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Pricing strategy and system performance in a cloud-based manufacturing system built on blockchain technology

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Abstract

Blockchain technology has been enjoying rapid development in numerous areas where trust, safety, and global payments are deemed important. In this research, we introduce a framework that integrates blockchain technology with the emerging concept of cloud manufacturing. It has the potential to dramatically improve the feasibility and applicability of cloud manufacturing. Our focus is on the pricing strategies for service providers in bidding for jobs in such a system. A scenario is developed based on cloud additive manufacturing services. A game theoretic approach for pricing simulation is proposed in the research, and a fuzzy algorithm is employed in the pricing decision. Two pricing policies are considered: one is that the information of players is unknown to each other, and the other is that the information of players who are within a limit of Euclidean space is known to each other. The effects of system load and the weight coefficients in KNN recommendation algorithm are also studied. The results show that the pricing policy has significant effect on revenue generation for providers, and the increase of system load helps to grow revenue for providers only when the machine utilization is low and job waiting time is short. In addition, the system seems to be less sensitive to the change of KNN weights in terms of individual provider and system revenues.

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References

  • Adeniyi, D. A., Wei, Z., & Yongquan, Y. (2016). Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method. Applied Computing and Informatics, 12(1), 90–108.

    Google Scholar 

  • Anjum, A., Sporny, M., & Sill, A. (2017). Blockchain standards for compliance and trust. IEEE Cloud Computing, 4(4), 84–90.

    Google Scholar 

  • Anselmi, J., Ardagna, D., Lui, J., Wierman, A., Xu, Y., & Yang, Z. (2017). The economics of the cloud. ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS), 2(4), 18.

    Google Scholar 

  • Bahga, A., & Madisetti, V. K. (2016). Blockchain platform for industrial internet of things. Journal of Software Engineering and Applications, 9, 533–546.

    Google Scholar 

  • Cai, Y., & Zhu, D. (2016). Fraud detections for online businesses: a perspective from blockchain technology. Financial Innovation, 2(1), 20.

    Google Scholar 

  • Chen, T., & Wu, H. C. (2017). A new cloud computing method for establishing asymmetric cycle time intervals in a wafer fabrication factory. Journal of Intelligent Manufacturing, 28, 1095. https://doi.org/10.1007/s10845-015-1052-6.

    Article  Google Scholar 

  • Christidis, K., & Devetsikiotis, M. (2016). Blockchains and smart contracts for the internet of things. IEEE Access, 4, 2292–2303.

    Google Scholar 

  • Coupek, D., Lechler, A., & Verl, A. (2017). Cloud-based control strategy: Downstream defect reduction in the production of electric motors. IEEE Transactions on Industry Applications, 53(6), 5348–5353.

    Google Scholar 

  • Cui, L., Ou, P., Fu, X., Wen, Z., & Lu, N. (2017). A novel multi-objective evolutionary algorithm for recommendation systems. Journal of Parallel and Distributed Computing, 103, 53–63.

    Google Scholar 

  • De Angelis, S., Aniello, L., Baldoni, R., Lombardi, F., Margheri, A., & Sassone, V. (2018). PBFT vs proof-of-authority: applying the cap theorem to permissioned blockchain. In Proceedings of Italian conference on cyber security. Retrived Nov 20, 2019, from https://eprints.soton.ac.uk/id/eprint/415083.

  • Deng, J., Guo, J., & Wang, Y. (2019). A Novel K-medoids clustering recommendation algorithm based on probability distribution for collaborative filtering. Knowledge-Based Systems, 175, 96–106.

    Google Scholar 

  • Feng, Y., & Huang, B. (2018). Cloud manufacturing service QoS prediction based on neighbourhood enhanced matrix factorization. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1409-8.

    Article  Google Scholar 

  • Galbraith, C. S., Rodriguez, C. L., & DeNoble, A. F. (2008). SME competitive strategy and location behavior: An exploratory study of high-technology manufacturing. Journal of Small Business Management, 46(2), 183–202.

    Google Scholar 

  • Gao, F., Zhu, L., Shen, M., Sharif, K., Wan, Z., & Ren, K. (2018). A blockchain-based privacy-preserving payment mechanism for vehicle-to-grid networks. IEEE Network, 36(6), 184–192.

