Arc-Flow Approach For Single Batch-Processing Machine Scheduling

We address the problem of scheduling jobs with non-identical sizes and distinct processing times on a single batch-processing machine, aiming at minimizing the makespan. The extensive literature on this NP-hard problem mostly focuses on heuristics. Using an arc-flow based optimization approach, we construct a novel formulation that represents it as a problem of determining flows in graphs. The size of the formulation increases with the machine capacity and with the number of distinct sizes and processing times among the jobs, but it does not increase with the number of jobs, which makes it very effective to solve large instances to optimality, especially when multiple jobs have equal size and processing time. We compare our model to other models from the literature, showing its clear superiority on benchmark instances and proving optimality of random instances with up to 100 million jobs.