A neuro-tabu search heuristic for the flow shop scheduling problem

Flow shop scheduling deals with the sequencing of different jobs that visit a set of machines in the same order. A neural networks-based tabu search method, namely EXTS, is proposed for the flow shop scheduling. Unlike the other tabu search-based methods, the proposed approach helps diminishing the tabu effect in an exponential way rather than most commonly used way of diminishing it in a sudden manner. On the basis of the conducted tests, some rules are evolved to set the values for different parameters. The effectiveness of the proposed method is tested with 23 problems selected from literature. The computational results indicate that the proposed approach is effective in terms of reduced makespan for the attempted problems.