Advertisement

Soft Computing

, Volume 23, Issue 3, pp 1059–1070 | Cite as

Optimal platform design with modularity strategy under fuzzy environment

  • Qinyu Song
  • Yaodong NiEmail author
Methodologies and Application
  • 79 Downloads

Abstract

Platform has been known as an effective strategy for trading off between product customization and economies of scale, and firms have paid more and more attention to designing an effective and efficient product platform. In platform designing, modularity strategy is usually adopted and new modules are introduced to maintain the variety of products and flexibility of the platform. However, production costs and failure rates of new modules are usually uncertain due to the lack of historical data. Besides, the values of some key parameters in platform design, such as cost saving from designing a modular platform and demand quantity of the products, are also often vague. In this paper, we study the problem of designing a product platform with modularity strategy under fuzzy environment. By characterizing the cost saving from designing a modular platform, the demand quantity of the products and the parameters representing economies of scale and product quality improvement as fuzzy variables, we formulate three fuzzy programming models. An efficient algorithm combining fuzzy simulation and simulated annealing is proposed to solve the models. Numerical experiments are conducted to show the performance of the algorithm.

Keywords

Platform design Platform modularity Credibility theory Fuzzy programming Simulated annealing 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 71471038) and Program for Huiyuan Distinguished Young Scholars, UIBE.

Compliance with ethical standards

Conflicts of interest

Qinyu Song declares that she has no conflict of interest. Yaodong Ni declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Alberto J, Tollenaere M (2005) Modular and platform methods for product family design: literature analysis. J Intell Manuf 16(3):371–390CrossRefGoogle Scholar
  2. Bruno A, Bassetto S (2013) Modular design of product families for quality and cost. Int J Prod Res 51(6):1648–1667CrossRefGoogle Scholar
  3. Ben-Arieh D, Easton T, Choubey AM (2009) Solving the multiple platforms configuration problem. Int J Prod Res 47(7):1969–1988CrossRefzbMATHGoogle Scholar
  4. Du G, Jiao RJ, Chen M (2014) Joint optimization of product family configuration and scaling design by stackelberg game. Eur J Oper Res 232(2):330–341MathSciNetCrossRefzbMATHGoogle Scholar
  5. Du JM, Yu LA, Li X (2016) Fuzzy multi-objective chance-constrained programming model for hazardous materials transportation. Int J Gen Syst 45(3):286–310MathSciNetCrossRefzbMATHGoogle Scholar
  6. Farrell RS, Simpson TW (2003) Product platform design to improve commonality in custom products. J Intell Manuf 14(6):541–556CrossRefGoogle Scholar
  7. Gao J, Yu Y (2013) Credibilistic extensive game with fuzzy payoffs. Soft Comput 17(4):557–567CrossRefzbMATHGoogle Scholar
  8. Jiao JR, Zhang Y, Wang Y (2007) A heuristic genetic algorithm for product portfolio planning. Comput Oper Res 34(6):1777–1799CrossRefzbMATHGoogle Scholar
  9. Karl U (1995) The role of product architecture in the manufacturing firm. Res Policy 24(3):419–440CrossRefGoogle Scholar
  10. Li H, Azarm S (2002) An approach for product line design selection under uncertainty and competition. J Mech Des 124(3):385–392CrossRefGoogle Scholar
  11. Liu B (2004) Uncertainty theory: an introduction to its axiomatic foundations. Springer, BerlinCrossRefzbMATHGoogle Scholar
  12. Liu B, Liu YK (2002) Expected value of fuzzy variable and fuzzy expected value models. IEEE Trans Fuzzy Syst 10(4):445C450Google Scholar
  13. Liu B, Zhao R, Wang G (2003) Uncertain programming with applications. Press of Tsinghua University, BeijingGoogle Scholar
  14. Lu M (2014) On crisp equivalents and solutions of fuzzy programming with different chance measures. Biol Lett 10(6):691–692Google Scholar
  15. Martin S, Karlsson C (2012) Product platform replacements: challenges to managers. Int J Oper Prod Manag 32(6):746–766CrossRefGoogle Scholar
  16. Meyer MH, Lehnerd AP (1997) The power of product platforms. Simon and Schuster. Int J Mass Cust 1(13):1–13Google Scholar
  17. Ni Y (2008) Fuzzy minimum weight edge covering problem. Appl Math Model 32(7):1327–1337MathSciNetCrossRefzbMATHGoogle Scholar
  18. Ni Y, Zhao Z (2017) Two-agent scheduling problem under fuzzy environment. J Intell Manuf 28(3):739–748CrossRefGoogle Scholar
  19. Olivares-Benitez E, Gonzalez-Velarde JL (2008) A metaheuristic approach for selecting a common platform for modular products based on product performance and manufacturing cost. J Intell Manuf 19(5):599–610CrossRefGoogle Scholar
  20. Qu T et al (2011) Two-stage product platform development for mass customisation. Int J Prod Res 49(8):2197–2219CrossRefGoogle Scholar
  21. Simpson TW (2004) Product platform design and customization: status and promise. Artif Intell Eng Des Anal Manuf 18(1):3–20CrossRefGoogle Scholar
  22. Suryakant S (2015) Tyagi, optimization of a platform configuration with generational changes. Int J Prod Econ 169:299–309CrossRefGoogle Scholar
  23. Souma C, Messac A, Khire RA (2011) Comprehensive product platform planning (cp3) framework. J Mech Des 133(10):101004CrossRefGoogle Scholar
  24. Tarang A et al (2013) A hybrid model of component sharing and platform modularity for optimal product family design. Int J Prod Res 51(2):614–625CrossRefGoogle Scholar
  25. Vish K, Gupta S (2001) Appropriateness and impact of platform-based product development. Manag Sci 47(1):52–68CrossRefGoogle Scholar
  26. Yang L, Zhou X, Gao Z (2013) Rescheduling trains with scenario-based fuzzy recovery time representation on two-way double-track railways. Soft Comput 17(4):605–616CrossRefGoogle Scholar
  27. Yigit AS, Ulsoy AG, Allahverdi A (2002) Optimizing modular product design for reconfigurable manufacturing. J Intell Manuf 13(4):309–316CrossRefGoogle Scholar
  28. Yigit AS, Ulsoy AG, Allahverdi A (2003) Optimal selection of module instances for modular products in reconfigurable manufacturing. Int J Prod Res 41:4063–4074CrossRefGoogle Scholar
  29. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Information Technology and ManagementUniversity of International Business and EconomicsBeijingChina

Personalised recommendations