Managing intermittent demand is a vital task in several industrial contexts, and good forecasting ability is a fundamental prerequisite for an efficient inventory control system in stochastic environments. In recent years, research has been conducted on single-hidden layer feedforward neural networks, with promising results. In particular, back-propagation has been adopted as a gradient descent-based algorithm for training networks. However, when managing a large number of items, it is not feasible to optimize networks at item level, due to the effort required for tuning the parameters during the training stage. A simpler and faster learning algorithm, called the extreme learning machine, has been therefore proposed in the literature to address this issue, but it has never been tried for forecasting intermittent demand. On the one hand, an extensive comparison of single-hidden layer networks trained by back-propagation is required to improve our understanding of them as predictors of intermittent demand. On the other hand, it is also worth testing extreme learning machines in this context, because of their lower computational complexity and good generalisation ability. In this paper, neural networks trained by back-propagation and extreme learning machines are compared with benchmark neural networks, as well as standard forecasting methods for intermittent demand on real-time series, by combining different input patterns and architectures. A statistical analysis is then conducted to validate the best performance through different aggregation levels. Finally, some insights for practitioners are presented to improve the potential of neural networks for implementation in real environments.

Single-hidden layer neural networks for forecasting intermittent demand / Lolli, Francesco; Gamberini, Rita; Regattieri, A.; Balugani, Elia; Gatos, T.; Gucci, S.. - In: INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS. - ISSN 0925-5273. - 183:(2017), pp. 116-128. [10.1016/j.ijpe.2016.10.021]

Single-hidden layer neural networks for forecasting intermittent demand

LOLLI, Francesco;GAMBERINI, Rita;BALUGANI, ELIA;
2017

Abstract

Managing intermittent demand is a vital task in several industrial contexts, and good forecasting ability is a fundamental prerequisite for an efficient inventory control system in stochastic environments. In recent years, research has been conducted on single-hidden layer feedforward neural networks, with promising results. In particular, back-propagation has been adopted as a gradient descent-based algorithm for training networks. However, when managing a large number of items, it is not feasible to optimize networks at item level, due to the effort required for tuning the parameters during the training stage. A simpler and faster learning algorithm, called the extreme learning machine, has been therefore proposed in the literature to address this issue, but it has never been tried for forecasting intermittent demand. On the one hand, an extensive comparison of single-hidden layer networks trained by back-propagation is required to improve our understanding of them as predictors of intermittent demand. On the other hand, it is also worth testing extreme learning machines in this context, because of their lower computational complexity and good generalisation ability. In this paper, neural networks trained by back-propagation and extreme learning machines are compared with benchmark neural networks, as well as standard forecasting methods for intermittent demand on real-time series, by combining different input patterns and architectures. A statistical analysis is then conducted to validate the best performance through different aggregation levels. Finally, some insights for practitioners are presented to improve the potential of neural networks for implementation in real environments.
183
116
128
Single-hidden layer neural networks for forecasting intermittent demand / Lolli, Francesco; Gamberini, Rita; Regattieri, A.; Balugani, Elia; Gatos, T.; Gucci, S.. - In: INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS. - ISSN 0925-5273. - 183:(2017), pp. 116-128. [10.1016/j.ijpe.2016.10.021]
Lolli, Francesco; Gamberini, Rita; Regattieri, A.; Balugani, Elia; Gatos, T.; Gucci, S.
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S0925527316303176-main.pdf

non disponibili

Descrizione: Versione dell'editore
Tipologia: Versione dell'editore (versione pubblicata)
Dimensione 767.73 kB
Formato Adobe PDF
767.73 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Single-hidden layer neural networks for forecasting intermittent demand.pdf

accesso aperto

Tipologia: Post-print dell'autore (bozza post referaggio)
Dimensione 1.05 MB
Formato Adobe PDF
1.05 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1135468
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 75
  • ???jsp.display-item.citation.isi??? 63
social impact