Robustness against short-term sequence alterations is aspired by automotive manufacturers. Sequence alterations in the assembly occur whenever the body of a scheduled car is delayed or required parts are missing. In these situations, the car needs to be taken out of the planned sequence. The resulting gap is rarely left idle to maintain the planned production volume. Instead, either the succeeding car in the sequence is pulled forward or, if available, an adequate substitute car fills the gap. As these cars typically differ from the initially scheduled car, workload changes and potentially work overloads occur. A sequence is robust if sequence alterations do not cause work overloads at the stations. In this paper, we propose an approach that includes the cars' failure likelihoods in sequence planning in order to generate robust sequences. We develop a lexicographic mixed-integer linear program that extends the classical car sequencing problem by a second objective that minimizes the risk of work overloads in the event of failures. Our approach is validated in a simulation study using real-world data from a European OEM.      «

Robustness against short-term sequence alterations is aspired by automotive manufacturers. Sequence alterations in the assembly occur whenever the body of a scheduled car is delayed or required parts are missing. In these situations, the car needs to be taken out of the planned sequence. The resulting gap is rarely left idle to maintain the planned production volume. Instead, either the succeeding car in the sequence is pulled forward or, if available, an adequate substitute car fills the gap. A...     »