Abstract: Disassembly is the first critical step of product recovery which affects the efficiency of the recovery process directly. The main objectives to be achieved for the Disassembly Line Balancing Problem（DLBP） are as follows： minimize the idle time, equilibrate workload, remove hazardous and high-demand components as early as possible and minimize the disassembly cost. To overcome the shortcomings of conventional algorithms in solving DLBP, a new approach based on the Bacteria Foraging Optimization（BFO） algorithm is proposed. The paper ameliorates the bacteria moving rules, adds a global information sharing strategy and develops an adaptive elimination and dispersal probability for the purpose of improving the performance of the BFO algorithm. Finally, the improved algorithm is tested in series of numerical experiments with different sizes. The results indicate the validity of the proposed algorithm.

Key words: disassembly line balancing, intelligent optimization, bacteria foraging optimization

摘要： 拆卸是产品回收过程中最重要的环节，拆卸过程高效与否直接影响产品的回收效率。为克服传统算法求解拆卸线平衡问题时性能不稳定的缺陷，在构建基于工作站利用率、负荷均衡，尽早拆卸有危害、高需求的零件，最小化拆卸成本等方面的拆卸线平衡问题多目标优化模型的基础上，提出一种改进的细菌觅食优化算法对问题求解。通过改进细菌的移动规则扩大搜索空间，引入全局信息共享策略增强算法收敛性能，定义了一种自适应驱散概率防止驱散操作中解的退化。在对不同规模算例的对比分析中，验证了该算法的有效性。

关键词: 拆卸线平衡, 智能优化, 细菌觅食优化算法