In this paper, an improved decoding procedure with embedded heuristic rules is proposed to generate better active schedules in applying genetic algorithms to a scheduling problem of a metal mold assembly process which is modeled as a large-scale jobshop with additional constraints. An experiment result shows that the proposed algorithm outperforms the algorithms proposed previously and gives better suboptimal solutions for real data.
