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Asia-Pacific Conference on Simulated Evolution and Learning

SEAL 2017: Simulated Evolution and Learning pp 671–683Cite as

Bi-objective Water Cycle Algorithm for Solving Remanufacturing Rescheduling Problem

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10593))

Abstract

This paper researches on the remanufacturing rescheduling problems (RRP) for new job insertion. The objective is to minimize the total flow time and the instability at the same time. A bi-objective function is developed for RRP and water cycle algorithm (WCA) is employed and improved to solve the problem. A discretization strategy is proposed to make the WCA applicable for handling the RRP. An ensemble of local search operators is developed to improve the performance of the discrete WCA (DWCA) algorithm. Six real-life remanufacturing cases with different scales are solved by DWCA. The results and comparisons indicate the superiority of the proposed DWCA scheme over the famous bi-objective algorithm, NSGAII.

This work was supported by National Nature Science Foundation under Grant 61603169, 61374187. This work was conducted within the Delta-NTU Corporate Lab for Cyber-Physical Systems with funding support from Delta Electronics Inc and the National Research Foundation (NRF) Singapore under the Corp Lab@University Scheme.

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Author information

Authors and Affiliations

  1. School of Computer, Liaocheng University, Liaocheng, China

    Kaizhou Gao & Junqing Li

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kaizhou Gao & Rong Su

  3. School of Information Science and Engineering, Shandong Normal University, Jinan, China

    Peiyong Duan

Authors
  1. Kaizhou Gao
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  2. Peiyong Duan
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  3. Rong Su
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  4. Junqing Li
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Corresponding author

Correspondence to Peiyong Duan .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  2. City University of Hong Kong, Hong Kong, Kowloon, Hong Kong

    Kay Chen Tan

  3. Victoria University of Wellington, Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. Southern University of Science and Technology, Shenzhen, China

    Ke Tang

  5. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  6. City University of Hong Kong, Kowloon Tong, Hong Kong

    Qingfu Zhang

  7. Peking University, Beijing, China

    Ying Tan

  8. University of Leipzig, Leipzig, Germany

    Martin Middendorf

  9. University of Surrey, Guildford, Surrey, United Kingdom

    Yaochu Jin

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Gao, K., Duan, P., Su, R., Li, J. (2017). Bi-objective Water Cycle Algorithm for Solving Remanufacturing Rescheduling Problem. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_54

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  • DOI: https://doi.org/10.1007/978-3-319-68759-9_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68758-2

  • Online ISBN: 978-3-319-68759-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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