As multi-core computing is now standard, it seems irresponsible for constraints researchers
to ignore the implications of it. Researchers need to address a number of issues to exploit …

Local search metaheuristics (LSMs) are efficient methods for solving hard optimization
problems in science, engineering, economics and technology. By using LSMs, we could …

Optimization tasks are often complex, CPU-time consuming and usually deal with finding the
best (or good enough) solution among alternatives for a given problem. Parallel …

This paper presents an experimental study aimed at assessing the feasibility of parallelizing
constraint propagation—with particular focus on arc-consistency—using Graphical …

Resource provisioning for scientific workflows in Infrastructure-as-a-service (IaaS) clouds is
an important and complicated problem for budget and performance optimizations of …

Discrete optimization is a central problem in artificial intelligence. The optimization of the
aggregated cost of a network of cost functions arises in a variety of problems including …

Local search metaheuristics are a recognized means of solving hard combinatorial
problems. Over the last couple of decades, significant advances have been made in terms of …

Vehicle Routing Problems (VRPs) are fundamental optimization problems of distribution
logistics and transportation systems. In the real-world, VPRs are dynamic in the sense that …

This paper proposes the design and implementation of a dynamic programming based
algorithm for (distributed) constraint optimization, which exploits modern massively parallel …

In this preliminary paper we describe a general approach for multiple learning in conflict-
driven SAT solvers. The proposed formulation of the conflict analysis task turns out to be …