Abstract not found

Optimising a train schedule on a single line track is known to be NP-Hard with respect to the number of conflicts in the schedule. This makes it difficult to determine optimum solutions to real life problems in reasonable time and raises the need for good heuristic techniques. The heuristics applied and compared in this paper are a local search heuristic with an improved neighbourhood structure, genetic algorithms, tabu search and two hybrid algorithms. When no time constraints are enforced on solution time, the genetic and hybrid algorithms were within five percent of the optimal solution for at least ninety percent of the test problems.

The basic principles of constraint programming (constraint satisfaction problems, search, constraint propagation) are introduced by discussing how constraint programming can be used to solve a specific optimization problem. A set of orchestral pieces is to be rehearsed and the problem requires finding a sequence that will minimize the time that players are at the rehearsal but not playing, if they arrive for the first piece they are involved in and leave after the last. A constraint programming model of this problem is presented. A similar problem arises in `talent scheduling' in shooting a film; improvements to the basic model are given that allow a larger instance of this kind to be solved.

Abstract not found

This article describes applications of modern heuristic search methods to pattern sequencing problems, i.e., problems seeking for a permutation of the rows of a given matrix with respect to some given objective function. We consider two di#erent objectives: Minimization of the number of simultaneously open stacks and minimization of the average order spread. Both objectives require the adaptive evaluation of changed solutions to allow an e#cient application of neighbourhood search techniques.