Comparison of Iterative Improvement Techniques for Schedule Optimization * Jürgen Dorn, Mario Girsch, Günther Skele, Wolfgang Slany Christian Doppler Laboratory for Expert Systems Technical University of Vienna Paniglgasse 16, A-1040 Vienna, Austria Email: {dorn | girsch | skele | slany}@vexpert.dbai.tuwien.ac.at Abstract Due to complexity reasons of realistic scheduling applications, often itera- tive improvement techniques that perform a kind of local search to improve a given schedule are proposed instead of enumeration techniques that guar- antee optimal solutions. In this paper we describe an experimental com- parison of four iterative improvement techniques for schedule optimization that differ in the local search methodology. Namely, these techniques are iterative deepening, random search, tabu search and genetic algorithms. To compare the performance of these techniques, we use the same evalu- ation 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 satisfac- tions 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 tech- niques reported in the literature, our application is constrained by a greater variety of antagonistic criteria that are partly contradictory. Keywords: Scheduling Theory; Optimization; Tabu Search; Genetic Algorithms * The reported work was partly funded by the Austrian Industries in the framework ot the Christian Doppler Laboratory for Expert Systems