Logistics network design for product recovery in fuzzy environment

The design of product recovery network is one of the important and challenging problems in the field of reverse logistics. Some models have been formatted by researchers under deterministic environment. However, uncertainty is inherent during the process of the practical product recovery. In order t...

Full description

Saved in:
Bibliographic Details
Published in:European journal of operational research Vol. 202; no. 2; pp. 479 - 490
Main Authors: Qin, Zhongfeng, Ji, Xiaoyu
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 16.04.2010
Elsevier
Elsevier Sequoia S.A
Series:European Journal of Operational Research
Subjects:
Algorithms
Applied sciences
Credibility measure
Credibility measure Product recovery Reverse logistics Fuzzy programming Genetic algorithm Fuzzy simulation
Exact sciences and technology
Flows in networks. Combinatorial problems
Fuzzy logic
Fuzzy programming
Fuzzy simulation
Genetic algorithm
Genetic algorithms
Logistics
Operational research and scientific management
Operational research. Management science
Optimization
Product recovery
Reverse logistics
Simulation
Studies
Uncertainty
Credibility measure
Fuzzy simulation
Fuzzy programming
Genetic algorithm
Product recovery
Reverse logistics
Fuzzy logic
Material recovery
Uncertain system
Multiobjective programming
Modeling
ISSN:0377-2217, 1872-6860
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The design of product recovery network is one of the important and challenging problems in the field of reverse logistics. Some models have been formatted by researchers under deterministic environment. However, uncertainty is inherent during the process of the practical product recovery. In order to deal with uncertainty, this paper employs a fuzzy programming tool to design the product recovery network. Based on different criteria, three types of optimization models are proposed and some properties of them are investigated. To solve the proposed models, we design a hybrid intelligent algorithm which integrates fuzzy simulation and genetic algorithm. Finally, several numerical examples are presented to illustrate the effectiveness of the proposed models and algorithm.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2009.05.036