Cluster Computing

, Volume 22, Supplement 4, pp 10145–10162 | Cite as

A case-based decision theory based process model to aid product conceptual design

  • Zhuo Hu
  • Congjun RaoEmail author
  • Chongyuan Tao
  • Peter R. N. Childs
  • Yong Zhao


In new product development, the rapid proposal of innovative solutions represents an important phase. This in turn relies on creative ideas, their evaluation, refinement and embodiment of worthwhile directions. This study aims to describe a CBDT based process model for product conceptual design that concentrates on rapidly generating innovations with the support of decision-making rationale. Case-based decision theory (CBDT), derived from case-based reasoning, is applied in this paper as a core method to aid design engineers to make an informed decision quickly, thus accelerating the design process. In the process of utilizing CBDT to support a decision, as for the similarity function, the proper value assignment methods to the selected attribute set for calculation are discussed. In order to assist with innovative solution, aspects of the theory of inventive problem solving (TRIZ) are integrated into the case-based reasoning process. Accordingly, a CBDT-TRIZ model is developed. Quality-function deployment is used to translate customer wants into relevant engineering design requirements and thus formulating the design specification. Image-Scale is used to offer an orthogonal coordinates system to aid evaluation. Finally, a case study is used to demonstrate the validity of the proposed process model based on the design of a cordless hand-tool for garden and lawn applications.


Product conceptual design Decision-making Case-based decision theory (CBDT) Theory of inventive problem solving (TRIZ) 



This work was supported by the National Natural Science Foundation of China (Nos. 61703317, 71671135, 71371148, and 71603197), the Fundamental Research Funds for the Central Universities (WUT: 2017VI026), the National Social Science Foundation of China (No. 16ZDA045).


