Multicriteria decision optimization for the design and manufacture of structural aircraft parts


This paper concerns an optimization method applied to the design for manufacturing (DFM) of aircraft structural parts. Today, the aerospace industry has to produce aircraft less and less costly. Usual design and manufacturing process are not sufficiently efficient. A dedicated DFM method ensures that all manufacturing requirements are taken into account at the design stage. However, all requirements cannot be satisfied together; thus the best compromise must be found. The proposed approach is based on the formulation of 5 design and manufacturing performance indicators to be satisfied. From the geometrical modelling of the problem, an NSGA II genetic algorithm computes a population of one thousand permissible solutions. Thus, a decision process is applied to identify the best compromise according to the behaviour of the decision maker, using Topsis and the AHP method. This methodology is applied in an industrial context to an aircraft structural part manufactured by stamping and machining. The optimal part geometry is then calculated for three different airplane configurations. Such tests are used to extract geometric design rules. In addition, the paper highlights the impact of the user’s behaviour on the computed results.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11


  1. 1.

    Ben Ammar, O.: Planification des réapprovisionnements sous incertitudes pour les systèmes d’assemblage à plusieurs niveaux. PhD diss., Ecole des Mines de Saint-Etienne (2014)

  2. 2.

    D’Addona, D.M., Roberto, T.R.: Genetic algorithm-based optimization of cutting parameters in turning processes. Procedia CIRP 7, 323–328 (2013).

    Article  Google Scholar 

  3. 3.

    Hnaien, F., Delorme, X., Dolgui, A.: Multi-objective optimization for inventory control in two-level assembly systems under uncertainty of lead times. Comput. Oper. Res. 37(11), 1835–1843 (2010).

    MathSciNet  MATH  Article  Google Scholar 

  4. 4.

    Kianfar, F., Mokhtari, G.: Lot Sizing and lead time quotations in assembly systems. Sci. Iran. 16(2), 100–113 (2009)

    Google Scholar 

  5. 5.

    Perkgoz, C., Azaron, A., Katagiri, H., Kato, K., Sakawa, M.: A multi objective lead time control problem in multi-stage assembly systems using genetic algorithms. Eur. J. Oper. Res. 180(1), 292–308 (2007)

    MATH  Article  Google Scholar 

  6. 6.

    Bhoskar, T., Kulkarni, O.K., Kulkarni, N.K., Patekar, S.L., Kakandikar, G.M., Nandedkar, V.M.: Genetic algorithm and its applications to mechanical engineering: a review. Mater. Today: Proc. 2, 2624–2630 (2015).

    Article  Google Scholar 

  7. 7.

    Prasad, D., Ratnab, S.: Decision support systems in the metal casting industry: an academic review of research articles. Mater. Today: Proc. 5, 1298–1312 (2018)

    Google Scholar 

  8. 8.

    Asodariyaa, H., Patela, H.V., Babariyaa, D., Maniya, K.D.: Application of multi criteria decision making method to select and validate the material of a flywheel design. Mater. Today: Proc. 5, 17147–17155 (2018)

    Google Scholar 

  9. 9.

    Mendi, F., Başkal, T., Boran, K., Boran, F.E.: Optimization of module, shaft diameter and rolling bearing for spur gear through genetic algorithm. Expert Syst. Appl. 37(12), 8058–8064 (2010).

    Article  Google Scholar 

  10. 10.

    Sun, X., Yoon, J.Y.: Multi-objective optimization of a gas cyclone separator using genetic algorithm and computational fluid dynamics. Powder Technol. 325, 347–360 (2018).

    Article  Google Scholar 

  11. 11.

    Tao, J., Wang, H., Liao, H., Yu, S.: Mechanical design and numerical simulation of digital-displacement radial piston pump for multi-megawatt wind turbine drivetrain. Renew. Energy 143, 995–1009 (2019).

    Article  Google Scholar 

  12. 12.

    Saaty, T.L.: The Analytic Hierarchy Process. McGrow-Hill, New York (1980)

    MATH  Google Scholar 

  13. 13.

    Kubler, S., Robert, J., Derigent, W., Voisin, A., Le Traon, Y.: A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications. Expert Syst. Appl. 65, 398–422 (2016)

    Article  Google Scholar 

  14. 14.

    Sapuan, S.M., Mansor, M.R.: Concurrent engineering approach in the development of composite products: a review. Mater. Des. 58, 161–167 (2014)

    Article  Google Scholar 

  15. 15.

    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  16. 16.

    Chevrier, R., Liefooghe A., Jourdan J., Dhaenens, C.: On optimizing a demand responsive transport with an evolutionary multi-objective approach. In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, pp. 575–580 (2010)

  17. 17.

    Lacomme, P., Prins, C., Sevaux, M.: A genetic algorithm for a bi-objective capacitated arc routing problem. Comput. Oper. Res. 33(12), 3473–3493 (2006)

    MATH  Article  Google Scholar 

  18. 18.

    Sharma, S., Ukkusuri, S., Mathew, T.: Pareto optimal multiobjective optimization for robust transportation network design problem. Transp. Res. Record: J. Transp. Res. Board 2090, 95–104 (2009).

