Network model and optimization of reverse logistics by hybrid genetic algorithm

Authors:
Jeong-Eun Lee;Mitsuo Gen;Kyong-Gu Rhee
Affiliations:
Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu 808-0135, Japan;Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu 808-0135, Japan;Dongeui University, 995 Eomgwangno, Busanjin-gu, Busan 614-714, South Korea
Venue:
Computers and Industrial Engineering
Year:
2009

Citing 1
Cited 6

Supply planning model for remanufacturing system in reverse logistics environment

Computers and Industrial Engineering - Special issue: Logistics and supply chain management

A knowledge discovery way for reverse logistics center location

FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 4
Network optimization in supply chain: A KBGA approach

Decision Support Systems
An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach

Expert Systems with Applications: An International Journal
Cost benefits from standardization of the packaging glass bottles

Computers and Industrial Engineering
Inventory management in a two-echelon closed-loop supply chain with correlated demands and returns

Computers and Industrial Engineering
A robust hybrid multi-criteria decision making methodology for contractor evaluation and selection in third-party reverse logistics

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.01

Visualization

Abstract

The interest about recovery of used products and materials have been increased. Therefore, reverse logistics network problem (rLNP) will be powerful and get a great potential for winning consumers in a more competitive context in the future. We formulate a mathematical model of remanufacturing system as three-stage logistics network model for minimizing the total of costs to reverse logistics shipping cost and fixed opening cost of the disassembly centers and processing centers. And we consider a multi-stage, multi-product and some attach condition for disassembly centers and processing centers, respectively. For solving this problem, we propose a genetic algorithm (GA) with priority-based encoding method consisting of 1st and 2nd stages combined a new crossover operator called as weight mapping crossover (WMX). A heuristic approach is applied in the 3rd stage to transportation of parts from processing center to manufacturer. Numerical experiments with various scales of rLNP models show the effectiveness and efficiency of our approach by comparing the recent researches.