Constraint Satisfaction Methods for Applications in Engineering

Abstract

Constraints provide declarative descriptions of important requirements related to engineering projects. Most existing algorithms for constraint satisfaction require input consisting of binary constraints on variables that have discrete values. Such restrictions limit their use in engineering since complex constraints, involving several variables that have discrete and numeric values, are common. This paper provides an approach for decision support through approximating solution spaces that are defined by constraints. Our algorithm is not limited to a specific type of constraint, but handles numeric and discrete variables in the same framework. Since a new type of local consistency narrows down the search space effectively, full-scale engineering tasks, such as designs involving hundreds of variables, are accommodated without excessive computational complexity. The approach is demonstrated for selection of appropriate wind bracing for single story steel-framed buildings. Results may be used for input into other tools containing algorithms such as those offering (i) higher levels of consistency, (ii) optimally directed point-solution search, and (iii) simulation behaviour.Finally, extension to dynamic constraint satisfaction using different combinations of activation conditions is straightforward. It is expected that this approach will improve the performance of many existing and future computeraided engineering tools.

Parametric design method for geospatial data visualization and interactive map modeling

“…Basic method for constraint solving is to reduce large equations into small ones (Bouma, 1995;Fudos, 1995;Cazier, 1996;Aldefeld, 1998;Durand, 1998;Gelle, 2000;Gao, et al, 2004). The algorithm framework is as follows:…”

Case-based reasoning and system design: An integrated approach based on ontology and preference modeling

“…Then a solution can be reused and adapted to the new requirements (simple copy or copy plus modifications). However, in the current version of the prototype, the requirements constraints are unary constraints (they involve only one variable; Gelle et al, 2000). This software prototype has been tested by an industrial partner of the AT-LAS project in order to validate the approach.…”

“…Definition of the fuzzy set of compatible values with regard to a discrete constraint. It is considered in this article that constraints representing requirements are discrete constraints (Montanari, 1974;Gelle et al, 2000).…”

Constraint solving approach based on Differential Evolution Algorithm for geo-visualization variant design method

“…Sketch-based mode is corresponding to the natural process of map design and data visualization. Feature-based mode can realize rapid map modeling by formed feature units and their combination and reuse (Chen, 1995;Yin, 2000) [17,18,19,20]. Variant design method will utilize sketch-based parametric design and featurebased parametric design approaches to realize local model and whole model control.…”