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Station ant colony optimization for the type 2 assembly line balancing problem

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Abstract

An improved ant colony optimization (ACO), namely, station ant colony optimization (SACO), is proposed to solve the type 2 assembly line balancing problem (ALBP-2). In the algorithm, ACO is employed to search different better combinations of tasks (component solutions) for each station; an iteration compress mechanism is proposed to reduce the searching space of feasible solutions of ALBP-2. Three heuristic factors [i.e., (1) task time, (2) number of successors, and (3) number of releasable successors], two pheromones, and a task assignment mechanism are proposed to search better component solutions for every station. Finally, the effectiveness and stability of SACO are confirmed through comparison with literatures in 23 instances included in nine examples.

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Authors and Affiliations

  1. Computer College, Wuhan University, Wuhan, 430072, China

    Qiaoxian Zheng & Yuanxiang Li

  2. Faculty of Mathematic & Computer Science, Hubei University, Wuhan, 430062, China

    Qiaoxian Zheng

  3. Science College, Wuhan University of Science and Technology, Wuhan, 430065, China

    Ming Li

  4. College of Mechanics and Automation, Wuhan University of Science and Technology, Wuhan, 430065, China

    Qiuhua Tang

Authors
  1. Qiaoxian Zheng
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  2. Ming Li
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  3. Yuanxiang Li
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  4. Qiuhua Tang
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Correspondence to Qiaoxian Zheng.

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Zheng, Q., Li, M., Li, Y. et al. Station ant colony optimization for the type 2 assembly line balancing problem. Int J Adv Manuf Technol 66, 1859–1870 (2013). https://doi.org/10.1007/s00170-012-4465-9

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  • DOI: https://doi.org/10.1007/s00170-012-4465-9

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