Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21352
Title: A superiority search and optimisation algorithm to solve RFID and an environmental factor embedded closed loop logistics model
Authors: Kumar, VV
Chan, FTS 
Keywords: closed loop supply chain
random search techniques
RFID
Issue Date: 2011
Publisher: Taylor & Francis
Source: International journal of production research, 2011, v. 49, no. 16, p. 4807-4831 How to cite?
Journal: International journal of production research 
Abstract: Environmental management and economic concerns drive the remanufacturing industry to inevitable process in closed loop logistics. The collection of end-of-life products from end-users is a main issue as their amounts will directly affect the inventory of manufacturing units, with a significant impact on the final product cost and green environment. Most of the existing models on reverse logistics assumed the return rate as a fixed fraction. However, the number of returned products is always uncertain and depends on many factors like law, government policy, environmental protection issues, etc. The presented research overcomes this limitation and formulates a mathematical model in which the return rate will be a function of environmental factors. Moreover, the model is extended by integrating it with state-of-the-art radio frequency identification (RFID) technology. This RFID embedded model is aimed at mapping the economical merits by easily counting returned products and transferring them to different remanufacturing centres. In order to meet the objective of the proposed model, a superiority search and optimisation algorithm (SSOA) has been proposed. The novel approach efficiently predicts and selects a superior algorithm from a given set of algorithms, and adequately explores the entire search space to achieve superior performance on the underlying problem. Furthermore, the results obtained by implementing the proposed SSOA have been analysed and compared with other random search techniques including genetic algorithm (GA), simulated annealing (SA), and particle swarm optimisation (PSO). The proposed SSOA approach was seen to significantly outperform the rest.
URI: http://hdl.handle.net/10397/21352
ISSN: 0020-7543
EISSN: 1366-588X
DOI: 10.1080/00207543.2010.503201
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