IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v131y2011i2p701-708.html
   My bibliography  Save this article

Using least squares support vector machines for the airframe structures manufacturing cost estimation

Author

Listed:
  • Deng, S.
  • Yeh, Tsung-Han

Abstract

Accurate cost estimation plays a significant role in industrial product development and production. This research applied least squares support vector machines (LS-SVM) method solving the problem of estimating the manufacturing cost for airframe structural projects. This research evaluated the estimation performance using back-propagation neural networks and statistical regression analysis. In case studies, this research considered structural weight and manufacturing complexity as the main factors in determining the manufacturing labor hour. The test results verified that the LS-SVM model can provide accurate estimation performance and outperform other methods. This research provides a feasible solution for airframe manufacture industry.

Suggested Citation

  • Deng, S. & Yeh, Tsung-Han, 2011. "Using least squares support vector machines for the airframe structures manufacturing cost estimation," International Journal of Production Economics, Elsevier, vol. 131(2), pages 701-708, June.
    • Handle: RePEc:eee:proeco:v:131:y:2011:i:2:p:701-708
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925-5273(11)00077-6
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Verlinden, B. & Duflou, J.R. & Collin, P. & Cattrysse, D., 2008. "Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study," International Journal of Production Economics, Elsevier, vol. 111(2), pages 484-492, February.
    2. Chou, Jui-Sheng & Tai, Yian & Chang, Lian-Ji, 2010. "Predicting the development cost of TFT-LCD manufacturing equipment with artificial intelligence models," International Journal of Production Economics, Elsevier, vol. 128(1), pages 339-350, November.
    3. Cavalieri, Sergio & Maccarrone, Paolo & Pinto, Roberto, 2004. "Parametric vs. neural network models for the estimation of production costs: A case study in the automotive industry," International Journal of Production Economics, Elsevier, vol. 91(2), pages 165-177, September.
    4. Ciurana, J. & Quintana, G. & Garcia-Romeu, M.L., 2008. "Estimating the cost of vertical high-speed machining centres, a comparison between multiple regression analysis and the neural networks approach," International Journal of Production Economics, Elsevier, vol. 115(1), pages 171-178, September.
    5. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Binni & Wang, Pong & Tu, Yiliu, 2021. "Customer satisfaction service match and service quality-based blockchain cloud manufacturing," International Journal of Production Economics, Elsevier, vol. 240(C).
    2. Du, Shichang & Lv, Jun, 2013. "Minimal Euclidean distance chart based on support vector regression for monitoring mean shifts of auto-correlated processes," International Journal of Production Economics, Elsevier, vol. 141(1), pages 377-387.
    3. Zong, Wei & Wu, Feng & Chu, Lap-Keung & Sculli, Domenic, 2015. "A discriminative and semantic feature selection method for text categorization," International Journal of Production Economics, Elsevier, vol. 165(C), pages 215-222.
    4. Loyer, Jean-Loup & Henriques, Elsa & Fontul, Mihail & Wiseall, Steve, 2016. "Comparison of Machine Learning methods applied to the estimation of manufacturing cost of jet engine components," International Journal of Production Economics, Elsevier, vol. 178(C), pages 109-119.
    5. Isaac Kofi Nti & Adebayo Felix Adekoya & Benjamin Asubam Weyori & Owusu Nyarko-Boateng, 2022. "Applications of artificial intelligence in engineering and manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1581-1601, August.
    6. Bodendorf, Frank & Xie, Qiao & Merkl, Philipp & Franke, Jörg, 2022. "A multi-perspective approach to support collaborative cost management in supplier-buyer dyads," International Journal of Production Economics, Elsevier, vol. 245(C).
    7. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.
    8. Yang Liu & Naiwei Lu & Xinfeng Yin & Mohammad Noori, 2016. "An adaptive support vector regression method for structural system reliability assessment and its application to a cable-stayed bridge," Journal of Risk and Reliability, , vol. 230(2), pages 204-219, April.
    9. Ayşe Nur Adıgüzel Tüylü & Ergün Eroğlu, 2019. "Using Machine Learning Algorithms For Forecasting Rate of Return Product In Reverse Logistics Process," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 7(1), pages 143-156, June.
