Skip to main content
SpringerLink
Log in

Toward an action-granularity-oriented modularization strategy for complex mechanical products using a hybrid GGA-CGA method

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Modular technology is a mainstream industrial trend, especially in manufacturing transformation and upgrading. Modularization is fundamental in modular technology and plays an important role in modular production processes, such as modular design, manufacturing, and assembly. Previous modularization studies have neglected large-scale and complex mechanical products. Furthermore, the traditional modularization method generates coarse-grained modular results with complex structures and specific functions; these modules are difficult to standardize and apply in modular production processes for cross-family products. Therefore, this study proposes an action-granularity-oriented modularization strategy to obtain finer-granularity modules and emphasizes the simplicity, fundamentality, and typicality of these modules to increase their generality. This strategy clusters components into different modules at the action level by first analyzing the association strengths among the components based on the concept of key action components and a modularity-driven factor system. Then, with the help of the design structure matrix (DSM) theory and the evidence theory, the association information is synthesized to construct a synthetic association DSM. Additionally, a new modularization method combining the grouping genetic algorithm (GGA) and constrained genetic algorithm (CGA), called the hybrid GGA-CGA method, is developed to search for the optimal modular schemes among all possible solutions based on the synthetic association DSM. Finally, a modularization case for a typical complex mechanical product (a winding engine) is used to demonstrate the feasibility and effectiveness of the proposed hybrid method in addressing modularization.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25

Similar content being viewed by others

References

  1. Avlonitis V, Hsuan J (2017) Exploring modularity in services: cases from tourism. Int J Oper Prod Manag 37(6):771–790. https://doi.org/10.1108/ijopm-08-2015-0531

    Article  Google Scholar 

  2. Balasaraswathi M, Kalpana B (2018) Fast and effective classification using parallel and multi-start PSO. J Inf Technol Res 11(2):13–30. https://doi.org/10.4018/jitr.2018040102

    Article  Google Scholar 

  3. Bataglin M, Ferreira JCE (2020) A modularization method based on the triple bottom line and product desirability: a case study of a hydraulic product. J Clean Prod 271:122198. https://doi.org/10.1016/j.jclepro.2020.122198

    Article  Google Scholar 

  4. Baylis K, Zhang GL, McAdams DA (2018) Product family platform selection using a Pareto front of maximum commonality and strategic modularity. Res Eng Des 29(4):547–563. https://doi.org/10.1007/s00163-018-0288-5

    Article  Google Scholar 

  5. Brown DE, Huntley CL (1992) A practical application of simulated annealing to clustering. Pattern Recogn 25(4):401–412. https://doi.org/10.1016/0031-3203(92)90088-z

    Article  Google Scholar 

  6. Carlborg P, Kindstrom D (2014) Service process modularization and modular strategies (Article). J Bus Ind Mark 29(4):313–323. https://doi.org/10.1108/jbim-08-2013-0170

    Article  Google Scholar 

  7. Choi JO, O’Connor JT, Kwak YH, Shrestha BK (2019) Modularization business case analysis model for industrial projects. J Manag Eng. https://doi.org/10.1061/(asce)me.1943-5479.0000683

    Article  Google Scholar 

  8. Costa A, Cappadonna FV, Fichera S (2020) Minimizing makespan in a flow shop sequence dependent group scheduling problem with blocking constraint. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2019.103413

    Article  MATH  Google Scholar 

  9. Dao SD, Abhary K, Marian R (2017) An innovative framework for designing genetic algorithm structures. Expert Syst Appl 90:196–208. https://doi.org/10.1016/j.eswa.2017.08.018

    Article  Google Scholar 

  10. de Blok C, Meijboom B, Luijkx K, Schols J (2013) The human dimension of modular care provision: opportunities for personalization and customization. Int J Prod Econ 142(1):16–26. https://doi.org/10.1016/j.ijpe.2012.05.006

    Article  Google Scholar 

  11. de Blok C, Meijboom B, Luijkx K, Schols J, Schroeder R (2014) Interfaces in service modularity: a typology developed in modular health care provision. J Oper Manag 32(4):175–189. https://doi.org/10.1016/j.jom.2014.03.001

