Journal of Central South University

, Volume 25, Issue 2, pp 315–330 | Cite as

A hybrid algorithm based on tabu search and large neighbourhood search for car sequencing problem

  • Xiang-yang Zhang (张向阳)
  • Liang Gao (高亮)
  • Long Wen (文龙)
  • Zhao-dong Huang (黄兆东)


The car sequencing problem (CSP) concerns a production sequence of different types of cars in the mixed-model assembly line. A hybrid algorithm is proposed to find an assembly sequence of CSP with minimum violations. Firstly, the hybrid algorithm is based on the tabu search and large neighborhood search (TLNS), servicing as the framework. Moreover, two components are incorporated into the hybrid algorithm. One is the parallel constructive heuristic (PCH) that is used to construct a set of initial solutions and find some high quality solutions, and the other is the small neighborhood search (SNS) which is designed to improve the new constructed solutions. The computational results show that the proposed hybrid algorithm (PCH+TLNS+SNS) obtains 100 best known values out of 109 public instances, among these 89 instances get their best known values with 100% success rate. By comparing with the well-known related algorithms, computational results demonstrate the effectiveness, efficiency and robustness of the proposed algorithm.

Key words

car sequencing problem large neighborhood search tabu search ratio constraint 



汽车排序问题涉及混流装配线上由多种车型组成的一个加工序列, 一个混合算法用以搜索违约数最小的序列。 该混合算法以禁忌搜索和大邻域搜索为算法框架, 结合了两个组件以提高算法性能。 一个是平行构建启发式方法, 构建一系列初解用于选择高质量的解, 另一个是小邻域搜索, 进一步改进新解的质量。 计算结果显示, 针对 109 个问题的公共测试集, 该算法得到 100 个已知最好解, 89 个问题得到最好解的成功率是 100%。 结果表明, 与知名相关算法比较, 该算法具有有效性、 高效率和鲁棒性。


汽车排序问题 大邻域搜索 禁忌搜索 比率约束 


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Copyright information

© Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Xiang-yang Zhang (张向阳)
    • 1
    • 2
    • 3
  • Liang Gao (高亮)
    • 2
  • Long Wen (文龙)
    • 2
  • Zhao-dong Huang (黄兆东)
    • 1
    • 3
  1. 1.Faulty of Maritime and TransportationNingbo UniversityNingboChina
  2. 2.State Key Laboratory of Digital Manufacturing Equipment & TechnologyHuazhong University of Science & TechnologyWuhanChina
  3. 3.National Traffic Management Engineering & Technology Research Centre Ningbo University Sub-centerNingbo UniversityNingboChina

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