  1. 1.
    Pahl, G., Beitz, W., Schulz, H., Jarecki, U.: Engineering Design: A Systematic Approach. Springer, Berlin (2007)Google Scholar
  2. 2.
    Ma, H.Z., Chu, X.N., Xue, D.Y., Chen, D.P.: A systematic decision making approach for product conceptual design based on fuzzy morphological matrix. Expert Syst. Appl. 81(15), 444–456 (2017)Google Scholar
  3. 3.
    Rao, C.J., Zheng, J.J., Wang, C., Xiao, X.P.: A hybrid multi-attribute group decision making method based on grey linguistic 2-tuple. Iran. J. Fuzzy Syst. 13(2), 37–59 (2016)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Hu, Z., Rao, C.J., Zheng, Y., Huang, D.: Optimization decision of supplier selection in green procurement under the mode of low carbon economy. Int. J. Comput. Intell. Syst. 8(3), 407–421 (2015)Google Scholar
  5. 5.
    Gero, J.S.: Creativity, emergence and evolution in design. Knowl. Based Syst. 9(7), 435–448 (1996)Google Scholar
  6. 6.
    Hinckeldeyn, J., Dekkers, R., Altfeld, N., Kreutzfeldt, J.: Expanding bottleneck management from manufacturing to product design and engineering processes. Comput. Ind. Eng. 76, 415–428 (2014)Google Scholar
  7. 7.
    Lin, K.Y., Chien, C.F., Kerh, R.: UNISON framework of data-driven innovation for extracting user experience of product design of wearable devices. Comput. Ind. Eng. 99, 487–502 (2016)Google Scholar
  8. 8.
    Ko, Y.T.: Modeling a hybrid-compact design matrix for new product innovation. Comput. Ind. Eng. 107, 345–359 (2017)Google Scholar
  9. 9.
    Rao, C.J., Peng, J.: Fuzzy group decision making model based on credibility theory and gray relative degree. Int. J. Inform. Technol. Decis. Mak. 8(3), 515–527 (2009)zbMATHGoogle Scholar
  10. 10.
    Tan, R., Ma, J., Liu, F., Wei, Z.: UXDs-driven conceptual design process model for contradiction solving using CAIs. Comput. Ind. 60(8), 584–591 (2009)Google Scholar
  11. 11.
    Fu, X.X., Niu, Z.W., Yeh, M.K.: Research trends in sustainable operation: a bibliographic coupling clustering analysis from 1988 to 2016. Clust. Comput. 19, 2211–2223 (2016)Google Scholar
  12. 12.
    Wallace, K.M., Blessing, L.T.: Observations on some German contributions to engineering design in memory of professor Wolfgang Beitz. Res. Eng. Des. 12(1), 2–7 (2000)Google Scholar
  13. 13.
    Suh, N.P.: Axiomatic Design: Advances and Applications (The Oxford Series on Advanced Manufacturing) (2001)Google Scholar
  14. 14.
    Mann, D.L.: Better technology forecasting using systematic innovation methods. Technol. Forecast. Soc. Chang. 70(8), 779–795 (2003)Google Scholar
  15. 15.
    Gero, J.S., Kannengiesser, U.: A function-behavior-structure ontology of processes. Artif. Intell. Eng. Des. Anal. Manuf. 21(4), 379 (2007)Google Scholar
  16. 16.
    Wang, J.R.: Ranking engineering design concepts using a fuzzy outranking preference model. Fuzzy Sets Syst. 119(1), 161–170 (2001)Google Scholar
  17. 17.
    Madhusudan, T., Zhao, J.L., Marshall, B.: A case-based reasoning framework for workflow model management. Data Knowl. Eng. 50(1), 87–115 (2004)Google Scholar
  18. 18.
    Tran, H.M., Schnwlder, J.: DisCaRia–distributed case-based reasoning system for fault management. IEEE Trans. Netw. Serv. Manag. 12(4), 540–553 (2015)Google Scholar
  19. 19.
    Hassaniena, A.E., El-Bendaryb, N., Sweidan, A.H., Mohamed, A.E., Hegazyaa, O.M.: Hybrid-biomarker case-based reasoning system for water pollution assessment in Abou Hammad Sharkia. Egypt. Appl. Soft Comput. 46, 1043–1055 (2016)Google Scholar
  20. 20.
    Cho, B., Kim, K.J., Chung, J.W.: CBR-based network performance management with multi-agent approach. Clust. Comput. 20(1), 757–767 (2017)Google Scholar
  21. 21.
    Tsai, S., Childs, P.R.N.: TRIZ: incorporating the bright process in design. TRIZ Futur. Conf. 8, 243–250 (2008)Google Scholar
  22. 22.
    Chechurin, L., Borgianni, Y.: Understanding TRIZ through the review of top cited publications. Comput. Ind. 82, 119–134 (2016)Google Scholar
  23. 23.
    Li, W., Li, Y., Wang, J., Liu, X.: The process model to aid innovation of products conceptual design. Expert Syst. Appl. 37(5), 3574–3587 (2010)Google Scholar
  24. 24.
    Rao, C.J., Goh, M., Zhao, Y., Zheng, J.J.: Location selection of sustainability city logistics centers. Transp. Res. Part D 36, 29–44 (2015)Google Scholar
  25. 25.
    Rao, C.J., Xiao, X.P., Goh, M., Zheng, J.J., Wen, J.H.: Compound mechanism design of supplier selection based on multi-attribute auction and risk management of supply chain. Comput. Ind. Eng. 105, 63–75 (2017)Google Scholar
  26. 26.
    Cortes Robles, G., Negny, S., Le Lann, J.M.: Case-based reasoning and TRIZ: a coupling for innovative conception in Chemical Engineering. Chem. Eng. Process. 48(1), 239–249 (2009)Google Scholar
