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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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.
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.
Blosch, M. (2001). Pragmatism and organizational knowledge management. Knowledge & Process Management, 8(1), 39–47. https://doi.org/10.1002/kpm.95.
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.
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.
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.
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.
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.
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.
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.
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.
Dou, R. L., Zong, C., & Nan, G. F. (2016c). Multi-stage interactive genetic algorithm for collaborative product customization. Knowledge-Based Systems, 92, 43–54.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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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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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DOI: https://doi.org/10.1007/s10845-020-01613-x