Remanufacturing is a practice of growing importance as it returns the end of life products back to conditions that is as good as or better than new ones. The increasingly stringent environmental legislation and economic demands has led to the rapid development of remanufacturing industry in the world. Remanufacturing is still at its infantry. One of the major challenges faced by remanufacturing is to guarantee the reliability of remanufactured products since they came from cores with varying condition. Process planning plays a critical role in realizing a successful remanufacturing strategy since it directly affects the success rate of remanufacturing as well as reliability and cost. To do so, this work presents an optimization method for remanufacturing process planning in which reliability and cost are taken into consideration. In this method, reliability is represented by failure rate of remanufacturing operations which is influenced by the quality of returned used products (cores), whilst process cost includes machine cost and tool cost. The multi-objective optimization problem is solved by a genetic algorithm. To assess the usefulness and practicality of the proposed method, an illustrative example is given to illustrate the proposed models and the effectiveness of the proposed algorithm. The results showed that the proposed method is effective for improving reliability and reducing cost.
- Genetic algorithm
- Process planning
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- School of Arch, Tech and Eng - Principal Lecturer
- Applied Data Analytics Research and Enterprise Group
- Advanced Engineering Centre