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.
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