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Wheel hub customization with an interactive artificial immune algorithm

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Abstract

With the transformation from traditional manufacturing to intelligent manufacturing, customer-oriented personalized customization has gradually become the main mode of production. Interactive algorithms determine the pros and cons of the solution via customers which can make customers better participants in the customization process. However, if the population size is expanded and the number of evolutionary iterations is too high, frequent interactions are likely to cause customer fatigue. This paper proposes an adaptive interactive artificial immune algorithm based on improved hierarchical clustering. This algorithm uses the improved hierarchical clustering algorithm to optimize generation of the initial antibodies and applies the affinity calculation method based on customer intention, adaptive crossover and mutation operators, and a multisolution reservation method based on hybrid selection strategy to the artificial immune algorithm. Via empirical research on the customized operational data of wheel hubs, the proposed method effectively solves the problem of customer fatigue, significantly improves the convergence speed of the algorithm and reduces the time cost.

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References

  • Arrighi, P. A., & Mougenot, C. (2019). Towards user empowerment in product design: A mixed reality tool for interactive virtual prototyping. Journal of Intelligent Manufacturing, 30, 743–754. https://doi.org/10.1007/s10845-016-1276-0.

    Article  Google Scholar 

  • Babbar-Sebensa, M., & Minskerb, B. S. (2012). Interactive genetic algorithm with mixed initiative interaction for multi-criteria ground water monitoring design. Applied Soft Computing, 12(1), 182–195. https://doi.org/10.1016/j.asoc.2011.08.054.

    Article  Google Scholar 

  • Blosch, M. (2001). Pragmatism and organizational knowledge management. Knowledge & Process Management, 8(1), 39–47. https://doi.org/10.1002/kpm.95.

    Article  Google Scholar 

  • Charalampidis, D. (2005). A modified k-means algorithm for circular invariant clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(12), 1856–1865. https://doi.org/10.1109/TPAMI.2005.230.

    Article  Google Scholar 

  • Chen, Y., Sun, X. Y., Gong, D. W., Zhang, Y., Choi, J., & Klasky, S. (2017). Personalized search inspired fast interactive estimation of distribution algorithm and its application. IEEE Transactions on Evolutionary Computation, 21(4), 588–600. https://doi.org/10.1109/TEVC.2017.2657787.

    Article  Google Scholar 

  • Dou, R. L., Li, W., & Nan, G. F. (2019a). An integrated approach for dynamic customer requirement identification for product development. Enterprise Information Systems, 13(4), 448–466. https://doi.org/10.1080/17517575.2018.1526321.

    Article  Google Scholar 

  • Dou, R. L., Lin, D. D., Nan, G. F., & Lei, S. Y. (2018). A method for product personalized design based on prospect theory improved with interval reference. Computers & Industrial Engineering, 125, 708–719. https://doi.org/10.1016/j.cie.2018.04.056.

    Article  Google Scholar 

  • Dou, R. L., Zhang, Y. B., & Nan, G. F. (2016a). Customer-oriented product collaborative customization based on design iteration for tablet personal computer configuration. Computers & Industrial Engineering, 99, 474–486.

    Article  Google Scholar 

  • Dou, R. L., Zhang, Y., & Nan, G. (2019b). Application of combined Kano model and interactive genetic algorithm for product customization. Journal of Intelligent Manufacturing, 30(7), 2587–2602.

    Article  Google Scholar 

  • Dou, R. L., & Zong, C. (2014). Application of Interactive Genetic Algorithm based on hesitancy degree in product configuration for customer requirement. International Journal of Computational Intelligence Systems, 7(sup2), 74–84. https://doi.org/10.1080/18756891.2014.947118.

    Article  Google Scholar 

  • Dou, R. L., Zong, C., & Li, M. Q. (2016b). An interactive genetic algorithm with the interval arithmetic based on hesitation and its application to achieve customer collaborative product configuration design. Applied Soft Computing, 38, 384–394.

    Article  Google Scholar 

  • Dou, R. L., Zong, C., & Nan, G. F. (2016c). Multi-stage interactive genetic algorithm for collaborative product customization. Knowledge-Based Systems, 92, 43–54.

