-8.
8.Ben-Arieh D,Qian L.Activity-based cost management for design and development stage.Int J Prod Econ 2003;83(2):169-83.
9.Johnson VS.Minimizing life cycle cost for subsonic commercial aircraft.J Aircraft 1990;27(2):139-45.
10.Lee CH.Possibilities of cost reduction with all-wing aircraft.Aeronaut J 2016;69(659):744-9.
11.Stewart DJ,Campion BS.New technology in commercial aircraft design for minimum operating cost.Aeronaut J 1980;84(830):50-9.
12.Ross TE,Crossley WA.Method to assess commercial aircraft technologies.J Aircraft 2000;37(37):570-9.
13.Jensen SC,Rettie IH,Barber EA.Role of figures of merit in design optimization and technology assessment.J Aircraft 2015;18(2):76-81.
14.Zhong XP,Ding JF,Li WJ,Zhang Y.Robust airfoil optimization with multi-objective estimation of distribution algorithm.Chin JAeronaut 2008;21(4):289-95.
15.Tirovolis NL,Serghides VC.Unit cost estimation methodology for commercial aircraft.J Aircraft 2005;42(6):1377-86.
16.Lei Y,Ainsworth RA.A J integral estimation method for cracks in welds with mismatched mechanical properties.Int J Press Vessels Pip 1997;70(3):237-45.
17.Li S’A,Song BF.Method for multivariate analysis with small sample in aircraft cost estimation.J Aircraft 2007;44(3):1042-5.
18.Zhang HF,Jiang HY,Wang FL.Research on the cost estimating of China’s aviation aircraft development project based on the parametric method.Sci Technol Manage Res 2014;319(21):210-4.
19.Ben-Arieh D.Cost estimation system for machined parts.Int JProd Res 2000;38(17):4481-94.
20.Yui W,Egbelu P.A framework for estimating manufacturing cost from geometric design data.Int J Comput Integr Manuf 2000;13(1):50-63.
21.Kulkarni UA,Bao HP.Close-loop cost equation for objects manufactured by milling.J Manuf Sci Eng 2003;125(3):436-48.
22.Li S,Liu LJ,Rui M.Short-term traffic flow prediction based on improved particle swarm optimization algorithm and BP neural network.Syst Eng Theory Pract 2012;32(9):2045-9.
23.Sun YH,Li C.Fault diagnosis of rubbish crusher based on rough set-BP neural network.Mech Des Manuf 2012;26(1):218-20.
24.Wu YL,Yuan Y.Back propagation network optimization algorithm based on cultural particle swarm algorithm.J Syst Simulat 2011;23(5):930-4.
25.Sadeghi BHM.A BP-neural network predictor model for plastic injection molding process.J Mater Process Technol 2000;103(3):411-6.
26.Zhou HR,Zheng P.Time series forecasting based on hierarchical genetic algorithm and BP neural network.J Syst Simulat 2007;19(21):5055-8.
27.Liu FH,Xie NM.Civilian aircraft cost estimation model and algorithm based on small sample and poor information.J Syst Simulat 2014;26(3):687-91[Chinese].
28.Gunayd?n HM,Dog?an SZ.A neural network approach for early cost estimation of structural systems of buildings.Int J Project Manage 2004;22(7):595-602.
29.Hegazy T,Ayed A.Neural network model for parametric cost estimation of highway projects.J Construct Eng Manage 1998;124(3):210-8.
30.Sonmez R,Ontepeli B.Predesign cost estimation of urban railway projects with parametric modeling.J Civ Eng Manage 2009;15(4):405-9.
31.Kim GH,Seo DS,Kang KI.Hybrid models of neural networks and genetic algorithms for predicting preliminary cost estimates.JComput Civ Eng 2005;19(2):208-11.
32.Liu FH.Cost estimation model of civil aircraft under the background of small samples and poor information[dissertation].Nanjing:Nanjing University of Aeronautics and Astronautics;2014[Chinese].
2.Castagne S,Curran R,Rothwell A,Price M,Benard E,Raghunathan S.A generic tool for cost estimating in aircraft design.Res Eng Des 2008;18(4):149-62.
3.Curran R,Raghunathan S,Price M.Review of aerospace engineering cost modelling:The genetic causal approach.Prog Aerosp Sci 2004;40(8):487-534.
4.Asiedu Y,Gu P.Product life cycle cost analysis:state of the art review.Int J Prod Res 1998;36(4):883-908.
