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ANP-based quantification method for the smart manufacturing system design decomposition

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Abstract

Smart manufacturing system must be designed based on a set of strategic objectives and a variety of surrounding environments; therefore, it includes a series of complex decisions and trade-offs between investment and profit. The MSDD based on the axiomatic design principle has an advantage on breaking down requirements and parameters and considering the relationships of them, but there is a limit to quantify relative weights of each requirement and parameter. To complement the limitation, a quantification method applying the analytic network process is proposed. The proposed method can calculate the relative importance degrees of the requirement and parameter objectively and help us understand and focus on more important factors of the smart manufacturing system design.

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References

  1. Chryssolouris G (1992) Manufacturing systems: theory and practice. Springer, New York

    Book  Google Scholar 

  2. Wu B (1992) Manufacturing systems design and analysis, 2nd edn. Chapman and Hall, London

    Book  Google Scholar 

  3. Cochran DS, Arinez JF, Duda JW, Linck J (2001/2002) A decomposition approach for manufacturing system design. J Manuf Syst 20(6):371–389

  4. Carrus BJ, Cochran DS (1998) Application of a design methodology for production systems. In: Annals of the 2nd International Conference on Engineering Design and Automation, July 1998, Maui, Hawaii

  5. Neely A, Gregory M, Platts K (2005) Performance measurement system design: a literature review and research agenda. Int J Oper Prod Manag 25(12):1228–1263

    Article  Google Scholar 

  6. Suh NP (1998) Axiomatic design theory for systems. Res Eng Design 10(4):189–209

    Article  Google Scholar 

  7. De Toni A, Nassimbeni G, Tonchia S (1997) An integrated production performance measurement system. Ind Manag Data Syst 97(5):180–186

    Article  Google Scholar 

  8. Cochran DS, Lima PC (1998) Lean production system design decomposition version 4.2. Internal working document of Production System Design Laboratory. Cambridge, MA: Massachusetts Institute of Technology

  9. Cochran DS (1999) The production system design and deployment framework. In: Proceedings of the 1999 SAE International Automotive Manufacturing Conference SAE Technical Paper 1999-01-1644, May 1, pp 1–13, Detroit

  10. Suh NP (1990) The principles of design. Oxford University Press, New York

    Google Scholar 

  11. Delaram J, Valilai OF (2018) An architectural view to computer integrated manufacturing systems based on axiomatic design theory. Comput Ind 100:96–114

    Article  Google Scholar 

  12. Shao J, Lu F, Zeng C, Xu M (2016) Research progress analysis of reliability design method based on axiomatic design theory. Proc CIRP 53:107–112

    Article  Google Scholar 

  13. Palleti VR, Joseph JV, Silva A (2018) A contribution of axiomatic design principles to the analysis and impact of attacks on critical infrastructures. Int J Crit Infrastruct Prot. https://doi.org/10.1016/j.ijcip.2018.08.007

    Article  Google Scholar 

  14. Suh NP (2001) Axiomatic design: advantages and applications. Oxford University Press, New York

    Google Scholar 

  15. Togay C, Dogru AH, Tanik JC (2008) Systematic component-oriented development with axiomatic design. J Syst Softw 81:1803–1815

    Article  Google Scholar 

  16. Saaty TL (2001) Decision making with dependence feedback: the analytic network process. RWS Publications, Pittsburgh

    Google Scholar 

  17. Chou Chien-chang (2018) Application of ANP to the selection of shipping registry: the case of Taiwanese maritime industry. Int J Ind Ergon 67:89–97

    Article  Google Scholar 

  18. Becker Jarosław, Becker Aneta, Sulikowski Piotr, Zdziebko Tomasz (2018) ANP-based analysis of ICT usage in Central European enterprises. Procedia Comput Sci 126:2173–2183

    Article  Google Scholar 

  19. Bard JF, Sousk SF (1990) A tradeoff analysis for rough terrain cargo handlers using the AHP: an example of group decision-making. IEEE Trans Eng Manag 37(3):222–227

    Article  Google Scholar 

  20. Tang Xiaoli, Feng Junwen (2006) ANP Theory and Application expectation. Stat Decis-Mak 12(3):138–140

    Google Scholar 

  21. Yu R, Tzeng G-H (2006) A soft computing method for multi-criteria decision making with dependence and feedback. Appl Math Comput 180(6):63–75

    MathSciNet  MATH  Google Scholar 

  22. Yüksel İ, Dagdeviren M (2007) Using the analytic network process (ANP) in a SWOT analysis—A case study for a textile firm. Inf Sci 177:3364–3382

    Article  Google Scholar 

  23. Chang C-W, Wu C-R, Lin C-T, Lin H-L (2007) Evaluating digital video recorder systems using analytic hierarchy and analytic network processes. Inf Sci 177(16):3383–3396

    Article  Google Scholar 

  24. Jung UK, Seo DW (2010) An ANP approach for R&D project evaluation based on interdependencies between research objectives and evaluation criteria. Decis Support Syst 49(3):335–342

    Article  Google Scholar 

  25. Aragonés-Beltrán P, Chaparro-González F, Pastor-Ferrando JP, Rodríguez-Pozo F (2010) An ANP-based approach for the selection of photovoltaic solar power plant investment projects. Renew Sustain Energy Rev 14(1):249–264

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Humanitas College, Kyung Hee University, 1 Hoegi-dong, Dongdaemun-gu, Seoul, 130-701, Republic of Korea

    Hwa-Young Jeong

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  1. Hwa-Young Jeong
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Correspondence to Hwa-Young Jeong.

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Jeong, HY. ANP-based quantification method for the smart manufacturing system design decomposition. J Supercomput 76, 6141–6157 (2020). https://doi.org/10.1007/s11227-018-2665-2

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  • DOI: https://doi.org/10.1007/s11227-018-2665-2

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