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Advisor(s)
Abstract(s)
Scheduling is a critical function that is present throughout many industries and applications. A great need exists for developing scheduling approaches that can be applied to a number of different scheduling problems with significant impact on performance of business organizations. A challenge is emerging in the design of scheduling support systems for manufacturing environments where dynamic adaptation and optimization become increasingly important. In this paper, we describe a Self-Optimizing Mechanism for Scheduling System through Nature Inspired Optimization Techniques (NIT).
Description
Keywords
Self-optimization Multi-agent learning Autonomic computing Multi-agent systems Nature inspired optimization techniques
Citation
Publisher
Springer Netherlands