A New Hybrid Evolutionary Algorithm for Job-shop Scheduling Problems

In this paper, we present a hybrid method combining Tabu Search (TS) optimization algorithm with the Very Fast Simulated Annealing (VFSA) procedure for the Job-shop Scheduling Problem (JSP). Tabu search algorithms are among the most effective approaches for solving the job shop scheduling problem which is one of the most difficult NP-complete problems. However, neighborhood structures and move evaluation strategies play the central role in the effectiveness and efficiency of the tabu search for the JSP. We have modified the neighborhood selection strategy based on definition of threshold accepting criterion using Very Fast Simulated Annealing algorithm. In fact, in each iteration in TS, all of the moves which satisfy the threshold accepting criterion are selected as the starting points of the next iteration. The value of threshold is decreased by annealing schedule relation which is defined by VFSA algorithm. The proposed hybrid intelligent approach has been run on a set of standard benchmark instances. The numerical examples show that the mentioned hybrid method has better optimality performances than conventional TS algorithms.