Multi-objective Evolutionary Algorithm with Strong Convergence of Multi-area for Assembly Line Balancing Problem with Worker Capability
Multi-objective Evolutionary Algorithm with Strong Convergence of Multi-area for Assembly Line Balancing Problem with Worker Capability
复制标题
DOI：
10.1016/j.procs.2013.09.243
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Wenqiang Zhang;Weitao Xu;M. Gen
中科院分区：
文献类型：
--
作者：
Wenqiang Zhang;Weitao Xu;M. Gen
考虑工人能力的多目标装配线平衡问题是经典装配线平衡问题中的一个现实而重要的问题。在给定固定工位数限制的条件下，以最小化生产周期和总人工成本为目标，提出了一种多区域强收敛的多目标进化算法（MOEA-SCM）。该算法采用特殊的适应度函数策略，考虑个体间的支配与被支配关系，采用混合选择机制，使个体向Pareto前沿的多个区域收敛。这种多区域强收敛的能力可以保持算法的收敛性和均匀分布性能。数值算例表明，MOEA-SCM比现有的MOEA-SCM具有更好的收敛分布性能。
Multiobjective assembly line balancing with worker capability (moALB-wc) is a realistic and important issue from classical assembly line balancing (ALB) problem involving conflicting criteria such as the cycle time, the total worker cost, and/or the variation of workload. This paper proposes a multiobjective evolutionary algorithm (MOEA) with strong convergence of multi- area (MOEA-SCM) to deal with moALB-wc problem considering minimization of the cycle time and total worker cost, given a fixed number of station limit. It adopts special fitness function strategy considering dominating and dominated relationship among individuals and hybrid selection mechanism so as to the individuals could converging toward the multiple areas of Pareto front. Such ability to strong convergence of multi-area could preserve both the convergence and even distribution performance of proposed algorithm. Numerical comparisons with various problem instances show that MOEA-SCM could get the better convergence distribution performance than existing MOEAs.