Applications of Declarative Programming and Knowledge by Ulrich Geske, Hans-Joachim Goltz (auth.), Dietmar Seipel,

By Ulrich Geske, Hans-Joachim Goltz (auth.), Dietmar Seipel, Michael Hanus, Armin Wolf (eds.)

This quantity constitutes the completely refereed post-conference complaints of the seventeenth overseas convention on purposes of Declarative Programming and data administration, INAP 2007, and the twenty first Workshop on good judgment Programming, WLP 2007, held in Würzburg, Germany, in the course of October 4-6, 2007.

The sixteen completely revised complete papers provided including 1 invited paper have been rigorously reviewed and chosen from a number of submissions. the themes coated are constraints; databases and information mining; extensions of good judgment programming; and process demonstrations.

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Additional resources for Applications of Declarative Programming and Knowledge Management: 17th International Conference, INAP 2007, and 21st Workshop on Logic Programming, WLP 2007, Würzburg, Germany, October 4-6, 2007, Revised Selected Papers

Example text

In this paper, we invented new pruning rules for weighted sums where the summands are the weighted start times of tasks to be serialized. Then, the presented theoretical results are adopted for their practical application in constraint programming. Further, their adequacy was practically shown in a real-world application domain: the optimal sequencing of surgeries in hospitals. There, in contrast to the original rules the new pruning rules behave in very robust way, especially in real scheduling problems.

Solution computation of all solutions n time/msec. # choices # backtracks time/msec. # combinations n! 8 11 8 0 31 40320 9 15 9 0 313 362880 10 15 10 0 2648 3628800 11 17 11 0 27737 39916800 12 17 12 0 279418 479001600 Fig. 7. )) are necessary if we use WeightedSum either together with SingleResource or any other, even specialized constraint for scheduling equal length tasks8 (cf. [1]). However, WeightedTaskSum requires the least number of choices to find an optimal solution: n for the finding and proving the optimality where n is the number of surgeries.

Furthermore, each task ti ∈ T has a fixed positive priority αi ∈ Q. , a machine, an employee, etc. or especially an operating room). , for setting-up the used machine. e. some start times s1 ∈ S1 , . . e. it minimizes the sum i=1 αi · si such that for any other n solution s1 ∈ S1 , . . , sn ∈ Sn it holds: i=1 αi · si ≤ ni=1 αi · si . In the following, we call this problem, which is characterized by a task set T , the Prioritized Task Scheduling Problem of T , or PTSP(T ) for short. Furthermore, we call a PTSP(T ) solvable if a (minimal) solution exists.

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