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A Cloud-based Decision Support System Framework for Order Planning and Tracking

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Proceedings of the Seventh International Conference on Management Science and Engineering Management

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 241))

Abstract

This paper presents a cloud-based decision support system framework for order planning and tracking in a distributed manufacturing environment. Under this framework, computational intelligent techniques are employed to generate order planning decisions while RFID and cloud computing technologies are utilized to capture real-time production records and make remote production order tracking. On the basis of this framework, a pilot system was developed and implemented in a distributed manufacturing company, which reported distinct reductions in production costs and increases in production efficiency. The system framework is also easy-to-extend to integrate wider operations processes in supply chain.

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References

  1. Smitha KK, Thomas T, Chitharanjan K (2012) Cloud based e-governance system: A survey. Procedia Engineering 38:3816–3823

    Google Scholar 

  2. Xu X (2012) From cloud computing to cloud manufacturing. Robotics and Computer-Integrated Manufacturing 28:75–86

    Google Scholar 

  3. Lai W, Tam T, Chan S (2012) Knowledge cloud system for network collaboration: A case study in medical service industry in China. Expert Systems with Applications 39:12205–12212

    Google Scholar 

  4. Sultan N (2010) Cloud computing for education: A new dawn? International Journal of Information Management 30:109–116

    Google Scholar 

  5. Dolgui A, Prodhon C (2007) Supply planning under uncertainties in MRP environments: A state of the art. Annual Reviews in Control 31:269–279

    Google Scholar 

  6. Wang L, Keshavarzmanesh S, Feng H et al (2009) Assembly process planning and its future in collaborative manufacturing: A review. The International Journal of Advanced Manufacturing Technology 41:132–144

    Google Scholar 

  7. Wazed M, Ahmed S, Nukman Y (2010) A review of manufacturing resources planning models under different uncertainties: State-of-the-art and future directions. South African Journal of Industrial Engineering 21:17–33

    Google Scholar 

  8. Sakallı Ü, Baykoc, Ö F, Birgören B (2010) A possibilistic aggregate production planning model for brass casting industry. Production Planning & Control 21:319–338

    Google Scholar 

  9. Engin O, Ceran G, Yilmaz MK (2011) An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems. Applied Soft Computing 11:3056–3065

    Google Scholar 

  10. Hu XF,Wu EF, Bao JS et al (2010) A branch-and-bound algorithm to minimize the line length of a two-sided assembly line. European Journal of Operational Research 206:703–707

    Google Scholar 

  11. Guo ZX, Wong WK, Leung SYS et al (2012) Applications of artificial intelligence in the apparel industry: A review. Textile Research Journal 81:1871–1892

    Google Scholar 

  12. Axsater S (2005) Planning order releases for an assembly system with random operation times. OR Spectrum 27:459–470

    Google Scholar 

  13. Chen Z, Pundoor G (2006) Order assignment and scheduling in a supply chain. Operations Research 54:555–572

    Google Scholar 

  14. Guo ZX, Wong WK, Leung SYS (2013) A hybrid intelligent model for order allocation planning in make-to-order manufacturing. Applied Soft Computing 13:1376–1390

    Google Scholar 

Download references

Acknowledgments

This work was funded by the Sichuan University (Grant No. SKYB201301) and the National Natural Science Foundation of China (Grant Nos. 71020107027, 71172197).

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Authors and Affiliations

  1. Business School, Sichuan University, Chengdu, 610065, People’s Republic of China

    Zhaoxia Guo & Chunxiang Guo

Authors
  1. Zhaoxia Guo
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  2. Chunxiang Guo
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Corresponding author

Correspondence to Zhaoxia Guo .

Editor information

Editors and Affiliations

  1. Business School, Sichuan University, Chengdu, China, People's Republic

    Jiuping Xu

  2. Drexel University, Philadelphia, USA

    John A. Fry

  3. LeBow College of Business Department of Decision Sciences, Drexel University, Philadelphia, Pennsylvania, USA

    Benjamin Lev

  4. Azerbaijan National Academy of Sciences, Institute of Cybernetics, Baku, Azerbaijan

    Asaf Hajiyev

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Guo, Z., Guo, C. (2014). A Cloud-based Decision Support System Framework for Order Planning and Tracking. In: Xu, J., Fry, J., Lev, B., Hajiyev, A. (eds) Proceedings of the Seventh International Conference on Management Science and Engineering Management. Lecture Notes in Electrical Engineering, vol 241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40078-0_7

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