    Google Scholar 

  • Gu, P., Balasubramanian, S., & Norrie, D. H. (1997). Bidding-based process planning and scheduling in a multi-agent system. Computers & Industrial Engineering, 32(2), 477–496.

    Google Scholar 

  • Hellendoorn, H., & Thomas, C. (1993). Defuzzification in fuzzy controllers. Journal of Intelligent & Fuzzy Systems, 1(2), 109–123.

    Google Scholar 

  • Huang, X., Zhang, Y., Li, D., & Han, L. (2019). An optimal scheduling algorithm for hybrid EV charging scenario using consortium blockchains. Future Generation Computer Systems, 91, 555–562.

    Google Scholar 

  • Jin, X., Yu, S., Zheng, P., Liu, Q., & Xu, X. (2018). Cloud-based approach for smart product personalization. Procedia CIRP, 72, 922–927.

    Google Scholar 

  • Jung, K. S., & Hwang, H. J. (2011). Competition and cooperation in a remanufacturing system with take-back requirement. Journal of Intelligent Manufacturing, 22, 427. https://doi.org/10.1007/s10845-009-0300-z.

    Article  Google Scholar 

  • Khosroshahi, H., Rasti-Barzoki, M., & Hejazi, S. R. (2019). A game theoretic approach for pricing decisions considering CSR and a new consumer satisfaction index using transparency-dependent demand in sustainable supply chains. Journal of Cleaner Production, 208, 1065–1080.

    Google Scholar 

  • Kreye, M. E., Newnes, L. B., & Goh, Y. M. (2013). Information availability at the competitive bidding stage for service contracts. Journal of Manufacturing Technology Management, 24(7), 976–997.

    Google Scholar 

  • Kshetri, N. (2017). Blockchain’s roles in strengthening cybersecurity and protecting privacy. Telecommunications Policy, 41(10), 1027–1038.

    Google Scholar 

  • Kusiak, A. J. (2020). Service manufacturing = Process-as-a-service + manufacturing operations-as-a-service. Journal of Intelligent Manufacturing, 31, 1. https://doi.org/10.1007/s10845-019-01527-3.

    Article  Google Scholar 

  • Leiding, B., Memarmoshrefi, P., & Hogrefe, D. (2016). Self-managed and blockchain-based vehicular ad-hoc networks. In Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing: Adjunct (pp. 137–140). ACM.

  • Letenneur, M., Kreitcberg, A., & Brailovski, V. (2019). Optimization of laser powder bed fusion processing using a combination of melt pool modeling and design of experiment approaches: Density control. Journal of Manufacturing and Materials Processing., 3(1), 21. https://doi.org/10.3390/jmmp3010021.

    Article  Google Scholar 

  • Li, F., Liao, T. W., & Zhang, L. (2019). Two-level multi-task scheduling in a cloud manufacturing environment. Robotics and Computer-Integrated Manufacturing, 56, 127–139.

    Google Scholar 

  • Li, X., Shi, D., Charastrakul, V., et al. (2009). Advanced P-Tree based K-Nearest neighbors for customer preference reasoning analysis. Journal of Intelligent Manufacturing, 20, 569. https://doi.org/10.1007/s10845-008-0146-9.

    Article  Google Scholar 

  • Li, Z., Barenji, A. V., & Huang, G. Q. (2018). Toward a blockchain cloud manufacturing system as a peer to peer distributed network platform. Robotics and Computer-Integrated Manufacturing, 54, 133–144.

    Google Scholar 

  • Lim, K. Y. H., Zheng, P., & Chen, C. H. (2019). A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-019-01512-w.

    Article  Google Scholar 

  • Lim, M. K., & Zhang, Z. (2012). A multi-agent system using iterative bidding mechanism to enhance manufacturing agility. Expert Systems with Applications, 39(9), 8259–8273.

    Google Scholar 

  • Lu, Q., & Xu, X. (2017). Adaptable blockchain-based systems: a case study for product traceability. IEEE Software, 34(6), 21–27.

    Google Scholar 

  • Lu, Y., Wang, H., & Xu, X. (2019). ManuService ontology: a product data model for service-oriented business interactions in a cloud manufacturing environment. Journal of Intelligent Manufacturing, 30(1), 317–334.

