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Applied Intelligence

, Volume 48, Issue 5, pp 1394–1405 | Cite as

The promotion strategy of supply chain flexibility based on deep belief network

  • Fanhui Kong
  • Jian Li
Article

Abstract

Supply chain flexibility is the processing ability of the enterprize to deal with the uncertain environment of supply and demand. In this paper, we consider the supply side (node interrupt) and demand side (demand fluctuations) under uncertain environment. By using the deep belief network (DBN), which is composed of multilayer Restricted Boltzmann Machine (RBM), it establishes the supply chain flexibility network with optimization of the transfer node and flow. The deep belief network is trained by the data of large manufacturing enterprize, compared with the traditional neural network (MLR, BP and GA). The results show that the deep belief network model overcomes the shortcomings of the traditional neural networks, such as easy to fall into local optimum, long training time and low function fitting degree, and it has higher prediction accuracy. This network model based on the deep belief network can promote the supply chain flexibility more, when supply and demand fluctuations occur.

Keywords

Supply chain flexibility Deep belief network Restricted Boltzmann Machine Promotion strategy 

Notes

Acknowledgments

This work was partially supported by Key projects for the Chinese Ministry of Education (No. 15JZD021), and Tianjin higher education innovation team training program (No. TD12-5013).

References

  1. 1.
    Slack N (1987) The flexibility of manufacturing systems. Int J Oper Prod Manag 25(4):1190–1200Google Scholar
  2. 2.
    Sun Y, Song H, Jara AJ et al (2016) Internet of things and big data analytics for smart and connected communities. IEEE Access 4:1–1CrossRefGoogle Scholar
  3. 3.
    Lee H, Grosse R, Ranganath R et al (2009) Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: International conference on machine learning, ICML 2009. DBLP, Montreal, pp 609–616Google Scholar
  4. 4.
    Ding G, Wang L, Wu Q (2013) Big data analytics in future internet of things. Comput Sci 9:430–434Google Scholar
  5. 5.
    Lummus RR, Duclos LK, Vokurka RJ (2003) Supply chain flexibility: building a new model. Glob J Flex Syst Manag 4(4):1–13Google Scholar
  6. 6.
    Ji NN, Zhang JS, Zhang CX (2014) A sparse-response deep belief network based on rate distortion theory. Pattern Recogn 47(9):3179–3191CrossRefGoogle Scholar
  7. 7.
    Nagarajan V, Savitskie K, Ranganathan S et al (2013) The effect of environmental uncertainty, information quality, and collaborative logistics on supply chain flexibility of small manufacturing firms in India. Asia Pac J Mark Logist 25(5):784–802CrossRefGoogle Scholar
  8. 8.
    Ruekert RW, Churchill GA (1984) Reliability and validity of alternative measures of channel member satisfaction. J Mark Res 21(2):226–233CrossRefGoogle Scholar
  9. 9.
    Mohamed AR, Sainath TN, Dahl G et al (2011) Deep belief networks using discriminative features for phone recognition. In: IEEE international conference on acoustics, speech, and signal processing, ICASSP 2011, Prague Congress Center, Prague, Czech Republic. DBLP, pp 5060–5063Google Scholar
  10. 10.
    Vickery SN, Calantone R, Droge C (1999) Supply chain flexibility: an empirical study. J Supply Chain Manag 35(2):16–24CrossRefGoogle Scholar
  11. 11.
    El FR, Saliba W, Mortada R (2011) The impact of yield-dependent trading costs on pricing and production planning under supply and demand uncertainty. Manuf Serv Oper Manag 13(3):404–417CrossRefGoogle Scholar
  12. 12.
    Ghahabi O, Hernando J (2014) Deep belief networks for i-vector based speaker recognition. In: IEEE international conference on acoustics, speech and signal processing. IEEE, pp 1700–1704Google Scholar
  13. 13.
    Kuremoto T, Kimura S, Kobayashi K et al (2014) Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing 137(15):47C56Google Scholar
  14. 14.
    Liu B (2010) Uncertain risk analysis and uncertain reliability analysis. J Uncertain Syst 4(4):163–170Google Scholar
  15. 15.
    Brosch T, Tam R (2015) Efficient training of convolutional deep belief networks in the frequency domain for application to high-resolution 2d and 3d images. Neural Comput 27(1):211–227CrossRefGoogle Scholar
  16. 16.
    Snyder LV, Daskin MS (2005) Reliability models for facility location: the expected failure cost case. Transp Sci 39(3):400–416CrossRefGoogle Scholar
  17. 17.
    Chanta S, Mayorga ME, Mclay LA (2014) Improving emergency service in rural areas: a biobjective covering location model for EMS systems. Ann Oper Res 221(1):133–159MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Hatefi SM, Jolai F (2014) Robust and reliable forwardCreverse logistics network design under demand uncertainty and facility disruptions. Appl Math Modell 38(9C10):2630–2647CrossRefGoogle Scholar
  19. 19.
