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Genetic Algorithm-Based Optimization of Mass Customization Using Hyperledger Fabric Blockchain

Year 2022, Volume: 17 Issue: 2, 451 - 460, 30.09.2022
https://doi.org/10.55525/tjst.1145047

Abstract

With the developing technology, the production model, which is structured in line with user requests, has become a very popular topic. This production model, which expresses individualization, has become increasingly common. For this reason, it attracts the attention of many researchers and company executives. At this point, studies are concentrated on the concept of mass customization, which expresses personalized production. Considering the related studies, various difficulties are encountered in this production model on issues such as cooperation, trust, and optimization. In this proposed method, a blockchain-based platform is designed to solve the problems of cooperation and trust, one of the most important problems of mass customization. In addition, in this study, the problem of optimization of the production and supply chain process in the manufacturing sector has been examined. This process includes reaching from the producer to the consumer and many parameters. Therefore, the optimization of this process is a very difficult problem. A two-stage system has been proposed to find a solution to this problem. In the first stage, a reliable platform was created by bringing together service providers and buyers in the manufacturing sector with blockchain. In the second stage, the most suitable parties were selected by a genetic algorithm.

References

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  • G. Da Silveira, D. Borenstein, and F. S. Fogliatto, “Mass customization: Literature review and research directions,” Int. J. Prod. Econ., vol. 72, no. 1, pp. 1–13, 2001, doi: 10.1016/S0925-5273(00)00079-7.
  • J. L. Pallant, S. Sands, and I. O. Karpen, “The 4Cs of mass customization in service industries: a customer lens,” J. Serv. Mark., vol. 34, no. 4, pp. 499–511, 2020, doi: 10.1108/JSM-04-2019-0176.
  • G. Kumar, R. Saha, C. Lal, and M. Conti, “Internet-of-Forensic (IoF): A blockchain based digital forensics framework for IoT applications,” Futur. Gener. Comput. Syst., vol. 120, pp. 13–25, 2021, doi: 10.1016/j.future.2021.02.016.
  • D. Mourtzis, M. Doukas, and F. Psarommatis, “A multi-criteria evaluation of centralized and decentralized production networks in a highly customer-driven environment,” CIRP Ann. - Manuf. Technol., vol. 61, no. 1, pp. 427–430, 2012, doi: 10.1016/j.cirp.2012.03.035.
  • J. Bonnín Roca, P. Vaishnav, R. E. Laureijs, J. Mendonça, and E. R. H. Fuchs, “Technology cost drivers for a potential transition to decentralized manufacturing,” Addit. Manuf., vol. 28, no. December 2018, pp. 136–151, 2019, doi: 10.1016/j.addma.2019.04.010.
  • M. Pournader, Y. Shi, S. Seuring, and S. C. L. Koh, “Blockchain applications in supply chains, transport and logistics: a systematic review of the literature,” Int. J. Prod. Res., vol. 58, no. 7, pp. 2063–2081, 2020, doi: 10.1080/00207543.2019.1650976.
  • A. Vacca, A. Di Sorbo, C. A. Visaggio, and G. Canfora, “A systematic literature review of blockchain and smart contract development: Techniques, tools, and open challenges,” J. Syst. Softw., vol. 174, p. 110891, 2020, doi: 10.1016/j.jss.2020.110891.
  • A. Prashanth Joshi, M. Han, and Y. Wang, “A survey on security and privacy issues of blockchain technology,” Math. Found. Comput., vol. 1, no. 2, pp. 121–147, 2018, doi: 10.3934/mfc.2018007.
  • Y. Issaoui, A. Khiat, A. Bahnasse, and H. Ouajji, “Smart Logistics: Blockchain trends and applications,” J. Ubiquitous Syst. Pervasive Networks, vol. 12, no. 2, pp. 09–15, 2020, doi: 10.5383/juspn.12.02.002.
  • I. C. Lin and T. C. Liao, “A survey of blockchain security issues and challenges,” Int. J. Netw. Secur., vol. 19, no. 5, pp. 653–659, 2017, doi: 10.6633/IJNS.201709.19(5).01.
  • H. Wang, Z. Zheng, S. Xie, H. N. Dai, and X. Chen, “Blockchain challenges and opportunities: a survey,” Int. J. Web Grid Serv., vol. 14, no. 4, p. 352, 2018, doi: 10.1504/ijwgs.2018.10016848.
  • Y. Zhou, G. Xiong, T. Nyberg, B. Mohajeri, and S. Bao, “Social manufacturing realizing personalization production: A state-of-the-art review,” Proc. - 2016 IEEE Int. Conf. Serv. Oper. Logist. Informatics, SOLI 2016, pp. 7–11, 2016, doi: 10.1109/SOLI.2016.7551653.
