A geometric constraint over k-dimensional objects and shapes subject to business rules

This report presents a global constraint that enforces rules written in a language based on arithmetic and first-order logic to hold among a set of objects. In a first step, the rules are rewritten to Quantifier-Free Presburger Arithmetic (QFPA) formulas. Secondly, such formulas are compiled to generators of k-dimensional forbidden sets. Such generators are a generalization of the indexicals of cc(FD). Finally, the forbidden sets generated by such indexicals are aggregated by a sweep-based algorithm and used for filtering. The business rules allow to express a great variety of packing and placement constraints, while admitting efficient and effective filtering of the domain variables of the k-dimensional object, without the need to use spatial data structures. The constraint was used to directly encode the packing knowledge of a major car manufacturer and tested on a set of real packing problems under these rules, as well as on a packing-unpacking problem

A discrete inhomogeneous model for the yeast cell cycle

We study the robustness and stability of the yeast cell regulatory network by using a general inhomogeneous discrete model. We find that inhomogeneity, on average, enhances the stability of the biggest attractor of the dynamics and that the large size of the basin of attraction is robust against changes in the parameters of inhomogeneity. We find that the most frequent orbit, which represents the cell-cycle pathway, has a better biological meaning than the one exhibited by the homogeneous model.Comment: 5 pages, 1 figur

Inference of Well-Typings for Logic Programs with Application to Termination Analysis

This paper develops a method to infer a polymorphic well-typing for a logic program. One of the main motivations is to contribute to a better automation of termination analysis in logic programs, by deriving types from which norms can automatically be constructed. Previous work on type-based termination analysis used either types declared by the user, or automatically generated monomorphic types describing the success set of predicates. Declared types are typically more precise and result in stronger termination conditions than those obtained with inferred types. Our type inference procedure involves solving set constraints generated from the program and derives a well-typing in contrast to a success-set approximation. Experiments show that our automatically inferred well-typings are close to the declared types and thus result in termination conditions that are as good as those obtained with declared types for all our experiments to date. We describe the method, its implementation and experiments with termination analysis based on the inferred types

Hull Consistency Under Monotonicity

International audienceWe prove that hull consistency for a system of equations or inequalities can be achieved in polynomial time providing that the underlying functions are monotone with respect to each variable. This result holds including when variables have multiple occurrences in the expressions of the functions, which is usually a pitfall for interval-based contractors. For a given constraint, an optimal contractor can thus be enforced quickly under monotonicity and the practical significance of this theoretical result is illustrated on a simple example

An Hybrid, Qos-Aware Discovery of Semantic Web Services Using Constraint Programming

Most Semantic Web Services discovery approaches are not well suited when using complex relational, arithmetic and logical expressions, because they are usually based on Description Logics. Moreover, these kind of expressions usually appear when discovery is performed including Quality-of-Service conditions. In this work, we present an hybrid discovery process for Semantic Web Services that takes care of QoS conditions. Our approach splits discovery into stages, using different engines in each one, depending on its search nature. This architecture is extensible and loosely coupled, allowing the addition of discovery engines at will. In order to perform QoS-aware discovery, we propose a stage that uses Constraint Programming, that allows to use complex QoS conditions within discovery queries. Furthermore, it is possible to obtain the optimal offer that fulfills a given demand using this approach.Comisión Interministerial de Ciencia y Tecnología TIN2006-0047

An improved constraint satisfaction adaptive neural network for job-shop scheduling

Copyright @ Springer Science + Business Media, LLC 2009This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and in part by the National Nature Science Fundation of China under Grant 60821063 and National Basic Research Program of China under Grant 2009CB320601

Strategic directions in constraint programming

An abstract is not available