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.
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References
Baker, K.R., Trietsch, D.: Principles of Sequencing and Scheduling. Wiley, New York (2009)
Beasley J.E.: OR-Library: distributing test problems by electronic mail. J. Oper. Res. Soc. 41, 1069–1072 (1990)
Birattari, M.: Tuning Metaheuristics: A Machine Learning Perspective. Springer, Berlin (2009)
Birattari, M., Balaprakash, P., Dorigo, M.: The ACO/F-RACE Algorithm for Combinatorial Optimization Under Uncertainty Metaheuristics, pp. 189–203, Springer, Berlin (2007)
Conover, W.J.: Practical Nonparametric Statistics: Wiley, New York (1999)
Lau, H.C., Zhao, Z.J., Ge, S.S., Lee, T.-H.: Allocating resources in multiagent flowshops with adaptive auctions. Autom. Sci. Eng. 8(4) (2011)
Madureira, A.: Aplicação de Meta-Heurísticas ao Problema de Escalonamento em Ambiente Dinâmico de Produção Discreta. Ph.D. thesis, Universidade do Minho (2003)
Madureira, A., Santos, J., Pereira, I.: MASDSheGATS—scheduling system for dynamic manufacturing environments. In: Ahmed S., Karsiti, M.N. (eds.) Multi Agent Systems.Chapter 17. In-Tech, pp. 333–342. Vienna, Austria. ISBN: 978-3-902613-51-6, (2009a). doi:10.5772/6609
Madureira, A., Santos, J., Pereira, I.: A hybrid intelligent system for distributed dynamic scheduling. In: Chiong, R., Dhakal, S. (eds.) Natural Intelligence for Scheduling, Planning and Packing Problems, vol. 250 of Studies in Computational Intelligence, pp. 295–324. Springer-Verlag, Berlin, Heidelberg. ISBN: 978-3-642-04038-2, (2009b). doi:10.1007/978-3-642-04039-9_12
Pan, Q., Wang, L., Mao, K., Zhao, J.-H., Zhang, M.: An effective artificial bee colony algorithm for a real-world hybrid flowshop problem in steelmaking process. Autom. Sci. Eng. 10(2), 307–322 (2013)
Pinedo, M.: Scheduling: Theory, Algorithms, and Systems. Springer, New York (2012)
Pereira, I.: Sistema Inteligente para Escalonamento Assistido por Aprendizagem. Ph.D. thesis in Electrical and Computer Engineering. UTAD (2014) (in portuguese)
Pereira, I., Madureira, A.: Self-optimization module for scheduling using case-based reasoning. Appl. Soft Comput. 13(3), 1419–1432 (2013)
Vincent, L., Ponnambalam, S.G.: A differential evolution-based algorithm to schedule flexible assembly lines. Autom. Sci. Eng. 10(4) (2013)
Yang, S., Zhang, M.T., Yi, J., Zhang, L., Zheng, L.: Bottleneck station scheduling in semiconductor assembly and test manufacturing using ant colony optimization. Autom. Sci. Eng. 4(4), 569–578 (2007)
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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