| The rapid advancements in technology have resulted in noticeably shortening product life cycles, which increase the concerns about end-of-life products treatments. As a result, optimizing the planning and sequencing of a disassembly process at the end of a product life cycle becomes an important task. Components of an end-of-life product can be reused, recycled, or disposed, depending on their conditions. Therefore, information about the usage, maintenance, and repair of a product throughout its life cycle, i.e., the biographical data, plays a critical role during the end-of-life decision making. One of the main obstacles for optimizing a disassembly process is the loss of biographical data of a product after its date of sale. Due to the information loss, the recovery value of a component has to be estimated based on little information. In order to improve the product recovery decisions, this research proposes the use of radio-frequency identification (RFID) technology to enrich the decision support information with the biographical data of a product throughout its life cycle. Using the enriched information, a fuzzy based disassembly planning and sequencing model is proposed to determine what to be disassembled and how to disassemble it in order to maximize the net profits. Based on the data from RFID tags, a Bayesian approach determines the true condition of the product components. Using fuzzy logic, the model synthesizes three input variables (i.e., product usage, component usage, and biographical data) into a solution that maximizes the recovery value while minimizing disassembly costs with an optimal disassembly sequence. The model is solved by genetic algorithm. This research verifies the merits of using RFID to enrich decision support information for improving disassembly decisions. It also promotes a more effective way to recycle and reuse end-of-life products to minimize environmental impact. |
