The type-II assembly line rebalancing problem considering stochastic task learning
研究了任务时间不固定的装配线再平衡问题，提出一种名为ENCORE的算法，通过计算实验证明其比传统方法更能缩短总生产时间。
Assembly lines with non-constant task time attribute are widely studied in the literature. For the SALBP-II assembly line balancing problem, we take account of stochastic task time changes, which is more practical than the deterministic times often assumed in industrial application. An algorithm – ENCORE, which leverages the traditional algorithm SALOME2, is proposed to address the assembly line balancing problem with stochastic task time attribute. Computational and statistical experiments are conducted to show the efficiency of proposed algorithms over traditional methods with regards to the improvement of total production times.