Competitive methods for multi-level lot sizing and scheduling: tabu search and randomized regrets

Abstract

This contribution presents two heuristic approaches for multi-level, single-machine lot sizing and scheduling. The first one is a variant of a so-called randomized regret based heuristic which is assumed to be the fastest available method for this particular class of problems. The second approach is a tabu search technique that is competitive with respect to both the run-time performance and the average deviation from the optimum objective function values.