  In the automotive industry, some vehicles, failed vehicles, cannot be
produced according to the planned schedule due to some reasons such as material
shortage, paint failure, etc. These vehicles are pulled out of the sequence,
potentially resulting in an increased work overload. On the other hand, the
reinsertion of failed vehicles is executed dynamically as suitable positions
occur. In case such positions do not occur enough, either the vehicles waiting
for reinsertion accumulate or reinsertions are made to worse positions by
sacrificing production efficiency.
  This study proposes a bi-objective two-stage stochastic program and
formulation improvements for a mixed-model sequencing problem with stochastic
product failures and integrated reinsertion process. Moreover, an evolutionary
optimization algorithm, a two-stage local search algorithm, and a hybrid
approach are developed. Numerical experiments over a case study show that while
the hybrid algorithm better explores the Pareto front representation, the local
search algorithm provides more reliable solutions regarding work overload
objective. Finally, the results of the dynamic reinsertion simulations show
that we can decrease the work overload by ~20\% while significantly decreasing
the waiting time of the failed vehicles by considering vehicle failures and
integrating the reinsertion process into the mixed-model sequencing problem.