  • S. Aheleroff, R. Y. Zhong, and X. Xu, “A digital twin reference for mass personalization in industry 4.0,” Procedia CIRP, vol. 93, pp. 228–233, 2020, doi: 10.1016/j.procir.2020.04.023.
  • S. Iarovyi, J. L. M. Lastra, R. Haber, and R. del Toro, “From artificial cognitive systems and open architectures to cognitive manufacturing systems,” 2015, doi: 10.1109/indin.2015.7281910.
  • X. Zhu, J. Shi, S. Huang, and B. Zhang, “Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study,” Pervasive Mob. Comput., vol. 62, p. 101113, 2020, doi: 10.1016/j.pmcj.2020.101113.
  • M. Zhang, F. Lettice, and X. Zhao, “The impact of social capital on mass customisation and product innovation capabilities,” Int. J. Prod. Res., vol. 53, no. 17, pp. 5251–5264, 2015, doi: 10.1080/00207543.2015.1015753.
  • I. Ullah and R. Narain, “Achieving mass customization capability: the roles of flexible manufacturing competence and workforce management practices,” J. Adv. Manag. Res., vol. 18, no. 2, pp. 273–296, 2020, doi: 10.1108/JAMR-05-2020-0067.
  • M. Jin, H. Wang, Q. Zhang, and Y. Zeng, “Supply chain optimization based on chain management and mass customization,” Inf. Syst. E-bus. Manag., vol. 18, no. 4, pp. 647–664, 2020, doi: 10.1007/s10257-018-0389-8.
  • J. Yao and L. Liu, “Optimization analysis of supply chain scheduling in mass customization,” Int. J. Prod. Econ., vol. 117, no. 1, pp. 197–211, 2009, doi: 10.1016/j.ijpe.2008.10.008.
  • C. Liu and J. Yao, “Dynamic supply chain integration optimization in service mass customization,” Comput. Ind. Eng., vol. 120, no. November 2017, pp. 42–52, 2018, doi: 10.1016/j.cie.2018.04.018.
  • R. Z. Farahani and M. Elahipanah, “A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain,” Int. J. Prod. Econ., vol. 111, no. 2, pp. 229–243, 2008, doi: 10.1016/j.ijpe.2006.11.028.
  • S. Katoch, S. S. Chauhan, and V. Kumar, A review on genetic algorithm: past, present, and future, vol. 80, no. 5. Multimedia Tools and Applications, 2021.
  • M. Mureddu, E. Ghiani, and F. Pilo, “Smart grid optimization with blockchain based decentralized genetic Algorithm,” IEEE Power Energy Soc. Gen. Meet., vol. 2020-Augus, 2020, doi: 10.1109/PESGM41954.2020.9281759.
  • Y. Cheng, F. Tao, D. Zhao, and L. Zhang, “Modeling of manufacturing service supply–demand matching hypernetwork in service-oriented manufacturing systems,” Robot. Comput. Integr. Manuf., vol. 45, pp. 59–72, 2017, doi: 10.1016/j.rcim.2016.05.007.
  • F. Tao, J. Cheng, Y. Cheng, S. Gu, T. Zheng, and H. Yang, “SDMSim: A manufacturing service supply–demand matching simulator under cloud environment,” Robot. Comput. Integr. Manuf., vol. 45, pp. 34–46, 2017, doi: 10.1016/j.rcim.2016.07.001.
  • Z. Liu, L. Wang, X. Li, and S. Pang, “A multi-attribute personalized recommendation method for manufacturing service composition with combining collaborative filtering and genetic algorithm,” J. Manuf. Syst., vol. 58, no. PA, pp. 348–364, 2021, doi: 10.1016/j.jmsy.2020.12.019.
  • G. Zhang, Y. Zhang, X. Xu, and R. Y. Zhong, “An augmented Lagrangian coordination method for optimal allocation of cloud manufacturing services,” J. Manuf. Syst., vol. 48, pp. 122–133, 2018, doi: 10.1016/j.jmsy.2017.11.008.
  • W. Wang, R. Y. K. Fung, and Y. Chai, “Approach of just-in-time distribution requirements planning for supply chain management,” Int. J. Prod. Econ., vol. 91, no. 2, pp. 101–107, 2004, doi: 10.1016/S0925-5273(03)00212-3.
  • S. A. A., “Blockchain Ready Manufacturing Supply Chain Using Distributed Ledger,” Int. J. Res. Eng. Technol., vol. 05, no. 09, pp. 1–10, 2016, doi: 10.15623/ijret.2016.0509001.
  • F. Tian, “An agri-food supply chain traceability system for China based on RFID & blockchain technology,” 2016 13th Int. Conf. Serv. Syst. Serv. Manag. ICSSSM 2016, pp. 1–6, 2016, doi: 10.1109/ICSSSM.2016.7538424.
  • K. Biswas, V. Muthukkumarasamy, and W. L. Tan, “Blockchain Based Wine Supply Chain Traceability System,” Proc. 2017 Futur. Technol. Conf., no. December, pp. 56–62, 2017, [Online]. Available: https://www.researchgate.net/publication/321474197.
  • J. L. Breese, S.-J. Park, and V. Ganesh, “Blockchain Technology Adoption In Supply Change Management : Two Theoretical Perspectives,” Issues Inf. Syst., vol. 20, no. 2, pp. 140–150, 2019.
Year 2022, Volume: 17 Issue: 2, 451 - 460, 30.09.2022
https://doi.org/10.55525/tjst.1145047

