Genetic algorithm using sequence rule chain for multi-objective optimization in re-entrant micro-electronic production line

Abstract

Re-entrant production lines, such as those which occur in micro-electronic wafer fabrication, are characterized by a product routing that consists of multiple visits to a facility during the manufacturing process. With the development of micro-electronic technology, the research on the scheduling and control problem of re-entrant micro-electronic production line has attracted more and more people from both academia and industry to study and has become a challenging research subject. Some results of the scheduling of re-entrant micro-electronic production line based on heuristic sequence rules have been obtained. However, performances of these sequence rules are not good enough in re-entrant micro-electronic production line because of their sensitivity to the variation of types of production line. A genetic algorithm using sequence rule chain for multi-objective optimization in re-entrant micro-electronic production line is proposed in this paper. Comparisons between the proposed algorithm and some other typical sequence rules have been made through the simulations of a practical micro-electronic production line. The static and dynamic simulation results show that the algorithm has considerable improvements on performances of the micro-electronic production such as mean cycle time, mean number of work-in-process, production rate.