求解随机拆卸线平衡问题的改进人工蜂群算法

An Improved Artificial Bee Colony Algorithm for Solving Stochastic Disassembly Line Balancing

考虑拆卸时间不确定，以最少工作站开启数量、负荷均衡、尽早拆除有危害和高需求零部件为目标，建立随机拆卸线平衡问题优化模型，并提出一种改进人工蜂群算法。在雇佣蜂、观察蜂开采蜜源阶段，采用变邻域深度搜索策略，通过系统改变邻域结构以增强开采能力；在侦察蜂探索蜜源阶段，构建基于左右变异机制的全局学习策略，以提高探索蜜源质量加速跳出局部最优。最后，通过求解不同规模算例并与其他算法对比，验证所提算法的优越性。

Considering the uncertainty of disassembly time, a stochastic disassembly line balancing problem （SDLBP）model was constructed to minimize the number of opened workstations,distribute idle time across workstations evenly, and process hazardous/high-demand parts early. Then an improved artificial bee colony（IABC） algorithm was proposed to solve this problem. In the IABC, the employed/onlooker bees exploit new food sources using variable neighborhood descent strategy to enhance the ability of exploitation. The scout bees explore new food sources by performing one point left/right operator on the best-so-far food source to accelerate the speed of finding new high-quality solutions and escape from local optima rapidly. The IABC is applied to solve three different scale instances and compared with several other methods. The results show the superiority of the IABC in solving the SDLBP.