Post-Balstm a Bagged Lstm Forecasting Ensemble Embedded with a Postponement Framework to Target the Semiconductor Shortage in the Automotive Industry: An Electronics Manufacturing Services Case Study

Soto-Ferrari, Milton; Bhattacharyya, Kallol; Schikora, Paul F.

doi:10.2139/ssrn.4360445

Cited by 2 publications

(3 citation statements)

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“…The implementation embraces the stochastic nature of deep learning models, denoting that each predicted output for the correspondent run is not equal but statistically alike. While in (45), the best deep learning model in their application was a unidirectional LSTM architecture, in our implementation, as we are also considering CNN models, we will develop the bagged ensemble for the one we identify as the best (i.e., lowest MAPE), either CNN, LSTM, Bi-LSTM or CNN-LSTM (e.g., BaCNN, BaLSTM, BaBi-LSTM, BaCNN-LSTM). The general approach of the methodology segments the development stages into three phases:…”

Section: Our Proposed Ensemble Bagged Deep Learning Developmentmentioning

confidence: 99%

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Deep Learning Architectures Framework for Emerging Outbreak Forecasting of Mpox: A Bagged Ensemble Scheme to Model Accurate Prediction Intervals

Soto-Ferrari

Carrasco‐Pena

Prieto

2023

Preprint

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Background The Mpox virus is a disease of rare occurrence from the same family as the variola virus, which is hardly ever fatal, and its symptoms are like the ones of smallpox. As the outbreak was emerging in Brazil, the frail post-pandemic public health system and stigmatization yielded substandard data collection of confirmed cases. As of the beginning of 2023, the established case trend is short and has noisy patterns that challenge most existing forecasting methods.Methods To extend the modeling choices for emerging outbreaks with volatile and short-term confirmed case data, we evaluate the performance of multiple deep learning architectures, including Convolutional Neural Network (CNN), unidirectional Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM). We further developed a bagged ensemble methodology (i.e., BaLSTM) with the best-identified method for predicting the Mpox emerging outbreak in Brazil. Our evaluation dataset consists of Brazil’s weekly Mpox cases from July 2022 to January 2023, contrasting the performance for the coming six weeks. Additionally, accepted machine-learning models were built to predict weekly confirmed cases to compare the relative performance of our implementation. We evaluated the performance of our deep learning architectures with exponential smoothing (ETS), ARIMA, Support Vector Machine (SVM), K-nearest neighbors (KNN), and Neural Networks Autoregression (NNAR).Results Based on the results, the BaLSTM approach achieves an accuracy of 80.83% when considering its associated prediction intervals, translating into a 45% improvement in measuring forecast errors for the Mpox Brazil cases when evaluated with the remaining assessed methods. The proposed model can capture trends and patterns in the time series while considering the intrinsic attributes of the sample.Conclusions Implementation such as those described in this research will become increasingly crucial in predicting emerging epidemics with a small sample size and an apparent uncertain behavior.

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Section: Our Proposed Ensemble Bagged Deep Learning Developmentmentioning

confidence: 99%

“…We evaluate the BaLSTM ensemble rating using the SF measure (45). This measure aims to provide a percentage rating depending on the interval limits for each value predicted.…”

Section: Figure 8 Balstm Developmentmentioning

confidence: 99%

Deep Learning Architectures Framework for Emerging Outbreak Forecasting of Mpox: A Bagged Ensemble Scheme to Model Accurate Prediction Intervals

Soto-Ferrari

Carrasco‐Pena

Prieto

2023

Preprint

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“…Finally, the researchers conducted theoretical and computational analyses to explore the postponement strategies and deterioration rate's influence on optimal policies. Other studies (Kisperska-Moron and Swierczek, 2011;Altug, 2016;Chiu et al, 2021Chiu et al, , 2022aLin et al, 2022;Kiani et al, 2022;Hinkka et al, 2023;Soto-Ferrari et al, 2023) investigated various postponement strategies' impact on operations, management, and controlling of multi-item fabrication in manufacturers and supply chains.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a hybrid multi-item fabricating-shipping integrated system considering scrap, adjustable-rate, and postponement

Chiu,

Lo,

Pai

et al. 2024

10.5267/j.ijiec

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This study aims to optimize a hybrid multi-item fabricating-shipping integrated system incorporating scrap, adjustable rate, and postponement. In present-day competitive market environments, there is a clear client demand trend for various goods, shorter lead time, and expected quality. To satisfy the client’s needs, the management of manufacturing firms requires an effective and efficient plan to fabricate various high-quality goods in an expedited period, under limited capacity, and with minimal operating expenses. Inspired by facilitating production management to determine the best fabricating scheme/plan to achieve their operational goals, this work proposes an exploratory postponement model with quality assurance and uptime reduction strategies for their decision-making. By employing a two-phase making scheme, the required standard components are first made in the 1st phase, and multiple finished merchandise is fabricated in the 2nd phase. The study suggests strategies of contracting out a part of the common parts’ batch and adopting an adjusted/expedited making rate in the 2nd phase to considerably reduce both phases’ production uptimes. During both fabricating processes, the screening tasks identify/remove scrapped/faulty goods to ensure each finished batch’s quality. Equal-amount multiple shipments of end merchandise are transported to the clients in fixed time-interval. Optimization methodology and mathematical analyses support us in deriving the model’s expected annual operating cost and deciding the optimal production-transportation policy. A numerical illustration helps verify our model’s applicability and reveals important managerial insights into the studied problem to facilitate management in decision-making.

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Post-Balstm a Bagged Lstm Forecasting Ensemble Embedded with a Postponement Framework to Target the Semiconductor Shortage in the Automotive Industry: An Electronics Manufacturing Services Case Study

Cited by 2 publications

References 0 publications

Deep Learning Architectures Framework for Emerging Outbreak Forecasting of Mpox: A Bagged Ensemble Scheme to Model Accurate Prediction Intervals

Deep Learning Architectures Framework for Emerging Outbreak Forecasting of Mpox: A Bagged Ensemble Scheme to Model Accurate Prediction Intervals

Optimization of a hybrid multi-item fabricating-shipping integrated system considering scrap, adjustable-rate, and postponement

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