Abstract

References

  • Y. Qi, Z. Mao, M. Zhang, and H. Guo, “Manufacturing practices and servitization: The role of mass customization and product innovation capabilities,” Int. J. Prod. Econ., vol. 228, no. January 2019, p. 107747, 2020, doi: 10.1016/j.ijpe.2020.107747.
  • S. Kotha, “Mass customization: Implementing the emerging paradigm for competitive advantage,” Strateg. Manag. J., vol. 16, no. S1, pp. 21–42, 1995, doi: 10.1002/smj.4250160916.
  • G. Da Silveira, D. Borenstein, and F. S. Fogliatto, “Mass customization: Literature review and research directions,” Int. J. Prod. Econ., vol. 72, no. 1, pp. 1–13, 2001, doi: 10.1016/S0925-5273(00)00079-7.
  • J. L. Pallant, S. Sands, and I. O. Karpen, “The 4Cs of mass customization in service industries: a customer lens,” J. Serv. Mark., vol. 34, no. 4, pp. 499–511, 2020, doi: 10.1108/JSM-04-2019-0176.
  • G. Kumar, R. Saha, C. Lal, and M. Conti, “Internet-of-Forensic (IoF): A blockchain based digital forensics framework for IoT applications,” Futur. Gener. Comput. Syst., vol. 120, pp. 13–25, 2021, doi: 10.1016/j.future.2021.02.016.
  • D. Mourtzis, M. Doukas, and F. Psarommatis, “A multi-criteria evaluation of centralized and decentralized production networks in a highly customer-driven environment,” CIRP Ann. - Manuf. Technol., vol. 61, no. 1, pp. 427–430, 2012, doi: 10.1016/j.cirp.2012.03.035.
  • J. Bonnín Roca, P. Vaishnav, R. E. Laureijs, J. Mendonça, and E. R. H. Fuchs, “Technology cost drivers for a potential transition to decentralized manufacturing,” Addit. Manuf., vol. 28, no. December 2018, pp. 136–151, 2019, doi: 10.1016/j.addma.2019.04.010.
  • M. Pournader, Y. Shi, S. Seuring, and S. C. L. Koh, “Blockchain applications in supply chains, transport and logistics: a systematic review of the literature,” Int. J. Prod. Res., vol. 58, no. 7, pp. 2063–2081, 2020, doi: 10.1080/00207543.2019.1650976.
  • A. Vacca, A. Di Sorbo, C. A. Visaggio, and G. Canfora, “A systematic literature review of blockchain and smart contract development: Techniques, tools, and open challenges,” J. Syst. Softw., vol. 174, p. 110891, 2020, doi: 10.1016/j.jss.2020.110891.
  • A. Prashanth Joshi, M. Han, and Y. Wang, “A survey on security and privacy issues of blockchain technology,” Math. Found. Comput., vol. 1, no. 2, pp. 121–147, 2018, doi: 10.3934/mfc.2018007.
  • Y. Issaoui, A. Khiat, A. Bahnasse, and H. Ouajji, “Smart Logistics: Blockchain trends and applications,” J. Ubiquitous Syst. Pervasive Networks, vol. 12, no. 2, pp. 09–15, 2020, doi: 10.5383/juspn.12.02.002.
  • I. C. Lin and T. C. Liao, “A survey of blockchain security issues and challenges,” Int. J. Netw. Secur., vol. 19, no. 5, pp. 653–659, 2017, doi: 10.6633/IJNS.201709.19(5).01.
  • H. Wang, Z. Zheng, S. Xie, H. N. Dai, and X. Chen, “Blockchain challenges and opportunities: a survey,” Int. J. Web Grid Serv., vol. 14, no. 4, p. 352, 2018, doi: 10.1504/ijwgs.2018.10016848.
  • Y. Zhou, G. Xiong, T. Nyberg, B. Mohajeri, and S. Bao, “Social manufacturing realizing personalization production: A state-of-the-art review,” Proc. - 2016 IEEE Int. Conf. Serv. Oper. Logist. Informatics, SOLI 2016, pp. 7–11, 2016, doi: 10.1109/SOLI.2016.7551653.
  • S. Aheleroff, R. Y. Zhong, and X. Xu, “A digital twin reference for mass personalization in industry 4.0,” Procedia CIRP, vol. 93, pp. 228–233, 2020, doi: 10.1016/j.procir.2020.04.023.
  • S. Iarovyi, J. L. M. Lastra, R. Haber, and R. del Toro, “From artificial cognitive systems and open architectures to cognitive manufacturing systems,” 2015, doi: 10.1109/indin.2015.7281910.
  • X. Zhu, J. Shi, S. Huang, and B. Zhang, “Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study,” Pervasive Mob. Comput., vol. 62, p. 101113, 2020, doi: 10.1016/j.pmcj.2020.101113.