    Amin-Naseri MR, Khojasteh MA (2015) Price competition between two leaderCfollower supply chains with risk-averse retailers under demand uncertainty. Int J Adv Manuf Technol 79(1):377–393CrossRefGoogle Scholar
  20. 20.
    Song JS, Yano CA, Lerssrisuriya P (2000) Contract assembly: dealing with combined supply lead time and demand quantity uncertainty. Manuf Serv Oper Manag 2(3):287–296CrossRefGoogle Scholar
  21. 21.
    Kazaz B, Webster S (2015) Technical noteprice-setting newsvendor problems with uncertain supply and risk aversion. Oper Res 63(4)Google Scholar
  22. 22.
    Sanchez AM, Perez MP (2005) Supply chain flexibility and firm performance. Int J Oper Prod Manag 25 (7):681–700CrossRefGoogle Scholar
  23. 23.
    Tsay AA, Lovejoy WS (1999) Quantity flexibility contracts and supply chain performance. Manuf Serv Oper Manag 1(2):89–111CrossRefGoogle Scholar
  24. 24.
    Prater E, Biehl M, Smith MA (2001) International supply chain agility tradeoffs between flexibility and uncertainty. Int J Oper Prod Manag 21(5/6):823–839(17)CrossRefGoogle Scholar
  25. 25.
    Swafford PM, Ghosh S, Murthy N (2008) Achieving supply chain agility through IT integration and flexibility. Int J Prod Econ 116(2):288–297CrossRefGoogle Scholar
  26. 26.
    Stevenson M, Spring M (2007) Flexibility from a supply chain perspective: definition and review. Int J Oper Prod Manag 27(7):685–713CrossRefGoogle Scholar
  27. 27.
    Gosain S, Malhotra A, El Sawy OA (2005) Coordinating for flexibility in e-business supply chains. J Manag Inf Syst 21(3):7–46CrossRefGoogle Scholar
  28. 28.
    Tang C, Tomlin B (2008) The power of flexibility for mitigating supply chain risks. Int J Prod Econ 116 (1):12–27CrossRefGoogle Scholar
  29. 29.
    Hinton G, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Mohamed AR, Dahl GE, Hinton G (2012) Acoustic modeling using deep belief networks. IEEE Trans Audio Speech Lang Process 20(1):14–22CrossRefGoogle Scholar
  31. 31.
    Ranzato M, Boureau YL, Lecun Y (2007) Sparse feature learning for deep belief networks. In: Advances in neural information processing systems, pp 1185–1192Google Scholar
  32. 32.
    Roux NL, Bengio Y (1989) Representational power of restricted boltzmann machines and deep belief networks. Neural Comput 20(6):1631–1649MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Sainath TN, Kingsbury B, Ramabhadran B et al (2011) Making deep belief networks effective for large vocabulary continuous speech recognition. In: Automatic speech recognition and understanding. IEEE, pp 30–35Google Scholar
  34. 34.
    Niggemann O, Biswas G, Kinnebrew JS et al (2015) Data-driven monitoring of cyberPhysical systems leveraging on big data and the Internet-of-Things for diagnosis and control. International Workshop on the Principles of DiagnosisGoogle Scholar
  35. 35.
    Pahlavan K, Krishnamurthy P, Geng Y (2015) Localization challenges for the emergence of the smart world. Access IEEE 3:3058–3067CrossRefGoogle Scholar
  36. 36.
    Immonen A, Palviainen M, Ovaska E (2014) Requirements of an open data based business ecosystem. IEEE Access 2:88–103CrossRefGoogle Scholar
  37. 37.
    Fatemi M, Haykin S (2014) Cognitive control: theory and application. IEEE Access 2:698–710CrossRefGoogle Scholar
  38. 38.
    Anderson JW, Kennedy KE, Ngo LB et al (2014) Synthetic data generation for the internet of things. In: IEEE international conference on big data. IEEE, pp 171–176Google Scholar
  39. 39.
    Niyato D, Alsheikh MA, Wang P et al (2016) Market model and optimal pricing scheme of big data and Internet of Things (IoT)Google Scholar
  40. 40.
    Duclos LK, Vokurka RJ, Lummus RR (2003) A conceptual model of supply chain flexibility. Ind Manag Data Syst 103(6):446–456CrossRefGoogle Scholar
  41. 41.
    Sang S (2016) Revenue sharing contract in a multi-echelon supply chain with fuzzy demand and asymmetric information. Int J Comput Intell Syst 9(6):1028–1040CrossRefGoogle Scholar
  42. 42.
    Mashinchi MR, Selamat A, Ibrahim S (2015) Evaluating extant uranium: linguistic reasoning by fuzzy artificial neural networks, pp 296–307Google Scholar
  43. 43.
    Mashinchi MR, Selamat A (2009) An improvement on genetic-based learning method for fuzzy artificial neural networks. Appl Soft Comput 9(4):1208–1216CrossRefGoogle Scholar
  44. 44.
    Acharya UR, Fujita H, Lih OS et al (2017) Automated detection of arrhythmias using different intervals of Tachycardia ECG segments with convolutional neural network. Inf Sci 405:81–90CrossRefGoogle Scholar
  45. 45.
    Wu J, Chiclana F, Fujita H et al (2017) A visual interaction consensus model for social network group decision making with trust propagation. Knowl-Based Syst 122:39–50CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of ManagementTianjin University of TechnologyTianjinPeople’s Republic of China
  2. 2.College of Management and EconomicsTianjin UniversityTianjinPeople’s Republic of China

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