  27. 27.
    Baysan, D., Durmusoglu, M.B., Cinar, D.: Team based labour assignment methodology for new product development projects. Comput. Ind. Eng. 106, 83–104 (2017)Google Scholar
  28. 28.
    Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)Google Scholar
  29. 29.
    Ishikawa, T., Terano, T.: Analogy by abstraction: case retrieval and adaptation for inventive design expert systems. Expert Syst. Appl. 10(3), 351–356 (1996)Google Scholar
  30. 30.
    Lee, D., Lee, K.: An approach to case-based system for conceptual ship design assistant. Expert Syst. Appl. 16(2), 97–104 (1999)Google Scholar
  31. 31.
    Wu, M., Lo, Y., Hsu, S.: A fuzzy CBR technique for generating product ideas. Expert Syst. Appl. 34(1), 530–540 (2008)Google Scholar
  32. 32.
    Jung, S., Lim, T., Kim, D.: Integrating radial basis function networks with case-based reasoning for product design. Expert Syst. Appl. 36(3), 5695–5701 (2009)Google Scholar
  33. 33.
    Gilboa, I., Schmeidler, D.: Case-based decision theory. Q. J. Econ. 110(3), 605–639 (1995)zbMATHGoogle Scholar
  34. 34.
    Pape, A.D., Kurtz, K.J.: Evaluating case-based decision theory: predicting empirical patterns of human classification learning. Games Econ. Behav. 82, 52–65 (2013)MathSciNetzbMATHGoogle Scholar
  35. 35.
    Yan, A., Shao, H.S., Wang, P.: A soft-sensing method of dissolved oxygen concentration by group genetic case-based reasoning with integrating group decision making. Neurocomputing 169, 422–429 (2015)Google Scholar
  36. 36.
    Wang, Q., Zhao, Y., Rao, C.J.: Analyses and improvement of case-based decision model of product conceptual design. Adv. Neural Netw. ISNN 2009, 1131–1137 (2009)Google Scholar
  37. 37.
    Childs, P.R.N., Tsai, S.: Creativity in the design process in the turbomachinery industry. J. Des. Res. 8(2), 145–164 (2010)Google Scholar
  38. 38.
    Zanni-Merk, C., Cavallucci, D., Rousselot, F.: An ontological basis for computer aided innovation. Comput. Ind. 60(8), 563–574 (2009)Google Scholar
  39. 39.
    Wang, F.K., Yeh, C.T., Chu, T.P.: Using the design for six sigma approach with TRIZ for new product development. Comput. Ind. Eng. 98, 522–530 (2016)Google Scholar
  40. 40.
    Wang, Y.H., Lee, C.H., Trappey, A.J.C.: Service design blueprint approach incorporating TRIZ and service QFD for a meal ordering system: a case study. Comput. Ind. Eng. (in Press) (2017)Google Scholar
  41. 41.
    Rao, C.J., Goh, M., Zheng, J.J.: Decision mechanism for supplier selection under sustainability. Int. J. Inf. Technol. Decis. Mak. 16(1), 87–115 (2017)Google Scholar
  42. 42.
    Blessing, L.T., Chakrabarti, A.: DRM, a Design Research Methodology. Springer, Berlin (2009)Google Scholar
  43. 43.
    Lau, H.C., Jiang, B., Chan, F.T., Ip, R.W.: An innovative scheme for product and process design. J. Mater. Process. Technol. 123(1), 85–92 (2002)Google Scholar
  44. 44.
    Zhao, L., Chen, W., Ma, J., Yang, Y.: Structural bionic design and experimental verification of a machine tool column. J. Bionic Eng. 5, 46–52 (2008)Google Scholar
  45. 45.
    Chin, K., Wang, Y., Yang, J., Gary Poon, K.K.: An evidential reasoning based approach for quality function deployment under uncertainty. Expert Syst. Appl. 36(3), 5684–5694 (2009)Google Scholar
  46. 46.
    Anderberg, M.R.: Cluster analysis for applications, DTIC Document (1973)Google Scholar
  47. 47.
    Gu, M., Tong, X., Aamodt, A.: Comparing similarity calculation methods in conversational cbr. In: IEEE International Conference on Information Reuse and Integration (IRI-2005) (2005)Google Scholar
  48. 48.
    Maicher, L., Witschel, H.F.: Merging of distributed topic maps based on the subject identity measure (SIM) approach. Proc. Berl. XML Tags 4, 301–307 (2004)Google Scholar
  49. 49.
    Hu, Z., Zhao, Y., Chen, Y.: CBDT-TRIZ model for product conceptual design. J. Comput. Inf. Syst. 9(7), 2575–2585 (2013)Google Scholar
  50. 50.
    Altshuller, G.S.: Creativity as an Exact Science: The Theory of the Solution of Inventive Problems. CRC, Boca Raton (1984)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Zhuo Hu
    • 1
  • Congjun Rao
    • 2
    Email author
  • Chongyuan Tao
    • 1
  • Peter R. N. Childs
    • 3
  • Yong Zhao
    • 4
  1. 1.School of AutomationWuhan University of TechnologyWuhanChina
  2. 2.School of ScienceWuhan University of TechnologyWuhanChina
  3. 3.Dyson School of Design EngineeringImperial College LondonLondonUK
  4. 4.Institute of Systems EngineeringHuazhong University of Science and TechnologyWuhanChina

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