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

Reyes-Aldasoro, Constantino C, Ganguly, A R, Lemus, G and Gupta, A (1999) A hybrid model based on dynamic programming, neural networks, and surrogate value for inventory optimisation applications. Journal of the Operational Research Society, 50 (1). pp. 85-94. ISSN 0160-5682

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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.

Item Type: Article
Additional Information: 169QR Times Cited:1 Cited References Count:32
Keywords: data mining dynamic programming inventory optimisation neural networks lost sales demand system
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Depositing User: Constantino Reyes Aldasoro
Date Deposited: 24 Apr 2012 12:55
Last Modified: 30 Nov 2012 17:12
URI: http://sro.sussex.ac.uk/id/eprint/38684
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