Optimization methods for large-scale production scheduling problems

In this paper we present a computational study of optimization methods for production scheduling problems which can be described by a job shop model. Contrary to most existing publications in this field our research focuses on the performance of these methods with respect to large-scale problem instances. The examined methods rely on a graph model as a solution representation and have originally been designed for problems of small size. We apply them to a set of semi-randomly generated problem instances whose properties have been transferred from common (smaller) benchmarks. The experiments are based on tardiness minimization and the results are evaluated in relation to a priority rule based heuristic.