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

C C Reyes-Aldasoro, A R Ganguly, G Lemus () and A Gupta ()
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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

Journal of the Operational Research Society, 1999, vol. 50, issue 1, 85-94

Abstract: 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.

Keywords: data mining; dynamic programming; inventory optimisation; neural networks (search for similar items in EconPapers)
Date: 1999
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DOI: 10.1057/palgrave.jors.2600658

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