    Article  Google Scholar 

  12. Du G, Xia Y, Jiao RJ, Liu XJ (2019) Leader-follower joint optimization problems in product family design. J Intell Manuf 30(3):1387–1405. https://doi.org/10.1007/s10845-017-1332-4

    Article  Google Scholar 

  13. Du YW, Wang YM (2017) Evidence combination rule with contrary support in the evidential reasoning approach. Expert Syst Appl 88:193–204. https://doi.org/10.1016/j.eswa.2017.06.045

    Article  Google Scholar 

  14. Ezzat O, Medini K, Boucher X, Delorme X (2019) Product and service modularization for variety management. Proc Manuf 28:148–153. https://doi.org/10.1016/j.promfg.2018.12.024

    Article  Google Scholar 

  15. Fransen L, Peters VJT, Meijboom BR, de Vries E (2019) Modular service provision for heterogeneous patient groups: a single case study in chronic Down syndrome care. Bmc Health Serv Res. https://doi.org/10.1186/s12913-019-4545-8

    Article  Google Scholar 

  16. Giannakis M, Doran D, Mee D, Papadopoulos T, Dubey R (2018) The design and delivery of modular legal services: implications for supply chain strategy. Int J Prod Res 56(20):6607–6627. https://doi.org/10.1080/00207543.2018.1449976

    Article  Google Scholar 

  17. Hammad AWA, Akbarnezhad A, Wu P, Wang X, Haddad A (2019) Building information modelling-based framework to contrast conventional and modular construction methods through selected sustainability factors. J Clean Prod 228:1264–1281. https://doi.org/10.1016/j.jclepro.2019.04.150

    Article  Google Scholar 

  18. Hankammer S, Jiang R, Kleer R, Schymanietz M (2018) Are modular and customizable smartphones the future, or doomed to fail? A case study on the introduction of sustainable consumer electronics. CIRP J Manuf Sci Technol 23:146–155. https://doi.org/10.1016/j.cirpj.2017.11.001

    Article  Google Scholar 

  19. He S, Belacel N, Chan A, Hamam H, Bouslimani Y (2016) A Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Automatic Identification of Clusters. Int J Inf Technol Decis Mak 15(5):949–974. https://doi.org/10.1142/s0219622016500267

    Article  Google Scholar 

  20. Huang JH, Liu J (2016) A similarity-based modularization quality measure for software module clustering problems. Inf Sci 342:96–110. https://doi.org/10.1016/j.ins.2016.01.030

    Article  Google Scholar 

  21. Ji Y, Jiao RJ, Chen L, Wu C (2013) Green modular design for material efficiency: a leader–follower joint optimization model. J Clean Prod 41:187–201. https://doi.org/10.1016/j.jclepro.2012.09.022

    Article  Google Scholar 

  22. Jiang H, Kwong CK, Park WY, Yu KM (2018) A multi-objective PSO approach of mining association rules for affective design based on online customer reviews. J Eng Des 29(7):381–403. https://doi.org/10.1080/09544828.2018.1475629

    Article  Google Scholar 

  23. Jiang HM, Kwong CK, Siu KWM, Liu Y (2015) Rough set and PSO-based ANFIS approaches to modeling customer satisfaction for affective product design. Adv Eng Inform 29(3):727–738. https://doi.org/10.1016/j.aei.2015.07.005

    Article  Google Scholar 

  24. Kamrani AK, Gonzalez R (2003) A genetic algorithm-based solution methodology for modular design. J Intell Manuf 14(6):599–616. https://doi.org/10.1023/a:1027362822727

    Article  Google Scholar 

  25. Kashkoush M, Elmaraghy H (2014) Consensus tree method for generating master assembly sequence. Prod Eng Res Devel. https://doi.org/10.1007/s11740-013-0499-6

    Article  Google Scholar 

  26. Kashkoush M, ElMaraghy H (2017) Designing modular product architecture for optimal overall product modularity. J Eng Des 28(5):293–316. https://doi.org/10.1080/09544828.2017.1307949

    Article  Google Scholar 

  27. Kramer O (2017) Genetic algorithm essentials. In: Studies in computational intelligence

  28. Lee J, Perkins D (2021) A simulated annealing algorithm with a dual perturbation method for clustering. Pattern Recogn. https://doi.org/10.1016/j.patcog.2020.107713

