Development of intelligent evaluation system for product end-of-life selection strategy

• Main Author: Zakri, Ghazalli
• Format: Thesis
• Language: English
• Published: 2011
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://umpir.ump.edu.my/id/eprint/7737/1/ZAKRI_BIN_GHAZALLI.PDF; http://umpir.ump.edu.my/id/eprint/7737/

As the world population increases exponentially, the number of products purchased and consumed is also increasing. The rapid pace of technological changes renders some products obsolete even though they are still new and in excellent condition. Consequently, the supply of natural resources decreases severely. Additionally, the amount of waste generated at the end-of-life of products has become a serious problem in most countries. In response, manufacturers have to seek other essential resources to provide materials for manufacturing their products. This generates a strong demand for secondary resources such as refurbished parts and recycled materials. This problem can be best resolved by promoting end-of-life (EOL) selection strategies. The main objective of this research is to develop an evaluation system for selecting EOL strategy specifically for remanufacturing. The developed framework consists of several sub-objectives. The first sub-objective is to evaluate the remanufacturing selection strategy at the product level. In order to achieve this, the integration of analytic hierarchical process (AHP) and case-based reasoning (CBR) is used to evaluate the EOL options at the product level. The AHP, which allows pair-wise comparison and consistent judgement, is used to determine the weight in nearest neighbourhood (NN) algorithm of CBR. The second subobjective is to evaluate the EOL of parts and components. The integration of the economic and environmental cost (EOL cost) model is used to determine the EOL of parts and components. The third sub-objective is to optimize the disassembly sequence of the EOL product. This study integrates the travelling salesman problem with genetic algorithm (TSP-GA) for finding the optimal disassembly sequence and disassembling the EOL product. Additionally, this study uses EOL profits and net present value (NPV) of parts and subassemblies of the EOL product to determine the best EOL option of components and parts of the EOL product.