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

By Constantino C Reyes-Aldasoro, A R Ganguly, G Lemus and A Gupta

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

Topics: TK
Publisher: Palgrave Macmillan
OAI identifier: oai:sro.sussex.ac.uk:38684
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