Artificial Intelligence in Green Logistics: A Predictive Modeling Approach

View Sample PDF

Author(s): Usharani Bhimavarapu (Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India)
Copyright: 2026
Pages: 24
Source title: Innovations in Green and Energy-Efficient Warehousing
Source Author(s)/Editor(s): Mohamed Amine Frikha (King Faisal University, Saudi Arabia), Mohieddine Rahmouni (King Faisal University, Saudi Arabia)and Ben Othman Soufiane (King Faisal University, Saudi Arabia)
DOI: 10.4018/979-8-3373-3176-8.ch003

Keywords: Energy / Engineering Science Reference / Nuclear & Energy Engineering / Science & Engineering

Purchase

View Artificial Intelligence in Green Logistics: A Predictive Modeling Approach on the publisher's website for pricing and purchasing information.

Abstract

This study explores the implementation of green warehousing practices through the integration of advanced artificial intelligence techniques, specifically using a Bi-Stacked Artificial Neural Network (Bi-Stacked ANN) for predictive modeling. Data were collected via structured questionnaires from four stakeholder groups—enterprise employees, consumers, public officials, and producers—yielding 469 valid responses. Following preprocessing and feature normalization, Ant Colony Optimization (ACO) was applied to select the most relevant features influencing sustainable warehousing. The Bi-Stacked ANN model was then developed to analyze patterns and predict the effectiveness of green practices. The model demonstrated strong predictive performance and offered insights into critical sustainability factors.

The IRMA Community

Research IRM

Artificial Intelligence in Green Logistics: A Predictive Modeling Approach

Purchase

Abstract

Related Content

IRMA Sponsors