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

Abstract

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%, and F1score of 94,92%, and AUC of 88,17%. These results reveal the proof-of-principle for using LSTM models for effective and truthful logistics operations.

An improved resource scheduling strategy through concatenated deep learning model for edge computing IoT networks

“…Besides DRL, few other learning networks are employed for resource scheduling in edge computing applications. An LSTM‐based optimal resource allocation and task scheduling model was reported in Issaoui et al 20 that handles the dynamic resource requests in logistic systems. The neural network architecture reported in Manoharan et al 21 considers the interfering neighbor nodes to compute spatial convolutions.…”

The Rising Trends of Smart E-Commerce Logistics

“…5G communication technology features ultralow latency, high-speed broadband, and massive access, enabler of cloud computing and big data solutions for logistics [58]. The approach based on the LSTM model is more convenient for real-time resource allocation (response time 50s) and the proposed approach is suitable for real-time scheduling [38].…”

Recent Technological Progress to Empower Smart Manufacturing: Review and Potential Guidelines

“…The number of interconnected devices needed to collect data can lead to security issues, as more devices lead to more entry points into the network. Therefore, it is essential to choose the right software platform for industrial automation [92]. Before talking about data issues, it is important to know that today's manufacturing industries put product quality at the heart of any manufacturing process.…”