    Article  Google Scholar 

  29. Lee K (2014) Identification and modularization of feature interactions using feature-feature code mapping. J Inst Internet Broadcast Commun 14(3):105–110. https://doi.org/10.7236/jiibc.2014.14.3.105

    Article  Google Scholar 

  30. Li Z-K, Wang S, Yin W-W (2019) Determining optimal granularity level of modular product with hierarchical clustering and modularity assessment. J Braz Soc Mech Sci Eng. https://doi.org/10.1007/s40430-019-1848-y

    Article  Google Scholar 

  31. Li ZD, Shen GQ, Xue XL (2014) Critical review of the research on the management of prefabricated construction (Review). Habitat Int 43:240–249. https://doi.org/10.1016/j.habitatint.2014.04.001

    Article  Google Scholar 

  32. Lifen L, Changming Z (2010) Alert clustering using integrated SOM/PSO. In: 2010 International conference on computer design and applications, ICCDA 2010, 2. https://doi.org/10.1109/ICCDA.2010.5541319

  33. Liu B, Tang C, Tang K, Hu H (2020) A Water fraction measurement method using heuristic-algorithm-based electrical capacitance tomography images post-processing technology. IEEE Access 8:206418–206426. https://doi.org/10.1109/ACCESS.2020.3037721

    Article  Google Scholar 

  34. Liu F, Liu ZL, Wu YH (2018) A group decision making model based on triangular fuzzy additive reciprocal matrices with additive approximation-consistency. Appl Soft Comput 65:349–359. https://doi.org/10.1016/j.asoc.2018.01.020

    Article  Google Scholar 

  35. Lyu HM, Zhou WH, Shen SL, Zhou AN (2020) Inundation risk assessment of metro system using AHP and TFN-AHP in Shenzhen. Sustain Cities Soc. https://doi.org/10.1016/j.scs.2020.102103

    Article  Google Scholar 

  36. Ma J, Kremer GEO, Li M (2018) A key components-based heuristic modular product design approach to reduce product assembly cost. Int J Interact Des Manuf 12(3):865–875. https://doi.org/10.1007/s12008-017-0448-2

    Article  Google Scholar 

  37. Meng FY (2018) An approach to decision-making with triangular fuzzy reciprocal preference relations and its application. Int J Syst Sci 49(3):567–581. https://doi.org/10.1080/00207721.2017.1411988

    Article  MathSciNet  MATH  Google Scholar 

  38. Mignacca B, Locatelli G, Velenturf A (2020) Modularisation as enabler of circular economy in energy infrastructure. Energy Policy. https://doi.org/10.1016/j.enpol.2020.111371

    Article  Google Scholar 

  39. Mosleh M, Dalili K, Heydari B (2018) Distributed or monolithic? A computational architecture decision framework. IEEE Syst J 12(1):125–136. https://doi.org/10.1109/JSYST.2016.2594290

    Article  Google Scholar 

  40. Oh S, Yeom HY (2012) A comprehensive framework for the evaluation of ontology modularization. Expert Syst Appl 39(10):8547–8556. https://doi.org/10.1016/j.eswa.2012.01.129

    Article  Google Scholar 

  41. Panday A, Bansal H (2016) Energy management strategy for hybrid electric vehicles using genetic algorithm. J Renew Sustain Energy 8:015701. https://doi.org/10.1063/1.4938552

    Article  Google Scholar 

  42. Park HK, Ock J-H (2016) Unit modular in-fill construction method for high-rise buildings. KSCE J Civ Eng 20(4):1201–1210. https://doi.org/10.1007/s12205-015-0198-2

    Article  Google Scholar 

  43. Pattanaik LN, Jena A (2019) Tri-objective optimisation of mixed model reconfigurable assembly system for modular products. Int J Comput Integr Manuf 32(1):72–82. https://doi.org/10.1080/0951192x.2018.1550673

    Article  Google Scholar 

  44. Sánchez D, Melin P, Castillo O (2015) Optimization of modular granular neural networks using a hierarchical genetic algorithm based on the database complexity applied to human recognition. Inf Sci 309:73–101. https://doi.org/10.1016/j.ins.2015.02.020

