Single-hidden layer neural networks for forecasting intermittent demand

Lolli, Francesco; Gamberini, Rita; Regattieri, Alberto; Balugani, Elia; Gatos, T.; Gucci, S.

doi:10.1016/j.ijpe.2016.10.021

Cited by 96 publications

(55 citation statements)

References 51 publications

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“…For intermittent demand patterns, Kourentzes [10] proposed the ANN method, which allows interactions between the interdemand intervals and the demand of intermittent items. Lolli et al [12] used the ANN method trained by back-propagation and extreme learning machines. In addition, different input patterns and architectures were used to compare the forecasting results of proposed ANN methods for intermittent demand.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Artificial Intelligence Based Methodology for the Inventory Routing Problem

et al. 2019

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In this paper, a new hybrid method including simulation optimization and artificial intelligence based simulation is created to solve the inventory routing problem (IRP) in which three different routing strategies are evaluated for uneven demand patterns including intermittent, erratic, and lumpy demand. The proposed method includes two phases. In the first phase, a nondominated sorting genetic algorithm II based simulation is employed to perform a multi-objective search for the IRP where the objectives of the method are total supply chain cost minimization and average service level maximization. In the second phase, artificial neural network based simulation is used to adjust the reorder point and order-up-to-level by forecasting the customer demand at each replenishment time. The results of the study demonstrated that the average service level is at least 98.54% in the supply chain. From this, it can be concluded that the proposed method can provide a tremendous opportunity to improve the average service level under uncertain environments. In addition, it is determined that different routing strategies can be selected for different demand patterns according to the considered performance measures.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Artificial Intelligence Based Methodology for the Inventory Routing Problem

et al. 2019

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“…The classification tree is able to work with multiple reference items and is robust to misclassified reference items. The use of machine learning methodologies within inventory management is a recent area of research (Lolli et al, 2017a;Lolli et al, 2017b).…”

Section: : Definition Of Classesmentioning

confidence: 99%

DEASort: Assigning items with data envelopment analysis in ABC classes

Ishizaka

Lolli

Balugani

et al. 2018

International Journal of Production Economics

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“…In the literature, a large number of methods that are related to machine learning (ML), artificial intelligence (AI), and statistical learning theory (SLT) are available to conduct any of these processes [5][6][7][8][9]. However, most applications: (1) are oriented either to diagnostics or prognostics, which makes it difficult to practically understand the relationship between the two different tasks [10]; and, (2) adopt a supervised learning approach, i.e., require many training data corresponding to component health conditions for model construction [11].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical Systems

et al. 2020

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Predictive maintenance allows industries to keep their production systems available as much as possible. Reducing unforeseen shutdowns to a level that is close to zero has numerous advantages, including production cost savings, a high quality level of both products and processes, and a high safety level. Studies in this field have focused on a novel approach, prognostic health management (PHM), which relies on condition monitoring (CM) for predicting the remaining useful life (RUL) of a system. However, several issues remain in its application to real industrial contexts, e.g., the difficulties in conducting tests simulating each fault condition, the dynamic nature of industrial environments, and the need to handle large amounts of data collected from machinery. In this paper, a data-driven methodology for PHM implementation is proposed, which has the following characteristics: it is unsupervised, i.e., it does not require any prior knowledge regarding fault behaviors and it does not rely on pre-trained classification models, i.e., it can be applied “from scratch”; it can be applied online due to its low computational effort, which makes it suitable for edge computing; and, it includes all of the steps that are involved in a prognostic program, i.e., feature extraction, health indicator (HI) construction, health stage (HS) division, degradation modelling, and RUL prediction. Finally, the proposed methodology is applied in this study to a rotating component. The study results, in terms of the ability of the proposed approach to make a timely prediction of component fault conditions, are promising.

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Single-hidden layer neural networks for forecasting intermittent demand

Cited by 96 publications

References 51 publications

A Novel Hybrid Artificial Intelligence Based Methodology for the Inventory Routing Problem

A Novel Hybrid Artificial Intelligence Based Methodology for the Inventory Routing Problem

DEASort: Assigning items with data envelopment analysis in ABC classes

Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical Systems

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