TY - JOUR
T1 - Genetic algorithm dynamic performance evaluation for RFID reverse logistic management
AU - Trappey, Amy J.C.
AU - Trappey, Charles V.
AU - Wu, Chang Ru
PY - 2010/11/1
Y1 - 2010/11/1
N2 - Environmental awareness, green directives, liberal return policies, and recycling of materials are globally accepted by industry and the general public as an integral part of the product life cycle. Reverse logistics reflects the acceptance of new policies by analyzing the processes associated with the flow of products, components and materials from end users to re-users consisting of second markets and remanufacturing. The components may be widely dispersed during reverse logistics. Radio frequency identification (RFID) complying with the EPCglobal (2004) Network architecture, i.e., a hardware- and software-integrated cross-platform IT framework, is adopted to better enable data collection and transmission in reverse logistic management. This research develops a hybrid qualitative and quantitative approach, using fuzzy cognitive maps and genetic algorithms, to model and evaluate the performance of RFID-enabled reverse logistic operations (The framework revisited here was published as "Using fuzzy cognitive map for evaluation of RFID-based reverse logistics services", Proceedings of the 2009 international conference on systems, man, and cybernetics (Paper No. 741), October 11-14, 2009, San Antonio, Texas, USA.). Fuzzy cognitive maps provide an advantage to linguistically express the causal relationships between reverse logistic parameters. Inference analysis using genetic algorithms contributes to the performance forecasting and decision support for improving reverse logistic efficiency.
AB - Environmental awareness, green directives, liberal return policies, and recycling of materials are globally accepted by industry and the general public as an integral part of the product life cycle. Reverse logistics reflects the acceptance of new policies by analyzing the processes associated with the flow of products, components and materials from end users to re-users consisting of second markets and remanufacturing. The components may be widely dispersed during reverse logistics. Radio frequency identification (RFID) complying with the EPCglobal (2004) Network architecture, i.e., a hardware- and software-integrated cross-platform IT framework, is adopted to better enable data collection and transmission in reverse logistic management. This research develops a hybrid qualitative and quantitative approach, using fuzzy cognitive maps and genetic algorithms, to model and evaluate the performance of RFID-enabled reverse logistic operations (The framework revisited here was published as "Using fuzzy cognitive map for evaluation of RFID-based reverse logistics services", Proceedings of the 2009 international conference on systems, man, and cybernetics (Paper No. 741), October 11-14, 2009, San Antonio, Texas, USA.). Fuzzy cognitive maps provide an advantage to linguistically express the causal relationships between reverse logistic parameters. Inference analysis using genetic algorithms contributes to the performance forecasting and decision support for improving reverse logistic efficiency.
KW - Fuzzy cognitive maps
KW - Genetic algorithm
KW - Radio frequency identification (RFID)
KW - Reverse logistics
UR - http://www.scopus.com/inward/record.url?scp=77955415597&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2010.04.026
DO - 10.1016/j.eswa.2010.04.026
M3 - Article
AN - SCOPUS:77955415597
SN - 0957-4174
VL - 37
SP - 7329
EP - 7335
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 11
ER -