Comparison of iterative improvement techniques for schedule optimization

Due to complexity reasons of realistic scheduling applications, often iterative improvement techniques that perform a kind of local search to improve a given schedule are proposed instead of enumeration techniques that guarantee optimal solutions. In this paper we describe an experimental comparison of four iterative improvement techniques for schedule optimization that differ in the local search methodology. These techniques are iterative deepening, random search, tabu search and genetic algorithms. To compare the performance of these techniques, we use the same evaluation function, knowledge representation and data from one application. The evaluation function is defined on the gradual satisfaction of explicitly represented domain constraints and optimization functions. The satisfactions of individual constraints are weighted and aggregated for the whole schedule. We have applied these techniques on data of a steel making plant in Linz (Austria). In contrast to other applications of iterative improvement techniques reported in the literature, our application is constrained by a greater variety of antagonistic criteria that are partly contradictory.