Abstract: Based on genetic agorithms (Gas)，a scheduling approach is presented，which can be used to address the job shop scheduling problem in dynamic manufacturing systems constrained by machines, workers and robots. A new chromosome representation is also presented for batch process scheduling and its length is variable. In the dynamic environment，jobs arrive continuously，machines may be broken and repaired，due date of job may change，a new class job comes up during processing. Inspired by the rolling horizon optimization method from predictive control technology，a periodic and event-driven rolling horizon scheduling is utilized for adaptation to continuous processing in a changing environment. The algorithm takes into account dispatching rules with variable weights in the performance function. Simulation results show that the strategy is more suitable for a dynamic job shop environment than the static scheduling strategy.

Key words: Dynamic job-shop scheduling, Genetic algorithm, Multi-resources