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In most real-world industries, scheduling is processed in a stochastic and dynamic environment, it is necessary to generate a schedule which is suitable for the current system states. Dynamic scheduling solves unimplemented jobs and updates an existing schedule based on the real-time information with minimizing the deviation between the new and original schedules. In this paper, we investigate the dynamic hybrid flow shop scheduling problem and propose a dynamic differential evolution algorithm. In the algorithm, the search space shifts as new jobs arrive, and the problem is solved on a moving horizon based on the real-time information, after the new schedule is established, we update the current schedule. Experiments are carried out to prove the effectiveness of the proposed algorithm.

A Dynamic Differential Evolution Algorithm for Dynamic Hybrid Flow Shop Scheduling Problem