5.Scanlan J,Hill T,Marsh R,Christophe B,Dunkley M,Cleevely P.Cost modelling for aircraft design optimization.J Eng Des 2002;13(3):261-9.
6.Truitt LJ,Tarry SE.The rise and fall of general aviation:Product liability,market structure,and technological innovation.Trans J1995;34(4):52-70.
7.Wu H,Liu Y,Ding Y,Liu J.Methods to reduce direct maintenance costs for commercial aircraft.Aircraft Eng Aerospace Technol 2004;76(1):15-8.
8.Ben-Arieh D,Qian L.Activity-based cost management for design and development stage.Int J Prod Econ 2003;83(2):169-83.
9.Johnson VS.Minimizing life cycle cost for subsonic commercial aircraft.J Aircraft 1990;27(2):139-45.
10.Lee CH.Possibilities of cost reduction with all-wing aircraft.Aeronaut J 2016;69(659):744-9.
11.Stewart DJ,Campion BS.New technology in commercial aircraft design for minimum operating cost.Aeronaut J 1980;84(830):50-9.
12.Ross TE,Crossley WA.Method to assess commercial aircraft technologies.J Aircraft 2000;37(37):570-9.
13.Jensen SC,Rettie IH,Barber EA.Role of figures of merit in design optimization and technology assessment.J Aircraft 2015;18(2):76-81.
14.Zhong XP,Ding JF,Li WJ,Zhang Y.Robust airfoil optimization with multi-objective estimation of distribution algorithm.Chin JAeronaut 2008;21(4):289-95.
15.Tirovolis NL,Serghides VC.Unit cost estimation methodology for commercial aircraft.J Aircraft 2005;42(6):1377-86.
16.Lei Y,Ainsworth RA.A J integral estimation method for cracks in welds with mismatched mechanical properties.Int J Press Vessels Pip 1997;70(3):237-45.
17.Li S’A,Song BF.Method for multivariate analysis with small sample in aircraft cost estimation.J Aircraft 2007;44(3):1042-5.
18.Zhang HF,Jiang HY,Wang FL.Research on the cost estimating of China’s aviation aircraft development project based on the parametric method.Sci Technol Manage Res 2014;319(21):210-4.
19.Ben-Arieh D.Cost estimation system for machined parts.Int JProd Res 2000;38(17):4481-94.
20.Yui W,Egbelu P.A framework for estimating manufacturing cost from geometric design data.Int J Comput Integr Manuf 2000;13(1):50-63.
21.Kulkarni UA,Bao HP.Close-loop cost equation for objects manufactured by milling.J Manuf Sci Eng 2003;125(3):436-48.
22.Li S,Liu LJ,Rui M.Short-term traffic flow prediction based on improved particle swarm optimization algorithm and BP neural network.Syst Eng Theory Pract 2012;32(9):2045-9.
23.Sun YH,Li C.Fault diagnosis of rubbish crusher based on rough set-BP neural network.Mech Des Manuf 2012;26(1):218-20.
24.Wu YL,Yuan Y.Back propagation network optimization algorithm based on cultural particle swarm algorithm.J Syst Simulat 2011;23(5):930-4.
25.Sadeghi BHM.A BP-neural network predictor model for plastic injection molding process.J Mater Process Technol 2000;103(3):411-6.
26.Zhou HR,Zheng P.Time series forecasting based on hierarchical genetic algorithm and BP neural network.J Syst Simulat 2007;19(21):5055-8.
27.Liu FH,Xie NM.Civilian aircraft cost estimation model and algorithm based on small sample and poor information.J Syst Simulat 2014;26(3):687-91[Chinese].
28.Gunayd?n HM,Dog?an SZ.A neural network approach for early cost estimation of structural systems of buildings.Int J Project Manage 2004;22(7):595-602.
29.Hegazy T,Ayed A.Neural network model for parametric cost estimation of highway projects.J Construct Eng Manage 1998;124(3):210-8.
30.Sonmez R,Ontepeli B.Predesign cost estimation of urban railway projects with parametric modeling.J Civ Eng Manage 2009;15(4):405-9.
31.Kim GH,Seo DS,Kang KI.Hybrid models of neural networks and genetic algorithms for predicting preliminary cost estimates.JComput Civ Eng 2005;19(2):208-11.
32.Liu FH.Cost estimation model of civil aircraft under the background of small samples and poor information[dissertation].Nanjing:Nanjing University of Aeronautics and Astronautics;2014[Chinese].