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Research on location‐routing problem of reverse logistics with grey recycling demands based on PSO

Research on location‐routing problem of reverse logistics with grey recycling demands based on PSO Purpose – The purpose of this paper is to realize a location‐routing network optimization in reverse logistics (RL) using grey systems theory for uncertain information. Design/methodology/approach – There is much uncertain information in network optimization and location‐routing problem (LRP) of RL, including fuzzy information, stochastic information and grey information, etc. Fuzzy information and stochastic information have been studied in logistics, however grey information of RL has not been covered. In the LRP of RL, grey recycling demands are taken into account. Then, a mathematics model with grey recycling demands has been constructed, and it can be transformed into grey chance‐constrained programming (GCCP) model, grey simulation and a proposed hybrid particle swarm optimization (PSO) are combined to resolve it. An example is also computed in the last part of the paper. Findings – The results are convincing: not only that grey system theory can be used to deal with grey uncertain information about location‐routing problem of RL, but GCCP, grey simulation and PSO can be combined to resolve the grey model. Practical implications – The method exposed in the paper can be used to deal with location‐routing problem with grey recycling information in RL, and network optimization result with grey uncertain factor could be helpful for logistics efficiency and practicability. Originality/value – The paper succeeds in realising both a constructed model about location‐routing of RL with grey recycling demands and a solution algorithm about grey mathematics model by using one of the newest developed theories: grey systems theory. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Grey Systems: Theory and Application Emerald Publishing

Research on location‐routing problem of reverse logistics with grey recycling demands based on PSO

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Publisher
Emerald Publishing
Copyright
Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
ISSN
2043-9377
DOI
10.1108/20439371111106768
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to realize a location‐routing network optimization in reverse logistics (RL) using grey systems theory for uncertain information. Design/methodology/approach – There is much uncertain information in network optimization and location‐routing problem (LRP) of RL, including fuzzy information, stochastic information and grey information, etc. Fuzzy information and stochastic information have been studied in logistics, however grey information of RL has not been covered. In the LRP of RL, grey recycling demands are taken into account. Then, a mathematics model with grey recycling demands has been constructed, and it can be transformed into grey chance‐constrained programming (GCCP) model, grey simulation and a proposed hybrid particle swarm optimization (PSO) are combined to resolve it. An example is also computed in the last part of the paper. Findings – The results are convincing: not only that grey system theory can be used to deal with grey uncertain information about location‐routing problem of RL, but GCCP, grey simulation and PSO can be combined to resolve the grey model. Practical implications – The method exposed in the paper can be used to deal with location‐routing problem with grey recycling information in RL, and network optimization result with grey uncertain factor could be helpful for logistics efficiency and practicability. Originality/value – The paper succeeds in realising both a constructed model about location‐routing of RL with grey recycling demands and a solution algorithm about grey mathematics model by using one of the newest developed theories: grey systems theory.

Journal

Grey Systems: Theory and ApplicationEmerald Publishing

Published: Jan 28, 2011

Keywords: Systems theory; Reverse scheduling; Iterative methods; Process efficiency; Programming and algorithm theory

References