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 ...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 fimction. 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.展开更多
在分析双边装配线第Ⅰ类平衡问题(Two-sided Assembly Line Balancing Problem of Type-Ⅰ,TALBP-Ⅰ)离散性、序列相关性等特点后,提出了一种改进离散蝙蝠算法(Improved Discrete Bat Algorithm,IDBA)。为在总工位数相同情况下筛选出更...在分析双边装配线第Ⅰ类平衡问题(Two-sided Assembly Line Balancing Problem of Type-Ⅰ,TALBP-Ⅰ)离散性、序列相关性等特点后,提出了一种改进离散蝙蝠算法(Improved Discrete Bat Algorithm,IDBA)。为在总工位数相同情况下筛选出更优质的解,增加了启发式目标,引导种群向更优方向搜索。标准蝙蝠算法不能直接求解离散问题,针对TALBP-Ⅰ,设计了基于任务拓扑排序矩阵的编码策略,利用双重编码映射机制,实现蝙蝠飞行的连续物理空间到TALBP-Ⅰ离散解空间的映射。采用改进的"工位-操作"解码方法代替传统的"操作-工位"解码方法,减少工位的空闲时间。针对蝙蝠算法后期收敛速度慢,易陷入局部最优,设计了4种插入邻域算子,进行变邻域搜索。通过基准问题的数值实验验证了算法的有效性。展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.51275366,50875190,51305311)Specialized Research Fund for the Doctoral Program of Higher Education of China(Grant No.20134219110002)
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 fimction. 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.
文摘在分析双边装配线第Ⅰ类平衡问题(Two-sided Assembly Line Balancing Problem of Type-Ⅰ,TALBP-Ⅰ)离散性、序列相关性等特点后,提出了一种改进离散蝙蝠算法(Improved Discrete Bat Algorithm,IDBA)。为在总工位数相同情况下筛选出更优质的解,增加了启发式目标,引导种群向更优方向搜索。标准蝙蝠算法不能直接求解离散问题,针对TALBP-Ⅰ,设计了基于任务拓扑排序矩阵的编码策略,利用双重编码映射机制,实现蝙蝠飞行的连续物理空间到TALBP-Ⅰ离散解空间的映射。采用改进的"工位-操作"解码方法代替传统的"操作-工位"解码方法,减少工位的空闲时间。针对蝙蝠算法后期收敛速度慢,易陷入局部最优,设计了4种插入邻域算子,进行变邻域搜索。通过基准问题的数值实验验证了算法的有效性。
