An Advanced LSTM Model for Optimal Scheduling in Smart Logistic Environment: E-Commerce Case

• Main Authors: Yassine Issaoui, Azeddine Khiat, Ayoub Bahnasse, Hassan Ouajji
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Artificial intelligence; deep learning; LSTM; optimization; smart logistics; task management
• Online Access: https://ieeexplore.ieee.org/document/9531363/

At present, most logistics systems, especially those dedicated to e-commerce, are based on artificial intelligence techniques to offer better services and increase outcomes. However, the variety and complexity of resource allocation, as well as task scheduling, denote that dynamic environments have still great challenges to overcome. So advanced models based on strong algorithms are required. Introducing advanced models into scheduling solutions is a promising way to enhance logistics efficiency. As a result, managing system resources remain essential to optimize task scheduling respecting the interactive impacts, and logistics systems requirements. In response to these challenges, in this paper, a powerful solution based on a Long short-term memory (LSTM) model is proposed to optimize resource allocation and to enhance task scheduling in a smart logistics framework. This paper explores some of the most important scheduling techniques and hypothesizes that deep learning techniques might be able to afford accurate approaches. The proposed smart logistics model lays on strong techniques, for that, experimental simulations were conducted using various project instances. The validation tests demonstrated competitive results with important performance rates i.e.: accuracy of 92,44% with a precision of 93,83, a recall of 95.18%, F1-score of 94,92%, and an AUC of 88,17%. These results reveal the proof-of-principle for using LSTM models for effective and truthful logistics operations.