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.

翻译：在汽车工业中，部分故障车辆因材料短缺、涂装失效等原因无法按计划顺序生产，这些车辆被从序列中剔除，可能导致工作量超负荷增加。另一方面，故障车辆的重插入需根据出现的合适位置动态执行。若此类位置不足，要么待重插入车辆积累，要么以牺牲生产效率为代价在较差位置进行重插入。本研究针对存在随机产品故障及集成重插入过程的混合模型排序问题，提出了一种双目标两阶段随机规划模型及公式改进方法。此外，开发了进化优化算法、两阶段局部搜索算法及混合方法。基于案例研究的数值实验表明：混合算法能更优地探索帕累托前沿表征，而局部搜索算法在工作量超负荷目标上提供更可靠的解决方案。最后，动态重插入仿真结果显示，考虑车辆故障并将重插入过程集成至混合模型排序问题后，可减少约20%的工作量超负荷，同时显著缩短故障车辆的等待时间。