Backup strategy for robots' failures in an automotive assembly system

Automotive assembly lines are often characterized by robots' failures that may result in stoppages of the lines and manual backup of tasks. The phenomena tend to impair throughput rate and products' quality. This paper presents a backup strategy in which working robots perform tasks of failed robots. The proposed Mixed-Integer Linear-Programming based approach minimizes the throughput loss by utilizing the robots' redundancy in the system. Two algorithms are developed to comply with stochastic conditions of a real-world environment. The performance of these algorithms is compared with several heuristics, and the downstream-backup based algorithm is found superior to all other methods.