    Article  Google Scholar 

  • Esnaf, Ş., & Küçükdeniz, T. (2009). A fuzzy clustering-based hybrid method for a multi-facility location problem. Journal of Intelligent Manufacturing, 20(2), 259–265. https://doi.org/10.1007/s10845-008-0233-y.

    Article  Google Scholar 

  • Foliatto, F. S., & Silveira, G. J. C. D. (2008). Mass customization: A method for market segmentation and choice menu design. International Journal of Production Economics, 111(2), 606–622. https://doi.org/10.1016/j.ijpe.2007.02.034.

    Article  Google Scholar 

  • Gruen, T. W., Osmonbekov, T., & Czaplewski, A. J. (2006). eWOM: The impact of customer-to-customer online know-how exchange on customer value and loyalty. Journal of Business Research, 59(4), 449–456. https://doi.org/10.1016/j.jbusres.2005.10.004.

    Article  Google Scholar 

  • Haber, N., Fargnoli, M., & Sakao, T. (2018). Integrating QFD for product-service systems with the Kano model and fuzzy AHP. Total Quality Management & Business Excellence. https://doi.org/10.1080/14783363.2018.1470897.

    Article  Google Scholar 

  • Ignatius, J., Rahman, A., Yazdani, M., Šaparauskas, J., & Haron, S. H. (2016). An integrated fuzzy ANP–QFD approach for green building assessment. Journal of Civil Engineering and Management, 22(4), 551–563. https://doi.org/10.3846/13923730.2015.1120772.

    Article  Google Scholar 

  • Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002). An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 881–892. https://doi.org/10.1109/TPAMI.2002.1017616.

    Article  Google Scholar 

  • Kim, J. H., Choi, J. H., Yoo, K. H., & Nasridinov, A. (2019). AA-DBSCAN: An approximate adaptive DBSCAN for finding clusters with varying densities. The Journal of Supercomputing, 75(1), 142–169. https://doi.org/10.1007/s11227-018-2380-z.

    Article  Google Scholar 

  • Lei, J. S., Jiang, T., Wu, K., Du, H., Zhu, G., & Wang, Z. (2016). Robust K-means algorithm with automatically splitting and merging cluters and its applications for surveilance data. Multimedia Tools and Applications, 75(19), 12043–12059. https://doi.org/10.1007/s11042-016-3322-5.

    Article  Google Scholar 

  • Li, Q., Dou, R. L., Chen, F. Z., & Nan, G. F. (2014). A QoS-oriented Web service composition approach based on multi-population genetic algorithm for Internet of things. International Journal of Computational Intelligence Systems, 7(sup2), 26–34. https://doi.org/10.1080/18756891.2014.947090.

    Article  Google Scholar 

  • Li, S., Nahar, K., & Fung, B. C. M. (2015). Product customization of tablet computers based on the information of online reviews by customers. Journal of Intelligent Manufacturing, 26(1), 97–110. https://doi.org/10.1007/s10845-013-0765-7.

    Article  Google Scholar 

  • Lorbeer, B., Kosareva, A., Deva, B., Softić, D., Ruppel, P., & Küpper, A. (2018). Variations on the clustering algorithm BIRCH. Big Data Research, 11, 44–53. https://doi.org/10.1016/j.bdr.2017.09.002.

    Article  Google Scholar 

  • Lv, J., Zhu, M. M., Pan, W. J., & Liu, X. (2019). Interactive genetic algorithm oriented toward the novel design of traditional patterns. Information, 10(2), 36.

    Article  Google Scholar 

  • Nishino, H., Sueyoshi, T., Kagawa, T., & Utsumiya, K. (2008). An interactive 3D graphics modeler based on simulated human immune system. Journal of Multimedia, 3(3), 51–60.

    Article  Google Scholar 

  • Onar, S. Ç., Büyüközkan, G., Öztayşi, B., & Kahraman, C. (2016). A new hesitant fuzzy QFD approach: an application to computer workstation selection. Applied Soft Computing, 46, 1–16. https://doi.org/10.1016/j.asoc.2016.04.023.

