Chapter
Dec 9, 2021

A Study on Risk-Predicting Model of Supply Suspension Based on Elman Dynamic Neural Network

Publication: ICCREM 2021

ABSTRACT

Negatively affected by the nonlinear time-serial data of supply chain, the conventional assessment methods have no longer met the expectation of society, and in practice the failure to assess the risk of supply delay and suspension are more and more likely to occur. This study explores how to identify the threat of suppliers’ delay and suspension in dynamic uncertainty and creates assessment index criteria which can be used by experts for professionally measuring the risks of supply delay against dynamic circumstances. Furthermore, an Elman dynamic neural-network model based on risk assessment of supply delay and suspension is generated to examine the historical data from five vendors contracted by a certain corporation for its further learning and training to evaluate their future supply risks in the upcoming quarterly so as to prove the validity of this model.

Get full access to this article

View all available purchase options and get full access to this chapter.

REFERENCES

Burke, G. J., Carrillo, J. E., and Vakharia, A. J. (2009). “Sourcing decisions with stochastic supplier reliability and stochastic demand.” Production and Operations Management, 18(4), 475–484.
Chandra, R. (2015). “Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction.” IEEE Transactions on Neural Networks and Learning Systems, 26(12), 3123–3136.
Coats, P. K., and Fant, L. F. (1999). “Recognizing financial distress patterns using a neural network tool.” Financial Management, 22, 145–155.
How, B. S., and Lam, H. L. (2019). “Pca method for debottlenecking of sustainability performance in integrated biomass supply chain.” Process Integration and Optimization for Sustainability, 3(1), 43–64.
Kumar, M., Vrat, P., and Shankar, R. (2004). “A fuzzy programming approach for vendor selection problem in supply chain.” International Journal of Production Economics, 101(2), 273–285.
Molodtsov, D. (1999). “Soft set theory: first results.” Computers & Mathematics with Applications, 37(4-5), 19–31.
Palmer, S., Gorse, D., and Muk-Pavic, E. (2015). “Neural networks and particle swarm optimization for function approximation in Tri-SWACH hull design.” Proceedings of the 16th International Conference on Engineering Applications of Neural Networks (INNS), Rhodes, Greece, 1–6.
Rajesh, R., and Ravi, V. (2015). “Supplier selection in resilient supply chains: a grey relational analysis approach.” Journal of Cleaner Production, 86, 343–359.
Sheikhan, M., Abbasnezhad, A. M., and Gharavian, D. (2015). “Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study.” Connection Science, 27(4), 340–357.
Wysocki, A., and Ławryńczuk, M. (2016). “Elman neural network for modeling and predictive control of delayed dynamic systems.” Archives of Control Sciences, 26(1), 117–142.

Information & Authors

Information

Published In

Go to ICCREM 2021
ICCREM 2021
Pages: 118 - 131

History

Published online: Dec 9, 2021

Permissions

Request permissions for this article.

Authors

Affiliations

Junyu Zhang [email protected]
1Ph.D. Candidate, Management Science and Engineering, Harbin Institute of Technology, Harbin, China. Email: [email protected]
Yongxiang Wu [email protected]
2Professor, Dept. of Construction and Real Estate, Harbin Institute of Technology, Harbin, China. Email: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Paper
$35.00
Add to cart
Buy E-book
$224.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Paper
$35.00
Add to cart
Buy E-book
$224.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share