Constraint-based winner determination for auction-based scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Distributed text classification with an ensemble kernel-based learning approach
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Information Sciences: an International Journal
An Agent Based Approach to Patient Scheduling Using Experience Based Learning
International Journal of Agent Technologies and Systems
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This paper presents a complete multiagent framework for dynamic job shop scheduling, with an emphasis on robustness and adaptability. It provides both a theoretical basis and some experimental justifications for such a framework: a job dispatching procedure for a completely reactive scheduling approach, combining real-time and predictive decision making. It resolves various disruptions as flexibly as dispatching rules while providing more stability. It is ready to be implemented in a distributed environment where agents have minimum global information thereby improving system fault tolerance. Computational experiments on dynamic job arrivals provide the experimental justification of the framework. First, a comparison of computational results on unpredictable job arrivals among the presented framework and commonly used dispatching rules is presented to show the effectiveness and robustness of the developed framework. Then, a comparison of the computational results among four cases of dynamic job arrivals is presented to demonstrate the effects of making full use of available uncertain information about disruptions using this framework for the enhancement of scheduling robustness.