Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-26T04:10:44.819Z Has data issue: false hasContentIssue false
  • English
  • Français

Data-driven process planning for shipbuilding

Published online by Cambridge University Press:  31 January 2017

Jinsong Bao ,
Xiaohu Zheng ,
Jianguo Zhang ,
Xia Ji  and
Jie Zhang
Jinsong Bao
Affiliation:
School of Mechanical Engineering, Donghua University, Shanghai, China Shanghai Engineering Center of Process and Equipment for Aerospace Devices Manufacturing, Shanghai, China
Xiaohu Zheng*
Affiliation:
School of Mechanical Engineering, Donghua University, Shanghai, China Shanghai Engineering Center of Process and Equipment for Aerospace Devices Manufacturing, Shanghai, China
Jianguo Zhang
Affiliation:
School of Mechanical Engineering, Donghua University, Shanghai, China
Xia Ji
Affiliation:
School of Mechanical Engineering, Donghua University, Shanghai, China
Jie Zhang
Affiliation:
School of Mechanical Engineering, Donghua University, Shanghai, China
*
Reprint requests to: Xiaohu Zheng, School of Mechanical Engineering, Donghua University, Number 2999 North Renmin Road 201620, Songjiang District, Shanghai, China. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Erection planning in shipbuilding is a highly complex process. When a process change happens for some reason, it is often difficult to identify how many factors are affected and estimate how sensitive these factors can be. To optimize the planning and replanning of the shipbuilding plan for the best production performance, a data-driven approach for shipbuilding erection planning is proposed, which is composed of an erection plan model, identification of major factors related to the erection plan, and a data-driven algorithm to apply shipbuilding operation data for creating plans and forecasting, for plan adjustment, future availabilities of shipyard resources including machines, equipment, and man power. Through data clustering, the relevant factors are identified as a result of plan change, and critical equipment health management is carried out through data-driven anomaly detection. A case study is implemented, and the result shows that the proposed data-driven method is able to reschedule the shipbuilding plans smoothly.

Keywords

Anomaly DetectionIndustrial Big DataOptimizationRescheduleShipbuilding Plan
Type
Technical Brief Paper
Information
AI EDAM , Volume 32 , Issue 1 , February 2018 , pp. 122 - 130
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Ateca-Amestoy, V., & Prieto-Rodriguez, J. (2013). Forecasting accuracy of behavioural models for participation in the arts. European Journal of Operational Research 229 (1), 124–131.CrossRefGoogle Scholar
Azab, A., & Naderi, B. (2015). Modelling the problem of production scheduling for reconfigurable manufacturing systems. Procedia CIRP 33, 76–80.Google Scholar
Başak, O., & Albayrak, Y.E. (2014). Petri net based decision system modeling in real-time scheduling and control of flexible automotive manufacturing systems. Computers & Industrial Engineering 86, 116–126.Google Scholar
Baydar, C.M., & Saitou, K. (2001). Automated generation of robust error recovery logic in assembly systems using genetic programming. Journal of Manufacturing Systems 20 (1), 55–68.CrossRefGoogle Scholar
Choudhary, A.K., Harding, J.A., & Tiwari, M.K. (2008). Data mining in manufacturing: a review based on the kind of knowledge. Journal of Intelligent Manufacturing 20 (5), 501–521.Google Scholar
Deutsch, T. (2014). Analytics Hype: The Next Wave in Big Data Backlash. Accessed at http://www.ibmbigdatahub.com/blog/analytics-hype-next-wave-big-data-backlash Google Scholar
Esmaeilian, B., Behdad, S., & Wang, B. (2016). The evolution and future of manufacturing: a review. Journal of Manufacturing Systems 39, 79–100.CrossRefGoogle Scholar
Gordon, A.D. (1987). A review of hierarchical classification. Journal of the Royal Statistical Society 150 (2), 119–137.Google Scholar
Hey, T. (2012). The Fourth Paradigm—Data-Intensive Scientific Discovery. Berlin: Springer.Google Scholar
Huang, G.Q., Zhong, R.Y., & Tsui, K.L. (2015). Special issue on “big data for service and manufacturing supply chain management.” International Journal of Production Economics 165, 172–173.Google Scholar
Jain, S., Triantis, K.P., & Liu, S. (2011). Manufacturing performance measurement and target setting: a data envelopment analysis approach. European Journal of Operational Research 214 (3), 616–626.CrossRefGoogle Scholar
Jin, X., Wah, B.W., Cheng, X., & Wang, Y. (2015). Significance and challenges of big data research. Big Data Research 2 (2), 59–64.Google Scholar
Kim, Y.-S., Yang, J., & Han, S. (2006). A multichannel visualization module for virtual manufacturing. Computers in Industry 57 (7), 653–662.Google Scholar
Lee, J., Kao, H.-A., & Yang, S. (2014). Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP 16, 3–8.Google Scholar
Lee, J., Lapira, E., Bagheri, B., & Kao, H.A. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters 1 (1), 38–41.Google Scholar
Lin, J.T., & Chen, C.M. (2015). Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing. Simulation Modelling Practice & Theory 51, 100–114.Google Scholar
Moseley, B., Dasgupta, A., Kumar, R., & Sarlós, T. (2011). On scheduling in map-reduce and flow-shops. Proc. 23rd Annual ACM Symp. Parallelism in Algorithms and Architectures, pp. 289–298. New York: ACM.CrossRefGoogle Scholar
Stockton, D.J., Khalil, R.A., & Mukhongo, L.M. (2012). Cost model development using virtual manufacturing and data mining: part II—comparison of data mining algorithms. International Journal of Advanced Manufacturing Technology 66 (9–12), 1389–1396.Google Scholar
Wang, R.Y., Storey, V.C., & Firth, C.P. (1995). A framework for analysis of data quality research. IEEE Transactions on Knowledge and Data Engineering 7 (4), 623–640.Google Scholar