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

Yassine Issaoui,Azeddine Khiat,Ayoub Bahnasse,Hassan Ouajji

doi:10.1109/access.2021.3111306

Yassine Issaoui, Azeddine Khiat + Show 2 more

Open Access

https://doi.org/10.1109/access.2021.3111306

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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%, 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.

Highlights

G LOBAL companies are often subject to a huge number of constraints that affect their processes
We have built a mathematical model for real-time scheduling in the smart logistics (SL) environment with optimization objectives such as minimizing cost, minimizing delivery time, and maximizing service satisfaction, which remains proportional to the priority of tasks, the occupation of resources, and the duration of logistics;
-- The approach based on the Long short-term memory (LSTM) model is more efficient than other methods;

Summary

Introduction

G LOBAL companies are often subject to a huge number of constraints that affect their processes. Logistics systems have become a major focus for smoothing flow management, advancing transactions, and improving the added value along the supply chain in the global economy. On this matter, smart logistics systems ensure the maximization of productivity, which has been the backbone of the previous industrial revolutions, the automation of processes, and the well-organized use of resources for greater efficiency. Real-time performance remains essential to warrant strong operative processes within a dynamic logistics environment, such as e-commerce These systems are based on a set, of human resources, hardware, software, techniques...., to manage the basic logistics infrastructures.

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