Used Product Pre-Sorting System Optimization Using Teaching-Learning-Based Optimization

Used Product Pre-Sorting System Optimization Using Teaching-Learning-Based Optimization

Copyright: © 2014 |Pages: 18
DOI: 10.4018/978-1-4666-4908-8.ch006
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In order to improve the overall output of remanufacturable end-of-life products, used products usually have to go through a pre-sorting system for identifying the sources of returns and rating them according to their characteristics (i.e., remanufacturable and non-remanufacturable). Under these circumstances, the radio frequency identification is normally used to ensure the efficiency and effectiveness of the pre-sorting process. In the last chapter, the authors focus on the multi-objective methodology to establish an evaluation model for the returned components and products; while in this chapter, the authors deal with the radio frequency identifications’ reliability in this evaluation model during the used products’ pre-sorting procedure. The chapter starts with an introduction about the issue of used product pre-sorting process and the importance of radio frequency identification tags’ reliability. Then, related studies dealing with similar problems in the literature are discussed in the background section. Next, the focal problem of this chapter is stated in the problem statement section. A detailed description about the approach (i.e., teaching-learning-based optimization algorithm) can be found in the proposed methodology section. Right after this, an illustrative example is explained in the experimental study section. The potential research directions regarding the main problem considered in this chapter are highlighted in the future trends section. Finally, the conclusion drawn in the last section closes this chapter.
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Pre-sorting issues hampering the whole remanufacturing process’ efficiency were found to be at the used products’ remanufacturability evaluation level (Tagaras & Zikopoulos, 2008). Thanks to emerging technologies such as radio frequency identification (RFID), the remanufacturers can obtain more timely and accurate information about the used products at the end-of-life (EoL) phase which can facilitate the whole pre-sorting process. However, from the perspective of production management, any pervasive environment and/or embedded systems requires strong efforts on all the aspects of system reliability (Meedeniya, Buhnova, Aleti, & Grunske, 2011). Consequently, in the context of RFID-based sorting procedure that is closely interact with the physical environment, to classify which used products should be remanufactured and which should be scrapped, requires more than just the multi-objective evaluation model (see Chapter 5) which can be ruled as sorting polices, the whole RFID system reliability is also need to be taken into account. In other words, the RFID system influences the capability of the pre-sorting procedure.

Normally, in a RFID-based system, the failure of one component can reduce the system reliability in two aspects: (1) to induce other component’s failure, and (2) to lose the whole system reliability contribution (Yu, Chu, Châtlet, & Yalaoui, 2007). In the pre-sorting environment for example, if there is a failure occur, it will lead to defective products, in which the remanufacturable products might be classified to the waste line. Thus, in this chapter, we deal with the problem of how to enhance the RFID systems performance. Several researchers (such as (Agarwal, Aggarwal, & Sharma, 2010; Gupta, Bhunia, & Roy, 2009; Ha & Kuo, 2006; Misra & Sharma, 1973)) suggested that adding more redundant components in various subsystems can improve the system reliability, particularly in the maintenance, semiconductors, memory integrated circuits and nanosystems domain. To cope with this issue, an innovative computational intelligence (CI) algorithm called teaching – learning-based optimization (TLBO) algorithm has been proposed to deal with the problem. Furthermore, in order to assure the performance of TLBO, it has been tested on some other complex engineering optimization problems as well.

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