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Effective hybrid teaching-learning-based optimization algorithm for balancing two-sided assembly lines with multiple constraints

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Abstract

Due to the NP-hardness of the two-sided assembly line balancing (TALB) problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints (TALB-MC). Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization (TLBO) algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost function. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search (VNS) is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm (LAHC) for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.

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Authors and Affiliations

  1. Industrial Engineering Department, Wuhan University of Science and Technology, Wuhan, 430081, China

    Qiuhua Tang, Zixiang Li & Liping Zhang

  2. Texas A&M Energy Institute, Texas A&M University, College Station, TX, 77843, USA

    C. A. Floudas

  3. Technique Center of Dongfeng Peugeot Citroen Automobile Company, Wuhan, 430056, China

    Xiaojun Cao

Authors
  1. Qiuhua Tang
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  2. Zixiang Li
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  3. Liping Zhang
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  4. C. A. Floudas
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  5. Xiaojun Cao
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Corresponding author

Correspondence to Qiuhua Tang.

Additional information

Supported by National Natural Science Foundation of China (Grant Nos. 51275366, 50875190, 51305311) and Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20134219110002)

TANG Qiuhua, born in 1970, is currently a professor at Wuhan University of Science and Technology, China. She received her PhD degree from Wuhan University of Science and Technology, China, in 2005. Her research interests include production planning and scheduling and industrial engineering.

LI Zixiang, born in 1990, is currently a postgraduate at Wuhan University of Science and Technology, China. He received his bachelor degree from Wuhan University of Science and Technology, China, in 2013.

ZHANG Liping, is currently a lecturer at Wuhan University of Science and Technology, China. She received her PhD degree from Huazhong University of Science and Technology, China, in 2013. Her research interest include dynamic scheduling, job shop scheduling and intelligent algorithm.

FLOUDAS C A, is currently a professor at Texas A&M University, USA. His research interests include product and process systems engineering, and bioinformatics and computational genomics.

CAO Xiaojun, born in 1974, is currently a senior engineer at Technique Center of Dongfeng Peugeot Citroen Automobile Company, China. He received his bachelor degree from Huazhong University of Science and Technology, China, in 1996.

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Tang, Q., Li, Z., Zhang, L. et al. Effective hybrid teaching-learning-based optimization algorithm for balancing two-sided assembly lines with multiple constraints. Chin. J. Mech. Eng. 28, 1067–1079 (2015). https://doi.org/10.3901/CJME.2015.0630.084

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  • DOI: https://doi.org/10.3901/CJME.2015.0630.084

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