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.
IoT Based Automatic Tyre Sorting System