Genetic algorithm dynamic performance evaluation for RFID reverse logistic management

  • Authors:

  • Amy J. C. Trappey;Charles V. Trappey;Chang-Ru Wu

  • Affiliations:

  • Department of Industrial Engineering and Management, National Taipei University of Technology, Taiwan and Department of Industrial Engineering and Engineering Management, National Tsing Hua Univer ...;Department of Management Science, National Chiao Tung University, Hsinchu 300, Taiwan;Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Taiwan

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2010

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Abstract

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.