Sequencing decisions in mixed-model assembly lines are complicated by various uncertainty factors. This paper addresses a real-life uncertainty factor identified in a manufacturer of large vehicles, by modelling unreliable part delivery and quality. Stochastic optimisation is applied to find sequencing policies that improve the on-time performance of its mixed-model assembly lines. As schedulers have different levels of risk aversion, a risk-averse programme is further presented to protect against the decision maker’s chosen fraction of worst scenarios. Computational studies with Progressive Hedging as the solution method, and its lower bounding approach, demonstrate the high quality of resulting sequencing decisions and the time efficiency of the solution method.

混合模型装配线中的排序决策因各种不确定因素而变得复杂。本文通过对不可靠的零件交付和质量进行建模，解决了大型车辆制造商中确定的现实不确定因素。随机优化被应用于寻找提高混合模型装配线准时性能的排序策略。由于调度程序具有不同程度的风险规避，因此进一步提出了风险规避程序以防止决策者选择的最坏情况的一部分。以渐进式对冲作为解决方法及其下限方法的计算研究证明了结果排序决策的高质量和解决方法的时间效率。