Distributed representation of fuzzy rules and its application to pattern classification
Fuzzy Sets and Systems
Integration of simulation modeling and inductive learning in an adaptive decision support system
Decision Support Systems - Special issue on model management systems
Genetic learning of dynamic scheduling within a simulation environment
Computers and Operations Research - Special issue: heuristic, genetic and tabu search
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Dynamic scheduling I: simulation-based scheduling for dynamic discrete manufacturing
Proceedings of the 35th conference on Winter simulation: driving innovation
A distributed time-driven simulation method for enabling real-time manufacturing shop floor control
Computers and Industrial Engineering
Review: Meta knowledge of intelligent manufacturing: An overview of state-of-the-art
Applied Soft Computing
Evolutionary computing in manufacturing industry: an overview of recent applications
Applied Soft Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
How good are fuzzy If-Then classifiers?
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A framework for the automatic synthesis of hybrid fuzzy/numerical controllers
Applied Soft Computing
Engineering Applications of Artificial Intelligence
The biobjective inventory routing problem: problem solution and decision support
INOC'11 Proceedings of the 5th international conference on Network optimization
Evolving priority scheduling heuristics with genetic programming
Applied Soft Computing
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This paper proposes a fuzzy rule-based system for an adaptive scheduling, which dynamically selects and applies the most suitable strategy according to the current state of the scheduling environment. The adaptive scheduling problem is generally considered as a classification task since the performance of the adaptive scheduling system depends on the effectiveness of the mapping knowledge between system states and the best rules for the states. A rule base for this mapping is built and evolved by the proposed fuzzy dynamic learning classifier based on the training data cumulated by a simulation method. Distributed fuzzy sets approach, which uses multiple fuzzy numbers simultaneously, is adopted to recognize the system states. The developed fuzzy rules may readily be interpreted, adopted and, when necessary, modified by human experts. An application of the proposed method to a job-dispatching problem in a hypothetical flexible manufacturing system (FMS) shows that the method can develop more effective and robust rules than the traditional job-dispatching rules and a neural network approach.