  • M. Zhang, F. Lettice, and X. Zhao, “The impact of social capital on mass customisation and product innovation capabilities,” Int. J. Prod. Res., vol. 53, no. 17, pp. 5251–5264, 2015, doi: 10.1080/00207543.2015.1015753.
  • I. Ullah and R. Narain, “Achieving mass customization capability: the roles of flexible manufacturing competence and workforce management practices,” J. Adv. Manag. Res., vol. 18, no. 2, pp. 273–296, 2020, doi: 10.1108/JAMR-05-2020-0067.
  • M. Jin, H. Wang, Q. Zhang, and Y. Zeng, “Supply chain optimization based on chain management and mass customization,” Inf. Syst. E-bus. Manag., vol. 18, no. 4, pp. 647–664, 2020, doi: 10.1007/s10257-018-0389-8.
  • J. Yao and L. Liu, “Optimization analysis of supply chain scheduling in mass customization,” Int. J. Prod. Econ., vol. 117, no. 1, pp. 197–211, 2009, doi: 10.1016/j.ijpe.2008.10.008.
  • C. Liu and J. Yao, “Dynamic supply chain integration optimization in service mass customization,” Comput. Ind. Eng., vol. 120, no. November 2017, pp. 42–52, 2018, doi: 10.1016/j.cie.2018.04.018.
  • R. Z. Farahani and M. Elahipanah, “A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain,” Int. J. Prod. Econ., vol. 111, no. 2, pp. 229–243, 2008, doi: 10.1016/j.ijpe.2006.11.028.
  • S. Katoch, S. S. Chauhan, and V. Kumar, A review on genetic algorithm: past, present, and future, vol. 80, no. 5. Multimedia Tools and Applications, 2021.
  • M. Mureddu, E. Ghiani, and F. Pilo, “Smart grid optimization with blockchain based decentralized genetic Algorithm,” IEEE Power Energy Soc. Gen. Meet., vol. 2020-Augus, 2020, doi: 10.1109/PESGM41954.2020.9281759.
  • Y. Cheng, F. Tao, D. Zhao, and L. Zhang, “Modeling of manufacturing service supply–demand matching hypernetwork in service-oriented manufacturing systems,” Robot. Comput. Integr. Manuf., vol. 45, pp. 59–72, 2017, doi: 10.1016/j.rcim.2016.05.007.
  • F. Tao, J. Cheng, Y. Cheng, S. Gu, T. Zheng, and H. Yang, “SDMSim: A manufacturing service supply–demand matching simulator under cloud environment,” Robot. Comput. Integr. Manuf., vol. 45, pp. 34–46, 2017, doi: 10.1016/j.rcim.2016.07.001.
  • Z. Liu, L. Wang, X. Li, and S. Pang, “A multi-attribute personalized recommendation method for manufacturing service composition with combining collaborative filtering and genetic algorithm,” J. Manuf. Syst., vol. 58, no. PA, pp. 348–364, 2021, doi: 10.1016/j.jmsy.2020.12.019.
  • G. Zhang, Y. Zhang, X. Xu, and R. Y. Zhong, “An augmented Lagrangian coordination method for optimal allocation of cloud manufacturing services,” J. Manuf. Syst., vol. 48, pp. 122–133, 2018, doi: 10.1016/j.jmsy.2017.11.008.
  • W. Wang, R. Y. K. Fung, and Y. Chai, “Approach of just-in-time distribution requirements planning for supply chain management,” Int. J. Prod. Econ., vol. 91, no. 2, pp. 101–107, 2004, doi: 10.1016/S0925-5273(03)00212-3.
  • S. A. A., “Blockchain Ready Manufacturing Supply Chain Using Distributed Ledger,” Int. J. Res. Eng. Technol., vol. 05, no. 09, pp. 1–10, 2016, doi: 10.15623/ijret.2016.0509001.
  • F. Tian, “An agri-food supply chain traceability system for China based on RFID & blockchain technology,” 2016 13th Int. Conf. Serv. Syst. Serv. Manag. ICSSSM 2016, pp. 1–6, 2016, doi: 10.1109/ICSSSM.2016.7538424.
  • K. Biswas, V. Muthukkumarasamy, and W. L. Tan, “Blockchain Based Wine Supply Chain Traceability System,” Proc. 2017 Futur. Technol. Conf., no. December, pp. 56–62, 2017, [Online]. Available: https://www.researchgate.net/publication/321474197.
  • J. L. Breese, S.-J. Park, and V. Ganesh, “Blockchain Technology Adoption In Supply Change Management : Two Theoretical Perspectives,” Issues Inf. Syst., vol. 20, no. 2, pp. 140–150, 2019.
There are 34 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section TJST
Authors

