This paper describes a simple heuristic approach to solving large-scale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through the space of possible repairs. The search can be guided by a value-ordering heuristic, the min-conflicts heuristic, that attempts to minimize the number of constraint violations after each step. The heuristic can be used with a variety of different search strategies. We demonstrate empirically that on the n-queens problem, a technique based on this approach performs orders of magnitude better than traditional backtracking techniques. We also describe a scheduling application where the approach has been used successfully. A theoretical analysis is presented both to explain why this method works well on certain types of problems and to predict when it is likely to be most effective.

This paper describes a simple heuristic approach to solving large-scale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through the space of possible repairs. The search can be guided by a value-ordering heuristic, the min-conflicts heuristic, that attempts to minimize the number of constraint violations after each step. The heuristic can be used with a variety of different search strategies. On the n-queens problem, a technique based on this approach performs orders of magnitude better than traditional backtracking techniques. The technique has also been used for scheduling the Hubble Space telescope. A theoretical analysis is presented both to explain why this method works well on certain types of problems and to predict when it is likely to be most effective. 1 Introduction One of the most promising general approaches for solving combinatorial search problems is to generate an initial...

Integer and combinatorial optimization problems constitute a major challenge for algorithmics. They arise when a large number of discrete organizational decisions have to be made, subject to constraints and optimization criteria. This thesis

Partial constraint satisfaction problems (PCSPs) were proposed by Freuder and Wallace to address some of the representational di culties with traditional constraint satisfaction techniques. However, the reasoning method of their proposal was limited to traditional backtracking based algorithms. In this paper, we extend the PCSP model by associating it with a local search algorithm, which has found great successes in solving many large scale problems in the past. Furthermore, we extend the combined model to incorporate abstract problem solving, and show that the extended model has not only the advantages of both PCSP and local search, but also a number of new features useful for scheduling applications. We demonstrate the feasibility of our approach by an application to a university course scheduling domain.