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Development of distributed LSTM framework to forecast transportation lead time

Author

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  • Utkarsh Mittal
  • Dilbagh Panchal

Abstract

This study aimed to develop an AI-based system to evaluate delivery complexities and reduce system vulnerabilities more accurately. The approach of the study is empirical where dataset from different systems is used to develop ML and DL models to forecast more accurately transportation time and improve profitability. Various models, e.g., linear regression, deep learning, and distributed long short-term memory (DLSTM) networks are used. It is found that the DLSTM regression model shows superior performance in forecasting the delivery times compared to the other models, achieving an accuracy of around 90%, as the model has the ability to handle complex and nonlinear relationships among variables. The findings underscore the potential of machine learning (ML) and deep learning (DL) in improving predictability and profitability aimed increasing digitalisation in global transportation.

Suggested Citation

  • Utkarsh Mittal & Dilbagh Panchal, 2025. "Development of distributed LSTM framework to forecast transportation lead time," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 49(4), pages 520-544.
    • Handle: RePEc:ids:ijisen:v:49:y:2025:i:4:p:520-544
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