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A hybrid model based on dynamic programming, neural networks, and surrogate value for inventory optimisation applications

Author

Listed:
  • C C Reyes-Aldasoro

    (Instituto Tecnológico Autónomo de México)

  • A R Ganguly

    (Massachusetts Institute of Technology)

  • G Lemus

    (Massachusetts Institute of Technology)

  • A Gupta

    (Massachusetts Institute of Technology)

Abstract

This paper proposes a new approach to minimise inventory levels and their associated costs within large geographically dispersed organisations. For such organisations, attaining a high degree of agility is becoming increasingly important. Linear regression-based tools have traditionally been employed to assist human experts in inventory optimisation; endeavours; recently, Neural Network (NN) techniques have been proposed for this domain. The objective of this paper is to create a hybrid framework that can be utilised for analysis, modelling and forecasting purposes. This framework combines two existing approaches and introduces a new associated cost parameter that serves as a surrogate for customer satisfaction. The use of this hybrid framework is described using a running example related to a large geographically dispersed organisation.

Suggested Citation

  • C C Reyes-Aldasoro & A R Ganguly & G Lemus & A Gupta, 1999. "A hybrid model based on dynamic programming, neural networks, and surrogate value for inventory optimisation applications," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(1), pages 85-94, January.
    • Handle: RePEc:pal:jorsoc:v:50:y:1999:i:1:d:10.1057_palgrave.jors.2600658
    DOI: 10.1057/palgrave.jors.2600658
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    Cited by:

    1. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.

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