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The work presented in this paper is a continuation for efforts to devise a complete multiple agents' framework for real-time dynamic job shop scheduling, which considers robustness and adaptability. Previous work has been reported in Liu, N. et al, 2004. The framework inherits the advantages of decentralized models, such as flexibility, robustness, and high fault tolerance. The framework is actually a job dispatching procedure, a completely reactive scheduling approach, combining real time decision making with predictive decision-making based on optimization. It can solve various disruptions as flexibly as dispatching rules. This paper provides an experimental justification of the arguments presented above using computational experiments on dynamic job arrivals. First, it compares computational results on unpredictable job arrivals among the presented framework and commonly used dispatching rules to show the effectiveness and efficiency of the framework. Then it compares computational results among four cases of dynamic job arrivals to demonstrate the effects of making full use of available information of disruptions.