Hybrid genetic-Tabu Search approach to scheduling optimization for dual-resource constrained job shop

In order to avoid the premature convergence and to balance the exploration and exploitation abilities of simple GA, a hybrid algorithm is proposed to solve dynamic scheduling problem in flexible production environment. It combines the advantage of global search ability of GA with the self-adaptive merit of Tabu Search (TS) and improves its convergence. It is proved capable of providing optimized schedule to the job-shop where the machine tool and manpower resources are both constrained. After crossover and mutation operations, an optimal or suboptimal scheduling plan can be found. The result of the test shows that this method is feasible and efficient.