    Article  Google Scholar 

  45. Schoenwitz M, Potter A, Gosling J, Naim M (2017) Product, process and customer preference alignment in prefabricated house building. Int J Prod Econ 183:79–90. https://doi.org/10.1016/j.ijpe.2016.10.015

    Article  Google Scholar 

  46. Shao C, Zhang Z-J, Ye X, Zhao Y-J, Sun H-C (2018) Modular design and optimization for intelligent assembly system. Procedia CIRP 76:67–72. https://doi.org/10.1016/j.procir.2018.01.042

    Article  Google Scholar 

  47. Sharafi P, Samali B, Ronagh H, Ghodrat M (2017) Automated spatial design of multi-story modular buildings using a unified matrix method. Autom Constr 82:31–42. https://doi.org/10.1016/j.autcon.2017.06.025

    Article  Google Scholar 

  48. Shi J, Zhang W, Zhang S, Chen J (2021) A new bifuzzy optimization method for remanufacturing scheduling using extended discrete particle swarm optimization algorithm. Comput Ind Eng. https://doi.org/10.1016/j.cie.2021.107219

    Article  Google Scholar 

  49. Shoval S (2016) Dynamic modularization throughout system lifecycle using multilayer design structure matrices. In: 13th Global conference on sustainable manufacturing—decoupling growth from resource Use. 40: 85–90. https://doi.org/10.1016/j.procir.2016.01.062

  50. Smith S, Yen C-C (2010) Green product design through product modularization using atomic theory. Robot Comput Integr Manuf 26(6):790–798. https://doi.org/10.1016/j.rcim.2010.05.006

    Article  Google Scholar 

  51. Soffers R, Meijboom B, van Zaanen J, van der Feltz-Cornelis C (2014) Modular health services: a single case study approach to the applicability of modularity to residential mental healthcare. BMC Health Serv Res. https://doi.org/10.1186/1472-6963-14-210

    Article  Google Scholar 

  52. Sorensen DGH, ElMaraghy H, Brunoe TD, Nielsen K (2020) Classification coding of production systems for identification of platform candidates. CIRP J Manuf Sci Technol 28:144–156. https://doi.org/10.1016/j.cirpj.2019.11.001

    Article  Google Scholar 

  53. Sun L-X, Xie Y-L, Song X-H, Wang J-H, Yu R-Q (1994) Cluster analysis by simulated annealing. Comput Chem 18(2):103–108. https://doi.org/10.1016/0097-8485(94)85003-8

    Article  MATH  Google Scholar 

  54. Tseng MM, Wang Y, Jiao RJ (2019) Modular design. In: Chatti S, Laperrière L, Reinhart G, Tolio T (eds) CIRP encyclopedia of production engineering. Berlin, Heidelberg, pp 1226–1235

    Chapter  Google Scholar 

  55. Tugilimana A, Coelho RF, Thrall AP (2019) An integrated design methodology for modular trusses including dynamic grouping, module spatial orientation, and topology optimization. Struct Multidiscip Optim 60(2):613–638. https://doi.org/10.1007/s00158-019-02230-w

    Article  MathSciNet  Google Scholar 

  56. Van Broekhoven E, De Baets B (2006) Fast and accurate center of gravity defuzzification of fuzzy system outputs defined on trapezoidal fuzzy partitions. Fuzzy Sets Syst 157(7):904–918. https://doi.org/10.1016/j.fss.2005.11.005

    Article  MathSciNet  MATH  Google Scholar 

  57. Wang H, Sun H, Li C, Rahnamayan S, Pan J-S (2013) Diversity enhanced particle swarm optimization with neighborhood search. Inf Sci 223:119–135. https://doi.org/10.1016/j.ins.2012.10.012

    Article  MathSciNet  Google Scholar 

  58. Wang PP, Ming XG, Li D, Kong FB, Wang L, Wu ZY (2011) Modular development of product service systems (Article). Concurr Eng Res Appl 19(1):85–96. https://doi.org/10.1177/1063293x11403508

    Article  Google Scholar 

  59. Wang ZJ (2019) An axiomatic property based triangular fuzzy extension of Saaty’s consistency. Comput Ind Eng. https://doi.org/10.1016/j.cie.2019.106086

