Generic CSP techniques for the job-shop problem

Abstract

The job-shop is a classical problem in manufacturing, arising daily in factories and workshops. From an AI perspective, the job-shop is a constraint satisfaction problem (CSP), and many specific techniques have been developed to solve it efficiently. In this context, one may believe that generic search and CSP methods (which typically are better understood and easier to develop, codify and maintain than specific approaches) are not appropriated for this problem. In this paper, we contradict this belief. We show that generic search and CSP algorithms and heuristics can be successfully applied to job-shop problem instances that have been considered challenging by the job-shop community. In particular, we use forward checking with support-based heuristics, a combination of a generic CSP algorithm with generic heuristics. We improve this combination replacing the depth-first search strategy of forward checking by a discrepancy-based schema, a generic search strategy recently developed. Our approach obtains similar results to specific approaches in terms of the number of solved problems, with reasonable requirements in computational resources.