Nursena Bayğın 0000-0003-4457-5503

Mehmet Karaköse 0000-0002-3276-3788

Publication Date September 30, 2022
Submission Date July 18, 2022
Published in Issue Year 2022 Volume: 17 Issue: 2

Cite

APA Bayğın, N., & Karaköse, M. (2022). Genetic Algorithm-Based Optimization of Mass Customization Using Hyperledger Fabric Blockchain. Turkish Journal of Science and Technology, 17(2), 451-460. https://doi.org/10.55525/tjst.1145047
AMA Bayğın N, Karaköse M. Genetic Algorithm-Based Optimization of Mass Customization Using Hyperledger Fabric Blockchain. TJST. September 2022;17(2):451-460. doi:10.55525/tjst.1145047
Chicago Bayğın, Nursena, and Mehmet Karaköse. “Genetic Algorithm-Based Optimization of Mass Customization Using Hyperledger Fabric Blockchain”. Turkish Journal of Science and Technology 17, no. 2 (September 2022): 451-60. https://doi.org/10.55525/tjst.1145047.
EndNote Bayğın N, Karaköse M (September 1, 2022) Genetic Algorithm-Based Optimization of Mass Customization Using Hyperledger Fabric Blockchain. Turkish Journal of Science and Technology 17 2 451–460.
IEEE N. Bayğın and M. Karaköse, “Genetic Algorithm-Based Optimization of Mass Customization Using Hyperledger Fabric Blockchain”, TJST, vol. 17, no. 2, pp. 451–460, 2022, doi: 10.55525/tjst.1145047.
ISNAD Bayğın, Nursena - Karaköse, Mehmet. “Genetic Algorithm-Based Optimization of Mass Customization Using Hyperledger Fabric Blockchain”. Turkish Journal of Science and Technology 17/2 (September 2022), 451-460. https://doi.org/10.55525/tjst.1145047.
JAMA Bayğın N, Karaköse M. Genetic Algorithm-Based Optimization of Mass Customization Using Hyperledger Fabric Blockchain. TJST. 2022;17:451–460.
MLA Bayğın, Nursena and Mehmet Karaköse. “Genetic Algorithm-Based Optimization of Mass Customization Using Hyperledger Fabric Blockchain”. Turkish Journal of Science and Technology, vol. 17, no. 2, 2022, pp. 451-60, doi:10.55525/tjst.1145047.
Vancouver Bayğın N, Karaköse M. Genetic Algorithm-Based Optimization of Mass Customization Using Hyperledger Fabric Blockchain. TJST. 2022;17(2):451-60.