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 dependent 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 paper, we propose a soft computing algorithm to solve this problem by combining modified discrete particle swarm optimization (DPSO) with concept of precedence preservative crossover (PPX) in genetic algorithm. Discrete particle swarm optimization is a population based stochastic optimization technique, and PPX guarantees feasible solution at each of evaluations. Finally, a benchmark problem shows the efficiency of this approach.