Instances and detailed results of the paper Stochastic scheduling on a restricted batching machine

In this research, we consider a batching machine that can process several jobs at the same time. Batches of jobs have a restricted size, and the processing time of a given batch is equal to the largest processing time among all jobs within the batch. We consider minimizing the maximum lateness under uncertainty on the processing time of jobs. This function is particularly relevant in manufacturing environments where these machines are present, as meeting due dates is crucial on these bottleneck machines. We have developed mixed integer programming formulations and solved the stochastic problem using both a Benders decomposition approach and a biased random-key genetic algorithm. Our results show that we can solve instances of reasonable size and draw conclusions about the impact of different settings of uncertainty on schedules and maximum lateness.