    10. Hong-Sen Yan & Yu-Fang Wang, 2019. "Matching decision method for knowledgeable manufacturing system and its production environment," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 771-782, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chou, Jui-Sheng & Tai, Yian & Chang, Lian-Ji, 2010. "Predicting the development cost of TFT-LCD manufacturing equipment with artificial intelligence models," International Journal of Production Economics, Elsevier, vol. 128(1), pages 339-350, November.
    2. Carlos F. A. Arranz & Caleb Kwong & Vania Sena, 2023. "The effect of consumption and production policies on circular economy business models: A machine learning approach," Journal of Industrial Ecology, Yale University, vol. 27(4), pages 1089-1104, August.
    3. Quintana, Guillem & Ciurana, Joaquim, 2011. "Cost estimation support tool for vertical high speed machines based on product characteristics and productivity requirements," International Journal of Production Economics, Elsevier, vol. 134(1), pages 188-195, November.
    4. Johnson, Michael D. & Kirchain, Randolph E., 2009. "Quantifying the effects of product family decisions on material selection: A process-based costing approach," International Journal of Production Economics, Elsevier, vol. 120(2), pages 653-668, August.
    5. Kwon, He-Boong, 2017. "Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 159-170.
    6. Duffner, Fabian & Mauler, Lukas & Wentker, Marc & Leker, Jens & Winter, Martin, 2021. "Large-scale automotive battery cell manufacturing: Analyzing strategic and operational effects on manufacturing costs," International Journal of Production Economics, Elsevier, vol. 232(C).
    7. Bodendorf, Frank & Xie, Qiao & Merkl, Philipp & Franke, Jörg, 2022. "A multi-perspective approach to support collaborative cost management in supplier-buyer dyads," International Journal of Production Economics, Elsevier, vol. 245(C).
    8. Fethi, Meryem Duygun & Pasiouras, Fotios, 2010. "Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey," European Journal of Operational Research, Elsevier, vol. 204(2), pages 189-198, July.
    9. Rosa Lombardi & Raffaele Trequattrini & Federico Schimperna & Myriam Cano-Rubio, 2021. "The Impact of Smart Technologies on theManagement and Strategic Control: A Structured Literature Review," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2021(suppl. 1), pages 11-30.
    10. Yanqin Bai & Xin Yan, 2016. "Conic Relaxations for Semi-supervised Support Vector Machines," Journal of Optimization Theory and Applications, Springer, vol. 169(1), pages 299-313, April.
    11. Helder Sebastião & Pedro Godinho & Sjur Westgaard, 2020. "Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 67(si), pages 1-17, December.
    12. Hong-Yu Lin & Kuentai Chen, 2015. "The Trend of Average Unit Price in Taipei City," Research in World Economy, Research in World Economy, Sciedu Press, vol. 6(1), pages 133-142, March.
    13. Noemi Nava & Tiziana Di Matteo & Tomaso Aste, 2018. "Financial Time Series Forecasting Using Empirical Mode Decomposition and Support Vector Regression," Risks, MDPI, vol. 6(1), pages 1-21, February.
    14. Wei-Chiang Hong & Yucheng Dong & Chien-Yuan Lai & Li-Yueh Chen & Shih-Yung Wei, 2011. "SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting," Energies, MDPI, vol. 4(6), pages 1-18, June.
    15. Marius Lux & Wolfgang Karl Härdle & Stefan Lessmann, 2020. "Data driven value-at-risk forecasting using a SVR-GARCH-KDE hybrid," Computational Statistics, Springer, vol. 35(3), pages 947-981, September.
    16. Georgi Nalbantov & Philip Hans Franses & Patrick Groenen & Jan Bioch, 2010. "Estimating the Market Share Attraction Model using Support Vector Regressions," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 688-716.
    17. T. Law & J. Shawe-Taylor, 2017. "Practical Bayesian support vector regression for financial time series prediction and market condition change detection," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1403-1416, September.
    18. Cang, Shuang & Yu, Hongnian, 2014. "A combination selection algorithm on forecasting," European Journal of Operational Research, Elsevier, vol. 234(1), pages 127-139.
    19. Hyejung Chung & Kyung-shik Shin, 2018. "Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction," Sustainability, MDPI, vol. 10(10), pages 1-18, October.
    20. Ślepaczuk Robert & Zenkova Maryna, 2018. "Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market," Central European Economic Journal, Sciendo, vol. 5(52), pages 186-205, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:131:y:2011:i:2:p:701-708. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.