    Article  Google Scholar 

  19. 19.

    Vaidya, O.S., Kumar, S.: Analytic hierarchyprocess: an overview of applications. Eur. J. Oper. Res. 169(1), 1–29 (2006)

    MATH  Article  Google Scholar 

  20. 20.

    Hammami, A.: Modélisation Technico Economique d’une Chaîne Logistique dans une Entreprise Réseau. PhD diss., Université Jean Monnet (2013)

  21. 21.

    Hwang, C.-L., Yoon, K.: Multiple Attribute Decision Multiple Attribute Decision Making: Methods and Applications a State-of-the-Art Survey. Springer, New York (1981)

    MATH  Book  Google Scholar 

  22. 22.

    Behzadian, M., Otaghsara, S.K., Yazdani, M., Ignatius, J.: A state of the-art survey of TOPSIS applications. Expert Syst. Appl. 39(17), 13051–13069 (2012).

    Article  Google Scholar 

  23. 23.

    Mardani, A., Jusoh, A., Zavadskas, Edmundas Kazimieras: Fuzzy multiple criteria decision-making techniques and applications—two decades review from 1994 to 2014. Expert Syst. Appl. (2015).

    Article  Google Scholar 

  24. 24.

    Favi, C., Germani, M., Mandolini, M.: A multi-objective design approach to include material, manufacturing and assembly costs in the early design phase. Procedia CIRP 52, 251–256 (2016).

    Article  Google Scholar 

  25. 25.

    Mistry, M., Gandhi, F., Chandr, R.: Twist control of an I-beam through Vlasov bimoment actuation. In: 49th Structures, Structural Dynamics, and Materials Conference (2008)

  26. 26.

    Stark, J.: Product Lifecycle Management. Springer, Berlin (2015)

    Book  Google Scholar 

  27. 27.

    Dobbst, M.W., Nelson, R.B.: Minimum weight design of stiffened panels with fracture constraints. Comput. Struct. 8(6), 753–759 (1977)

    Article  Google Scholar 

  28. 28.

    Loughlan, J., Hussain, N.: The in-plane shear failure of transversely stiffened thin plates. Thin-Walled Struct. 81, 225–235 (2014)

    Article  Google Scholar 

  29. 29.

    Wang, W., Guo, S., Chang, N., Yang, W.: Optimum buckling design of composite stiffened panels using ant colony algorithm. Compos. Struct. 92(3), 712–719 (2010).

    Article  Google Scholar 

  30. 30.

    Bedair, O.K.: The elastic behaviour of multi-stiffened plates under uniform compression. Thin walled Struct. 27(4), 311–335 (1997)

    Article  Google Scholar 

  31. 31.

    Herencia, J.E., Weaver, P.M., Friswell, M.: Initial sizing optimisation of anisotropic composite panels with T-shaped stiffeners. Thin-Walled Struct. 46(4), 399–412 (2008)

    Article  Google Scholar 

  32. 32.

    Iuspa, L.: Free topology generation of self-stiffened panels using skeleton-based integral soft objects. Comput. Struct. 158, 184–210 (2015).

    Article  Google Scholar 

  33. 33.

    Yin, H., Yu, X.: Integration of manufacturing cost into structural optimization of composite wings. Chin. J. Aeronaut. 23(6), 670–676 (2010)

    Article  Google Scholar 

  34. 34.

    Boothroyd, G.: Product design for manufacture and assembly. Comput. Aided Des. 26(7), 505–520 (1994)

    Article  Google Scholar 

  35. 35.

    Hazony, Y.: Design for manufacturing. In: Handbook of Design, Manufacturing and Automation. Chapter 8, pp. 123–137. Boston: Wiley (1994)

  36. 36.

    Andersson, F., Hagqvist, A., Sundin, E., Björkman, M.: Design for manufacturing of composite structures for commercial aircraft—the development of a DFM strategy at SAAB aerostructures. Procedia CIRP 17, 362–367 (2014)

    Article  Google Scholar 

  37. 37.

    Hoque, A.S.M., Szecsi, T.: Designing using manufacturing feature library. J. Mater. Process. Technol. 201(1–3), 204–208 (2008)

    Article  Google Scholar 

  38. 38.

    Triantaphyllou, E.: Using the analytic hierarchy process for decision making in engineering applications: some challenges. Int. J. Ind. Eng. Appl. Pract. 2, 35–44 (1995)

    Google Scholar 

  39. 39.

    Sanchis, J.M.: El Poliedro del Problema del Cartero Rural. PhD diss., Universidad de Valencia (1990)

  40. 40.

    Jozefowiez, N.: Optimisation Combinatoire Multi-objectif: Des Méthodes aux Problèmes, de la Terre à (presque) la Lune. Habilitation à diriger des recherches INP, Toulouse (2015)

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to E. Duc.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fortunet, C., Durieux, S., Chanal, H. et al. Multicriteria decision optimization for the design and manufacture of structural aircraft parts. Int J Interact Des Manuf 14, 1015–1030 (2020).

Download citation


  • Design for manufacturing
  • Aircraft structural parts
  • Multicriteria optimization
  • Genetic algorithm
  • Decision aid method