Forecasting Auto Spare Parts Demand in Iran: A Hybrid Neural Network Approach with Meta-heuristic Optimization
محورهای موضوعی : Journal of Computer & Robotics
Hanyeh Zareian
1
,
Vahid Baradaran
2
,
AliReza Rashidi Komijan
3
1 - a PhD Candidate, Department of Industrial Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran.
2 - Department of Industrial Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Department of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
کلید واژه: Auto spare parts, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Genetic Algorithm (GA),
چکیده مقاله :
The automotive industry serves as a cornerstone of Iran's economy, with auto spare parts demand playing a vital role in its transportation infrastructure. Traditional forecasting methods often struggle to capture the intricacies of Iran's dynamic market dynamics, prompting the adoption of advanced computational techniques. This study explores the efficacy of hybrid neural networks, particularly the combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, optimized with genetic algorithm, in forecasting auto spare parts demand. Empirical evaluation demonstrates the superiority of the CNN-LSTM-GA model over traditional algorithms, showcasing its potential to drive operational efficiency and cost-effectiveness in the automotive supply chain. The findings underscore the significance of embracing innovative methodologies and present avenues for future research to explore broader applicability and scalability in diverse contexts.
The automotive industry serves as a cornerstone of Iran's economy, with auto spare parts demand playing a vital role in its transportation infrastructure. Traditional forecasting methods often struggle to capture the intricacies of Iran's dynamic market dynamics, prompting the adoption of advanced computational techniques. This study explores the efficacy of hybrid neural networks, particularly the combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, optimized with genetic algorithm, in forecasting auto spare parts demand. Empirical evaluation demonstrates the superiority of the CNN-LSTM-GA model over traditional algorithms, showcasing its potential to drive operational efficiency and cost-effectiveness in the automotive supply chain. The findings underscore the significance of embracing innovative methodologies and present avenues for future research to explore broader applicability and scalability in diverse contexts.
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