Abstract
In recent years, it is an important issue to handle products at the end-of-life (EOL). In order to efficiently recover the EOL products, it is necessary to disassemble the product into several smaller components in this process. However, the disassembly time is a performance index which depends on the disassembly sequence of components. The purpose of the study is to obtain the optimal disassembly sequence of the product which can minimize the disassembly time. In this article, we propose a soft computing algorithm to solve this kind of problem by combining discrete particle swarm optimization (DPSO) with concept of precedence preservative crossover (PPX) in genetic algorithm. Discrete particle swarm optimization (DPSO) is a population based stochastic optimization technique that is used to solve problems in discrete type, and PPX guarantees feasible solution at each of evaluations. In order to have a better result, we apply Taguchi methods to design experiment that was used to obtain the optimization of the parameters of DPSO. Finally, two benchmark problems will show the efficiency of this approach.