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Advances in Hybrid Metaheuristics for Stochastic Manufacturing Scheduling: Part II Case Studies

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Proceedings of the Tenth International Conference on Management Science and Engineering Management

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 502))

Abstract

Manufacturing Scheduling plays a very important role in the intelligent manufacturing system, where it can have a major impact on the productivity of a production process. However, it is very difficult to find an optimal solution for manufacturing scheduling problems since most of them fall into the class of NP-hard problem. Because real world manufacturing problems often contain nonlinearities, multiple objectives conflicting each other and also uncertainties that are too complex to be modeled analytically. In these environments, hybrid metaheuristic based optimization is a powerful tool to find optimal system settings to the stochastic manufacturing scheduling problems. Evolutionary algorithm (EA) in hybrid metaheuristics is a generic population-based metaheuristic, which can find compromised optimal solutions well for a complicated manufacturing scheduling problem. By using the hybrid sampling strategy-based EA (HSS-EA) and the multi-objective estimation of distribution algorithm (MoEDA), we survey several case studies such as stochastic multi-objective jobshop scheduling problem (S-MoJSP), stochastic multi-objective assembly line balancing (S-MoALB) problem and stochastic multi-objective resource-constrained project scheduling problem (S-MoRcPSP) with numerical experimental results to get the better efficacy and efficiency than existing NSGA-II, SPEA2 and awGA algorithms.

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Acknowledgments

This work is partly supported by the Grant-in-Aid for Scientific Research (C) of Japan Society of Promotion of Science (JSPS): No. 15K00357, National Science Foundation of China: No.61572100, Fundamental Research Funds for the Central Universities No. DUT15QY10, National Natural Science Foundation of China: No. U1304609, the Key Young Teacher Training Program of Henan University of Technology, the Fundamental Research Funds for the Henan Provincial Colleges and Universities No. 2014YWQQ12, and Research Funds for Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education, China.

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Authors and Affiliations

  1. Fuzzy Logic Systems Institute, Tokyo University of Science, Tokyo, Japan

    Mitsuo Gen

  2. College of Information Science and Engineering, Henan University of Technology, Hunan, People’s Republic of China

    Wenqiang Zhang

  3. Graduate School of Information, Production and Systems, Waseda University, Tokyo, Japan

    Xinchang Hao

Authors
  1. Mitsuo Gen
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  2. Wenqiang Zhang
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  3. Xinchang Hao
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Corresponding author

Correspondence to Mitsuo Gen .

Editor information

Editors and Affiliations

  1. Business School, Sichuan University, Chengdu, Sichuan, China

    Jiuping Xu

  2. Azerbaijan National Academy of Sciences, Baku, Azerbaijan

    Asaf Hajiyev

  3. Karlsruhe Institute of Technology, Karlsruhe, Baden-Württemberg, Germany

    Stefan Nickel

  4. Tokyo University of Science, Fuzzy Logic Systems Institute, Tokyo, Japan

    Mitsuo Gen

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Gen, M., Zhang, W., Hao, X. (2017). Advances in Hybrid Metaheuristics for Stochastic Manufacturing Scheduling: Part II Case Studies. In: Xu, J., Hajiyev, A., Nickel, S., Gen, M. (eds) Proceedings of the Tenth International Conference on Management Science and Engineering Management. Advances in Intelligent Systems and Computing, vol 502. Springer, Singapore. https://doi.org/10.1007/978-981-10-1837-4_89

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  • DOI: https://doi.org/10.1007/978-981-10-1837-4_89

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-1836-7

  • Online ISBN: 978-981-10-1837-4

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