49,737 research outputs found
Global propagation of side constraints for solving over-constrained problems
International audienceThis article deals with the resolution of over-constrained problems using constraint programming, which often imposes to add to the constraint network new side constraints. These side constraints control how the initial constraints of the model should be satisfied or violated, to obtain solutions that have a practical interest. They are specific to each application. In our experiments, we show the superiority of a framework where side constraints are encoded by global constraints on new domain variables, which are directly included into the model. The case-study is a cumulative scheduling problem with over-loads. The objective is to minimize the total amount of over-loads. We augment the Cumulative global constraint of the constraint programming solver Choco with sweep and task interval violation-based algorithms. We provide a theoretical and experimental comparison of the two main approaches for encoding over-constrained problems with side constraints
Solving the Resource Constrained Project Scheduling Problem with Generalized Precedences by Lazy Clause Generation
The technical report presents a generic exact solution approach for
minimizing the project duration of the resource-constrained project scheduling
problem with generalized precedences (Rcpsp/max). The approach uses lazy clause
generation, i.e., a hybrid of finite domain and Boolean satisfiability solving,
in order to apply nogood learning and conflict-driven search on the solution
generation. Our experiments show the benefit of lazy clause generation for
finding an optimal solutions and proving its optimality in comparison to other
state-of-the-art exact and non-exact methods. The method is highly robust: it
matched or bettered the best known results on all of the 2340 instances we
examined except 3, according to the currently available data on the PSPLib. Of
the 631 open instances in this set it closed 573 and improved the bounds of 51
of the remaining 58 instances.Comment: 37 pages, 3 figures, 16 table
Decompositions of Grammar Constraints
A wide range of constraints can be compactly specified using automata or
formal languages. In a sequence of recent papers, we have shown that an
effective means to reason with such specifications is to decompose them into
primitive constraints. We can then, for instance, use state of the art SAT
solvers and profit from their advanced features like fast unit propagation,
clause learning, and conflict-based search heuristics. This approach holds
promise for solving combinatorial problems in scheduling, rostering, and
configuration, as well as problems in more diverse areas like bioinformatics,
software testing and natural language processing. In addition, decomposition
may be an effective method to propagate other global constraints.Comment: Proceedings of the Twenty-Third AAAI Conference on Artificial
Intelligenc
Comparative study on the application of evolutionary optimization techniques to orbit transfer maneuvers
Orbit transfer maneuvers are here considered as benchmark cases for comparing performance of different optimization
techniques in the framework of direct methods. Two different classes of evolutionary algorithms, a
conventional genetic algorithm and an estimation of distribution method, are compared in terms of performance
indices statistically evaluated over a prescribed number of runs. At the same time, two different types of problem
representations are considered, a first one based on orbit propagation and a second one based on the solution of
Lambertâs problem for direct transfers. In this way it is possible to highlight how problem representation affects
the capabilities of the considered numerical approaches
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