Genetic Operators Based on Constraint Repair

In this paper we describe an approach to solve a scheduling problem in the steel making industry with a combination of a constraint repair approach and genetic algorithms. Because several, sometimes conflicting objectives exist in the production of steel, we have proposed a representation of constraint violations and their importance by fuzzy sets (Dorn and Slany 1994). A weighted aggregation of the violations gives us a means to compare schedules. Furthermore, as pointed out by Minton et al. (1990) the strategy to repair constraints in order to achieve better schedules is a good heuristic for large applications. We have therefore developed domain independent genetic operators that apply knowledge of constraint violations. We report on experiments that show the improvement of the combination for our application and draw some conclusions

DÉJÀ VU -- A Reusable Framework for the Construction of Intelligent Interactive Schedulers

We describe the basic techniques underlying the DÉJÀ VU Scheduling Class Library to achieve a library of reusable and extendible classes for the construction of interactive production scheduling systems. The constructed systems shall be efficient and user centered which means that the user shall have full control over the schedule construction process. Mixed-initiative scheduling shall be possible. We present how scheduling objects and constraints on these objects are realized. Further we describe the potential user interactions with the system and show prototypical examples from the graphical user interface. A first scheduling system was developed with this class library for the steel plant of Böhler in Kapfenberg. We demonstrate which extensions we made for this system

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. Namely, 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

Workshop Organization

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