Abstract
Current technological and market challenges increase the need for development of intelligent systems to support decision making, allowing managers to concentrate on high-level tasks while improving decision response and effectiveness. A Racing based learning module is proposed to increase the effectiveness and efficiency of a Multi-Agent System used to model the decision-making process on scheduling problems. A computational study is put forward showing that the proposed Racing learning module is an important enhancement to the developed Multi-Agent Scheduling System since it can provide more effective and efficient recommendations in most cases.
Keywords
- Multi-agent Scheduling System
- Racing Module
- Job Shop Scheduling Problem (JSSP)
- Classical Operations Research Techniques
- Nominal Removal
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Acknowledgments
This work is supported by FEDER Funds through the “Programa Operacional Factores de Competitividade—COMPETE” program and by National Funds through FCT “Fundação para a Ciência e a Tecnologia” under the projects: PEst-OE/EEI/UI0760/2014 and PTDC/EME-GIN/109956/2009.
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Pereira, I., Madureira, A. (2016). Self-Optimizing A Multi-Agent Scheduling System: A Racing Based Approach. In: Novais, P., Camacho, D., Analide, C., El Fallah Seghrouchni, A., Badica, C. (eds) Intelligent Distributed Computing IX. Studies in Computational Intelligence, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-319-25017-5_26
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DOI: https://doi.org/10.1007/978-3-319-25017-5_26
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