A global constraint for total weighted completion time for cumulative resources

The criterion of total weighted completion time occurs as a sub-problem of combinatorial optimization problems in such diverse areas as scheduling, container loading and storage assignment in warehouses. These applications often necessitate considering a rich set of requirements and preferences, which makes constraint programming (CP) an effective modeling and solving approach. On the other hand, basic CP techniques can be inefficient in solving models that require inference over sum type expressions. In this paper, we address increasing the solution efficiency of constraint-based approaches to cumulative resource scheduling with the above criterion. Extending previous results for unary capacity resources, we define the COMPLETIONm global constraint for propagating the total weighted completion time of activities that require the same cumulative resource. We present empirical results in two different problem domains: scheduling a single cumulative resource, and container loading with constraints on the location of the center of gravity. In both domains, the proposed constraint propagation algorithm out-performs existing propagation techniques

Unified processing of constraints for interactive simulation

International audienceThis paper introduces a generic way of dealing with a set of different constraints (bilateral, unilateral, dry friction) in the context of interactive simulation. We show that all the mentioned constraints can be handled within a unified framework: we define the notion of generalized constraints, which can be derived into most classical constraints types. The solving method is based on an implicit treatment of constraints that provides good stability for interactive applications using deformable models and rigid bodies. Each constraint law is expressed in constraint subspace, making constraint evaluation much easier. A global solution is calculated using an iterative process that takes into account the mechanical coupling between the constraints. Various examples, from basic to more complex, show the practical advantage of using generalized constraints, as a way of creating heterogeneously constrained systems, as well as the scalability of the proposed method

Unified processing of constraints for interactive simulation

International audienceThis paper introduces a generic way of dealing with a set of different constraints (bilateral, unilateral, dry friction) in the context of interactive simulation. We show that all the mentioned constraints can be handled within a unified framework: we define the notion of generalized constraints, which can be derived into most classical constraints types. The solving method is based on an implicit treatment of constraints that provides good stability for interactive applications using deformable models and rigid bodies. Each constraint law is expressed in constraint subspace, making constraint evaluation much easier. A global solution is calculated using an iterative process that takes into account the mechanical coupling between the constraints. Various examples, from basic to more complex, show the practical advantage of using generalized constraints, as a way of creating heterogeneously constrained systems, as well as the scalability of the proposed method

Parallel consistency in constraint programming

Program parallelization becomes increasingly important when new multi-core architectures provide ways to improve performance. One of the greatest challenges of this development lies in programming parallel applications. Using declarative languages, such as constraint programming, can make the transition to parallelism easier by hiding the parallelization details in a framework. Automatic parallelization in constraint programming has previously focused on data parallelism. In this paper, we look at task parallelism, specifically the case of parallel consistency. We have developed two models of parallel consistency, one that shares intermediate results and one that does not. We evaluate which model is better in our experiments. Our results show that parallelizing consistency can provide the programmer with a robust scalability for regular problems with global constraints

Automatic constraint-based synthesis of non-uniform rational B-spline surfaces

In this dissertation a technique for the synthesis of sculptured surface models subject to several constraints based on design and manufacturability requirements is presented. A design environment is specified as a collection of polyhedral models which represent components in the vicinity of the surface to be designed, or regions which the surface should avoid. Non-uniform rational B-splines (NURBS) are used for surface representation, and the control point locations are the design variables. For some problems the NURBS surface knots and/or weights are included as additional design variables. The primary functional constraint is a proximity metric which induces the surface to avoid a tolerance envelope around each component. Other functional constraints include: an area/arc-length constraint to counteract the expansion effect of the proximity constraint, orthogonality and parametric flow constraints (to maintain consistent surface topology and improve machinability of the surface), and local constraints on surface derivatives to exploit part symmetry. In addition, constraints based on surface curvatures may be incorporated to enhance machinability and induce the synthesis of developable surfaces;The surface synthesis problem is formulated as an optimization problem. Traditional optimization techniques such as quasi-Newton, Nelder-Mead simplex and conjugate gradient, yield only locally good surface models. Consequently, simulated annealing (SA), a global optimization technique is implemented. SA successfully synthesizes several highly multimodal surface models where the traditional optimization methods failed. Results indicate that this technique has potential applications as a conceptual design tool supporting concurrent product and process development methods

Constrained optimization in simulation: a novel approach.

This paper presents a novel heuristic for constrained optimization of random computer simulation models, in which one of the simulation outputs is selected as the objective to be minimized while the other outputs need to satisfy prespeci¯ed target values. Besides the simulation outputs, the simulation inputs must meet prespeci¯ed constraints including the constraint that the inputs be integer. The proposed heuristic combines (i) experimental design to specify the simulation input combinations, (ii) Kriging (also called spatial correlation modeling) to analyze the global simulation input/output data that result from this experimental design, and (iii) integer nonlinear programming to estimate the optimal solution from the Kriging metamodels. The heuristic is applied to an (s, S) inventory system and a realistic call-center simulation model, and compared with the popular commercial heuristic OptQuest embedded in the ARENA versions 11 and 12. These two applications show that the novel heuristic outperforms OptQuest in terms of search speed (it moves faster towards high-quality solutions) and consistency of the solution quality.

Constrained Optimization in Simulation: A Novel Approach

This paper presents a novel heuristic for constrained optimization of random computer simulation models, in which one of the simulation outputs is selected as the objective to be minimized while the other outputs need to satisfy prespeci¯ed target values. Besides the simulation outputs, the simulation inputs must meet prespeci¯ed constraints including the constraint that the inputs be integer. The proposed heuristic combines (i) experimental design to specify the simulation input combinations, (ii) Kriging (also called spatial correlation mod- eling) to analyze the global simulation input/output data that result from this experimental design, and (iii) integer nonlinear programming to estimate the optimal solution from the Krig- ing metamodels. The heuristic is applied to an (s, S) inventory system and a realistic call-center simulation model, and compared with the popular commercial heuristic OptQuest embedded in the ARENA versions 11 and 12. These two applications show that the novel heuristic outper- forms OptQuest in terms of search speed (it moves faster towards high-quality solutions) and consistency of the solution quality.