Risk-Averse Stochastic Integer Programs for Mixed-Model Assembly Line Sequencing Problems

A variety of optimization formulations have been proposed for mixed-model assembly sequencing problems with stochastic demand and task times. In the real world, however, mixed-model assembly lines are faced with more challenging uncertainties including timely part delivery, material quality, upstream sub-assembly completion and availability of other resources. In addition, sub-assembly lines must meet deadlines imposed by downstream stations. The inevitable disruptions require resequencing. We present a risk-averse stochastic mixed-integer model for mixed-model assembly line resequencing problems to increase on-time performance