    Google Scholar 

  • Lu, Y., & Xu, X. (2019). Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services. Robotics and Computer-Integrated Manufacturing, 57, 92–102.

    Google Scholar 

  • Mai, J., Zhang, L., Tao, F., & Ren, L. (2016). Customized production based on distributed 3D printing services in cloud manufacturing. The International Journal of Advanced Manufacturing Technology, 84(1–4), 71–83.

    Google Scholar 

  • Masood, S. H., & Lim, B. S. (1995). Concurrent intelligent rapid prototyping environment. Journal of Intelligent Manufacturing, 6, 291. https://doi.org/10.1007/BF00124674.

    Article  Google Scholar 

  • Mazzola, L., Waibel, P., Kaphanke, P., & Klusch, M. (2018). Smart process optimization and adaptive execution with semantic services in cloud manufacturing. Information, 9(11), 279.

    Google Scholar 

  • Meng, Q. N., & Xu, X. (2018). Price forecasting using an ACO-based support vector regression ensemble in cloud manufacturing. Computers & Industrial Engineering, 125, 171–177.

    Google Scholar 

  • Muzammal, M., Qu, Q., & Nasrulin, B. (2019). Renovating blockchain with distributed databases: an open source system. Future Generation Computer Systems, 90, 105–117.

    Google Scholar 

  • Nara, K., Shiose, A., Kitagawa, M., & Ishihara, T. (1992). Implementation of genetic algorithm for distribution systems loss minimum re-configuration. IEEE Transactions on Power Systems, 7(3), 1044–1051.

    Google Scholar 

  • Nike News, (2019). Nike flyprint is the first performance 3D printed textile upper. Retrieved February 12, 2020 from https://news.nike.com/news/nike-flyprint-3d-printed-textile.

  • Ostrosi, E., & Fougères, A. J. (2018). Intelligent virtual manufacturing cell formation in cloud-based design and manufacturing. Engineering Applications of Artificial Intelligence, 76, 80–95.

    Google Scholar 

  • Pass, R., & Shi, E. (2017, July). Fruitchains: A fair blockchain. In Proceedings of the ACM symposium on principles of distributed computing (pp. 315–324). ACM.

  • Puttonen, J., Lobov, A., Soto, M. A. C., et al. (2019). Cloud computing as a facilitator for web service composition in factory automation. Journal of Intelligent Manufacturing, 30, 687. https://doi.org/10.1007/s10845-016-1277-z.

    Article  Google Scholar 

  • Samworth, R. J. (2012). Optimal weighted nearest neighbour classifiers. The Annals of Statistics, 40(5), 2733–2763.

    Google Scholar 

  • Takano, Y., Ishii, N., & Muraki, M. (2014). A sequential competitive bidding strategy considering inaccurate cost estimates. Omega, 42(1), 132–140.

    Google Scholar 

  • Talhi, A., Fortineau, V., Huet, J. C., et al. (2019). Ontology for cloud manufacturing based product lifecycle management. Journal of Intelligent Manufacturing, 30, 2171. https://doi.org/10.1007/s10845-017-1376-5.

    Article  Google Scholar 

  • Tan, S. (2006). An effective refinement strategy for KNN text classifier. Expert Systems with Applications, 30(2), 290–298.

    Google Scholar 

  • Tao, F., Zhang, L., Venkatesh, V. C., Luo, Y., & Cheng, Y. (2011). Cloud manufacturing: a computing and service-oriented manufacturing model. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 225(10), 1969–1976.

    Google Scholar 

  • Teixidor, D., Grzenda, M., Bustillo, A., et al. (2015). Modeling pulsed laser micromachining of micro geometries using machine-learning techniques. Journal of Intelligent Manufacturing, 26, 801. https://doi.org/10.1007/s10845-013-0835-x.

    Article  Google Scholar 

  • Theodosiou, M., & Katsikeas, C. S. (2001). Factors influencing the degree of international pricing strategy standardization of multinational corporations. Journal of International Marketing, 9(3), 1–18.

