IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Reverse Logistics Network Design Using a Hybrid Genetic Algorithm and Simulated Annealing Methodology

Reverse Logistics Network Design Using a Hybrid Genetic Algorithm and Simulated Annealing Methodology
View Sample PDF
Author(s): Gülfem Tuzkaya (Yildiz Technical University, Turkey), Bahadir Gülsün (Yildiz Technical University, Turkey) and Ender Bildik (Yildiz Technical University, Turkey)
Copyright: 2011
Pages: 19
Source title: Electronic Supply Network Coordination in Intelligent and Dynamic Environments: Modeling and Implementation
Source Author(s)/Editor(s): Iraj Mahdavi (Mazandaran University of Science and Technology, Iran), Shima Mohebbi (University of Tehran, Iran) and Namjae Cho (Hanyang University, Korea)
DOI: 10.4018/978-1-60566-808-6.ch007

Purchase

View Reverse Logistics Network Design Using a Hybrid Genetic Algorithm and Simulated Annealing Methodology on the publisher's website for pricing and purchasing information.

Abstract

Reverse logistics network design (RLND) effectiveness has an important impact on the effectiveness of the whole supply network coordination. Considering that, in this study, the RLND problem is investigated and a hybrid genetic algorithms and simulated annealing (HGASA) methodology is proposed. This problem is applied to a preceding study which utilized genetic algorithms (GA) for the optimization. HGASA and GA results are tested with Wilcoxon rank-sum test for hundred runs and the results prove the difference between two approaches. Additionally, the averages and the standard deviations support that, the HGASA algorithm increases the probability of obtaining better solutions.

Related Content

Ana Azevedo. © 2021. 12 pages.
Atik Kulakli. © 2021. 31 pages.
Mouhib Alnoukari. © 2021. 19 pages.
Arun Thotapalli Sundararaman. © 2021. 28 pages.
Mohammad Kamel Daradkeh. © 2021. 22 pages.
Roumiana Ilieva, Malinka Ivanova, Tzvetilina Peycheva, Yoto Nikolov. © 2021. 30 pages.
Walisson Ferreira Carvalho, Luis Zarate. © 2021. 16 pages.
Body Bottom