Economical impact of RFID implementation in remanufacturing: a Chaos-based Interactive Artificial Bee Colony approach

© 2013, Springer Science+Business Media New York. In the modern manufacturing arena, environmental and economical concerns draw considerable attention from both practitioners and researchers towards remanufacturing practices. The success of remanufacturing firms depends on how efficiently the recovery process is executed. Radio Frequency Identification (RFID) technology holds immense potential to enhance the recovery process. The deployment of RFID technology at reverse echelons has the advantage of having a real time system with reduced inventory shrinkage, reduced processing time, reduced labor cost, process accuracy, and other directly measurable benefits. In spite of these expected benefits, the heavy financial investment required in implementing the RFID system is a big threat for remanufacturing companies. This paper examines the economical impact of RFID adoption to remanufacturing. The aim of the research is to compare the basic and RFID-diffused reverse logistics model, and to quantitatively decide whether RFID implementation is economically viable. In order to meet these objectives, we have proposed a Chaos-based Interactive Artificial Bee Colony (CI-ABC) algorithm. Numerical results from using the CI-ABC for optimal performance are presented and analyzed. Comparison between the canonical Artificial Bee Colony and the Particle Swarm Optimization reveals the superiority of the CI-ABC for this application

Closed loop supply chain network design with fuzzy tactical decisions

One of the most strategic and the most significant decisions in supply chain management is reconfiguration of the structure and design of the supply chain network. In this paper, a closed loop supply chain network design model is presented to select the best tactical and strategic decision levels simultaneously considering the appropriate transportation mode in activated links. The strategic decisions are made for a long term; thus, it is more satisfactory and more appropriate when the decision variables are considered uncertain and fuzzy, because it is more flexible and near to the real world. This paper is the first research which considers fuzzy decision variables in the supply chain network design model. Moreover, in this study a new fuzzy optimization approach is proposed to solve a supply chain network design problem with fuzzy tactical decision variables. Finally, the proposed approach and model are verified using several numerical examples. The comparison of the results with other existing approaches confirms efficiency of the proposed approach. Moreover the results confirms that by considering the vagueness of tactical decisions some properties of the supply chain network will be improved