    Google Scholar 

  • Viriyasitavat, W., Da Xu, L., Bi, Z., et al. (2018). Blockchain-based business process management (BPM) framework for service composition in industry 4.0. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1422-y.

    Article  Google Scholar 

  • Viriyasitavat, W., & Hoonsopon, D. (2019). Blockchain characteristics and consensus in modern business processes. Journal of Industrial Information Integration, 13, 32–39.

    Google Scholar 

  • Wang, L., Chen, X., & Liu, Q. (2017). A lightweight intelligent manufacturing system based on cloud computing for plate production. Mobile Networks and Applications, 22(6), 1170–1181.

    Google Scholar 

  • Wang, S. L., Zhu, Z. Q., & Kang, L. (2016). Resource allocation model in cloud manufacturing. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 230(10), 1726–1741.

    Google Scholar 

  • Wang, Y., & Zhang, Y. (2017). Remanufacturer’s production strategy with capital constraint and differentiated demand. Journal of Intelligent Manufacturing, 28(4), 869–882. https://doi.org/10.1007/s10845-014-1006-4.

    Article  Google Scholar 

  • Wu, D., Greer, M. J., Rosen, D. W., & Schaefer, D. (2013). Cloud manufacturing: Strategic vision and state-of-the-art. Journal of Manufacturing Systems, 32(4), 564–579.

    Google Scholar 

  • Wu, M., Song, Z., & Moon, Y. B. (2019). Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods. Journal of Intelligent Manufacturing, 30, 1111. https://doi.org/10.1007/s10845-017-1315-5.

    Article  Google Scholar 

  • Xie, C., Cai, H., Xu, L., Jiang, L., & Bu, F. (2017). Linked semantic model for information resource service toward cloud manufacturing. IEEE Transactions on Industrial Informatics, 13(6), 3338–3349.

    Google Scholar 

  • Xu, J., & Liu, N. (2017). Research on closed loop supply chain with reference price effect. Journal of Intelligent Manufacturing, 28, 51. https://doi.org/10.1007/s10845-014-0961-0.

    Article  Google Scholar 

  • Yao, X., Zhou, J., Lin, Y., et al. (2019). Smart manufacturing based on cyber-physical systems and beyond. Journal of Intelligent Manufacturing, 30, 2805. https://doi.org/10.1007/s10845-017-1384-5.

    Article  Google Scholar 

  • Yao, Y., Chen, D., Wang, L., & Yang, X. (2016). Additive manufacturing cloud via peer-robot collaboration. International Journal of Advanced Robotic Systems, 13(3), 97.

    Google Scholar 

  • Yoo, M., & Won, Y. (2018). A study on the transparent price tracing system in supply chain management based on blockchain. Sustainability, 10(11), 4037.

    Google Scholar 

  • Yuan, M., Yu, H., Huang, J., et al. (2019). Reconfigurable assembly line balancing for cloud manufacturing. Journal of Intelligent Manufacturing, 30, 2391. https://doi.org/10.1007/s10845-018-1398-7.

    Article  Google Scholar 

  • Zheng, P., Xu, X., & Chen, C. H. (2018). A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1430-y.

    Article  Google Scholar 

  • Zheng, P., Xu, X., Yu, S., & Liu, C. (2017). Personalized product configuration framework in an adaptable open architecture product platform. Journal of Manufacturing Systems, 43, 422–435.

    Google Scholar 

  • Zhu, X., Shi, J., & Lu, C. (2019). Cloud health resource sharing based on consensus-oriented blockchain technology: Case study on a breast tumor diagnosis service. Journal of Medical Internet Research, 21(7), e13767.

    Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical and Materials Engineering, College of Engineering and Applied Science, University of Cincinnati, Cincinnati, OH, 45221, USA

    Xiaobao Zhu & Jing Shi

  2. School of Modern Posts, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, China

    Fengjie Xie

  3. Lindner College of Business, University of Cincinnati, Cincinnati, OH, 45221, USA

    Rouqi Song

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  1. Xiaobao Zhu
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Zhu, X., Shi, J., Xie, F. et al. Pricing strategy and system performance in a cloud-based manufacturing system built on blockchain technology. J Intell Manuf 31, 1985–2002 (2020). https://doi.org/10.1007/s10845-020-01548-3

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