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International Conference on Management Science and Engineering Management

ICMSEM 2017: Proceedings of the Eleventh International Conference on Management Science and Engineering Management pp 212–222Cite as

Hybrid Multiobjective Evolutionary Algorithm with Differential Evolution for Process Planning and Scheduling Problem

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Part of the book series: Lecture Notes on Multidisciplinary Industrial Engineering ((LNMUINEN))

Abstract

In an intelligent manufacturing environment, process planning and scheduling (PPS) plays a very important role as a most complex and practical scheduling problem, which processes a set of prismatic parts into completed products by determining the optimal process plans and moments to execute each operation with competitive manufacturing resources. Many research works use multiobjective evolutionary algorithm (MOEA) to solve PPS problems with consideration of multiple complicated objectives to be optimized. This paper proposes a hybrid multiobjective evolutionary algorithm with differential evolution (HMOEA-DE) to solve the PPS problem. HMOEA-DE uses a special designed fitness function to evaluate the dominated and nondominated individuals and divides the population and elitism into two parts, which close to the center and edge areas of Pareto frontier. Moreover, differential evolution applied on elitism tries to improve the convergence and distribution performances much more by guiding the search directions through different individuals with different fitness function. Numerical comparisons indicate that the efficacy of HMOEA-DE outperforms the traditional HMOEA without DE in convergence and distribution performances.

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Acknowledgements

This research work is supported by the National Natural Science Foundation of China (U1304609), Foundation for Science & Technology Research Project of Henan Province (162102210044, 152102210068 152102110076), Program for Innovative Research Team (in Science and Technology) in University of Henan Province (17IRTSTHN011), Program for Key Project of Science and Technology in University of Education Department of Henan Province (17A520030), Fundamental Research Funds for the Henan Provincial Colleges and Universities (2014YWQQ12, 2015XTCX03, 2015XTCX04), Research Funds for Key Laboratory of Grain Information Processing and Control (Henan University of Technology) (KFJJ-2015-106), Ministry of Education and the Grant-in-Aid for Scientific Research (C) of Japan Society of Promotion of Science (JSPS): No. 15K00357.

Author information

Authors and Affiliations

  1. Henan University of Technology, Zhengzhou, People’s Republic of China

    Chunxiao Wang, Wenqiang Zhang & Le Xiao

  2. Key Laboratory of Grain Information Processing and Control, Ministry of Education, Zhengzhou, People’s Republic of China

    Chunxiao Wang, Wenqiang Zhang & Le Xiao

  3. Fuzzy Logic Systems Institute and Tokyo University of Science, Tokyo, Japan

    Mitsuo Gen

Authors
  1. Chunxiao Wang
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  2. Wenqiang Zhang
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  3. Le Xiao
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  4. Mitsuo Gen
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Corresponding author

Correspondence to Wenqiang Zhang .

Editor information

Editors and Affiliations

  1. Business and Administration, Sichuan University, Chengdu, China

    Jiuping Xu

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

    Mitsuo Gen

  3. Azerbaijan National Academy of Sciences, Baku, Azerbaijan

    Asaf Hajiyev

  4. Monash University, Clayton, Victoria, Australia

    Fang Lee Cooke

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Wang, C., Zhang, W., Xiao, L., Gen, M. (2018). Hybrid Multiobjective Evolutionary Algorithm with Differential Evolution for Process Planning and Scheduling Problem. In: Xu, J., Gen, M., Hajiyev, A., Cooke, F. (eds) Proceedings of the Eleventh International Conference on Management Science and Engineering Management. ICMSEM 2017. Lecture Notes on Multidisciplinary Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-59280-0_17

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  • DOI: https://doi.org/10.1007/978-3-319-59280-0_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59279-4

  • Online ISBN: 978-3-319-59280-0

  • eBook Packages: EngineeringEngineering (R0)

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