    Article  Google Scholar 

  • Song, Y. C., Meng, H. D., Wang, S. L., O’Grady, M., & O’Hare, G. (2009). Dynamic and incremental clustering based on density reachable. In 2009 fifth international joint conference on INC, IMS and IDC (pp.1307-1310). IEEE. https://doi.org/10.1109/NCM.2009.376.

  • Sun, Q. F., Duan, Y. X., Liu, F., & Li, H. Q. (2019). Application of improved multi-threshold birch clustering in reservoir prediction. In 2019 6th international conference on systems and informatics (ICSAI), Shanghai, China, 2019 (pp. 1509–1514).

  • Sun, X. Y., Gong, D. W., & Zhang, W. (2012). Interactive genetic algorithms with large population and semi-supervised learning. Applied Soft Computing, 12(9), 3004–3013. https://doi.org/10.1016/j.asoc.2012.04.021.

    Article  Google Scholar 

  • Tavana, M., Yazdani, M., & Caprio, D. D. (2017). An application of an integrated ANP-QFD framework for sustainable supplier selection. International Journal of Logistics Research and Applications, 20(3), 254–275. https://doi.org/10.1080/13675567.2016.1219702.

    Article  Google Scholar 

  • Tseng, H. E., & Lee, S. C. (2018). Disassembly sequence planning using interactive genetic algorithms. In 2018 14th international conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD) (pp. 77–84). IEEE. https://doi.org/10.1109/FSKD.2018.8686887.

  • Wang, D. J., Yu, H. L., Wu, J., Meng, Q. Y., & Lin, Q. L. (2019). Integrating fuzzy based QFD and AHP for the design and implementation of a hand training device. Journal of Intelligent & Fuzzy Systems, 36(4), 3317–3331. https://doi.org/10.3233/JIFS-181025.

    Article  Google Scholar 

  • Yazdani, M., Kahraman, C., Zarate, P., & Onar, S. C. (2019). A fuzzy multi attribute decision framework with integration of QFD and grey relational analysis. Expert Systems with Applications, 115, 474–485. https://doi.org/10.1016/j.eswa.2018.08.017.

    Article  Google Scholar 

  • Zhang, B., & Sundar, S. S. (2019). Proactive vs. reactive personalization: Can customization of privacy enhance user experience? International Journal of Human-Computer Studies, 128(8), 86–99. https://doi.org/10.1016/j.ijhcs.2019.03.002.

    Article  Google Scholar 

  • Zhang, H. W., Xie, J. W., Ge, J. A., Zhang, Z. J., & Zong, B. F. (2019). A hybrid adaptively genetic algorithm for task scheduling problem in the phased array radar. European Journal of Operational Research, 272(3), 868–878.

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by Tianjin Science and Technology Project No. 18YFCZZC00060 and No. 18ZXZNGX00100. The Natural Science Foundation of Hebei Province No. F2019202062.

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Authors and Affiliations

  1. School of Artificial Intelligence, Hebei University of Technology, Tianjin, China

    Jing Liu & Qiqi Zhi

  2. School of Mechanical Engineering, Hebei University of Technology, Tianjin, China

    Haipeng Ji

  3. School of Electrical Engineering, Hebei University of Technology, Tianjin, China

    Bolong Li

  4. Tianjin JingNuo Data Technology Co., Ltd., Tianjin, China

    Jing Liu & Haipeng Ji

  5. Beijing Baidu Netcom Science Technology Co., Ltd, Beijing, China

    Siyuan Lei

  6. College of Management and Economics, Tianjin University, Tianjin, China

    Siyuan Lei

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  1. Jing Liu
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  2. Qiqi Zhi
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  4. Bolong Li
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  5. Siyuan Lei
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Correspondence to Siyuan Lei.

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Liu, J., Zhi, Q., Ji, H. et al. Wheel hub customization with an interactive artificial immune algorithm. J Intell Manuf 32, 1305–1322 (2021). https://doi.org/10.1007/s10845-020-01613-x

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