    Article  Google Scholar 

  60. Xia Y, Liu XJ, Du G (2018) Solving bi-level optimization problems in engineering design using kriging models. Eng Optim 50(5):856–876. https://doi.org/10.1080/0305215x.2017.1358711

    Article  MathSciNet  Google Scholar 

  61. Xu XM, Zhang WX, Ding XL (2018) Modular design method for filament winding process equipment based on GGA and NSGA-II. Int J Adv Manuf Technol 94(5–8):2057–2076. https://doi.org/10.1007/s00170-017-0929-2

    Article  Google Scholar 

  62. Yahya AA, Osman A, El-Bashir MS (2017) Rocchio algorithm-based particle initialization mechanism for effective PSO classification of high dimensional data. Swarm Evol Comput 34:18–32. https://doi.org/10.1016/j.swevo.2016.11.005

    Article  Google Scholar 

  63. Yang Q, Yu S, Jiang D (2014) A modular method of developing an eco-product family considering the reusability and recyclability of customer products. J Clean Prod 64:254–265. https://doi.org/10.1016/j.jclepro.2013.07.030

    Article  Google Scholar 

  64. Ye D, Sun L, Zou B, Zhang Q, Tan W, Che W (2018) Non-destructive prediction of protein content in wheat using NIRS. Spectrochim Acta Part A Mol Biomol Spectrosc 189:463–472. https://doi.org/10.1016/j.saa.2017.08.055

    Article  Google Scholar 

  65. Ye J, Xu Z, Gou X (2020) Virtual linguistic trust degree-based evidential reasoning approach and its application to emergency response assessment of railway station. Inf Sci 513:341–359. https://doi.org/10.1016/j.ins.2019.11.001

    Article  MathSciNet  Google Scholar 

  66. Yu S, Yang Q, Tao J, Tian X, Yin F (2011) Product modular design incorporating life cycle issues: group genetic algorithm (GGA) based method. J Clean Prod 19(9–10):1016–1032. https://doi.org/10.1016/j.jclepro.2011.02.006

    Article  Google Scholar 

  67. Yu SR, Yang QY, Tao J, Xu X (2015) Incorporating quality function deployment with modularity for the end-of-life of a product family. J Clean Prod 87:423–430. https://doi.org/10.1016/j.jclepro.2014.10.037

    Article  Google Scholar 

  68. Zhang X, Ma S, Chen S (2019) Healthcare process modularization using design structure matrix. Adv Eng Inform 39:320–330. https://doi.org/10.1016/j.aei.2019.02.005

    Article  Google Scholar 

  69. Zhang Y, Qi G, Ji Y, Song L, Jiang P (2012) modular product family design for pumping unit based on design structure matrix. Adv Mech Des 479–481:2420. https://doi.org/10.4028/www.scientific.net/AMR.479-481.2420

    Article  Google Scholar 

  70. Zheng C, Qin X, Eynard B, Bai J, Li J, Zhang Y (2019) SME-oriented flexible design approach for robotic manufacturing systems. J Manuf Syst 53:62–74. https://doi.org/10.1016/j.jmsy.2019.09.010

    Article  Google Scholar 

  71. Zhou Q, Thai VV (2016) Fuzzy and grey theories in failure mode and effect analysis for tanker equipment failure prediction. Saf Sci 83:74–79. https://doi.org/10.1016/j.ssci.2015.11.013

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grant 51835001 and in part by the Independent Research Project of State Key Laboratory of Mechanical Transmission of China under Grant SKLMT-ZZKT-2021R06.

Author information

Authors and Affiliations

  1. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China

    Liming Xiao, Guangquan Huang & Genbao Zhang

  2. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400044, China

    Liming Xiao, Guangquan Huang & Genbao Zhang

  3. School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Science, Chongqing, 402160, China

    Genbao Zhang

Authors
  1. Liming Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guangquan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Genbao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Guangquan Huang or Genbao Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao, L., Huang, G. & Zhang, G. Toward an action-granularity-oriented modularization strategy for complex mechanical products using a hybrid GGA-CGA method. Neural Comput & Applic 34, 6453–6487 (2022). https://doi.org/10.1007/s00521-021-06796-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06796-9

Keywords

Search

Navigation