A Hybrid EA for Reactive Flexible Job-shop Scheduling

In this paper, we consider a reactive flexible job-shop scheduling problem (rFJSP) under uncertainty environment. The most existing reactive scheduling methods are characterized by least commitment strategies such as real-time dispatching that create partial schedules based on local information. In rFJSP, two extensions of these dispatching strategies are to allow the system to select multiple machines assignment, and multiple operation process for each job. So, how to design an effective flexible rescheduling strategy is the key point of this paper. For solving this rFJSP, we propose a hybrid evolutionary algorithm (hEA) with combining genetic algorithm (GA) and particle swarm optimization (PSO). Finally, the experiments verify the effectiveness of proposed hEA, by comparing with different evolutionary approaches for several scale test problems of rFJSP.