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

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• 作者：Qiuhua Tang ; Zixiang Li ; Liping Zhang…
• 关键词：two ; sided assembly line balancing ; teaching ; learning ; based optimization algorithm ; variable neighborhood search ; positional constraints ; zoning constraints ; synchronism constraints
• 刊名：Chinese Journal of Mechanical Engineering
• 出版年：2015
• 出版时间：September 2015
• 年：2015
• 卷：28
• 期：5
• 页码：1067-1079
• 全文大小：803 KB
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• 作者单位：Qiuhua Tang (1)
  Zixiang Li (1)
  Liping Zhang (1)
  C. A. Floudas (2)
  Xiaojun Cao (3)
  
  1. Industrial Engineering Department, Wuhan University of Science and Technology, Wuhan, 430081, China
  2. Texas A&M Energy Institute, Texas A&M University, College Station, TX, 77843, USA
  3. Technique Center of Dongfeng Peugeot Citroen Automobile Company, Wuhan, 430056, China
  
• 刊物主题：Mechanical Engineering; Theoretical and Applied Mechanics; Manufacturing, Machines, Tools; Engineering Thermodynamics, Heat and Mass Transfer; Power Electronics, Electrical Machines and Networks; Electronics and Microelectronics, Instrumentation;
• 出版者：Springer Berlin Heidelberg
• ISSN：2192-8258

文摘

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. Keywords two-sided assembly line balancing teaching-learning-based optimization algorithm variable neighborhood search positional constraints zoning constraints synchronism constraints