Generic CSP techniques for the job-shop problem
From an Al 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 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.
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- 1.Crawford J. and Baker A.: Experimental Results on the Application of Satisfiability Algorithms to Scheduling Problems, Proc. of 12th National Conference on Artificial Intelligence, (1994) 1092–1097.Google Scholar
- 2.Harvey W.: Nonsystematic Backtracking Search, PhD thesis, Stanford University, (1995).Google Scholar
- 3.Harvey W. and Ginsberg M.: Limited Discrepancy Search, Proc. of 14th Int. Joint Conference on Artificial Intelligence, (1995) 607–613.Google Scholar
- 4.Haralick R. and Elliot G.: Increasing tree search efficiency for constraint satisfaction problems, Artificial Intelligence, 14, (1980) 263–313.Google Scholar
- 5.Korf R.: Improved Limited Discrepancy Search, Proc. of 13th National Conference on Artificial Intelligence, (1996) 286–291.Google Scholar
- 6.Larrosa J. and Meseguer P.: Optimization-based Heuristics for Maximal Constraint Satisfaction, Proc. of 1st Int. Conference on Principles and Practice of Constraint Processing, (1995) 103–120.Google Scholar
- 7.Meseguer P.: Interleaved Depth-First Search, Proc. of 15th Int. Joint Conference on Artificial Intelligence, (1997) 1382–1387.Google Scholar
- 8.Meseguer P. and Larrosa J.: Constraint Satisfaction as Global Optimization, Proc. of 14th Int. Joint Conference on Artificial Intelligence, (1995) 579–584.Google Scholar
- 9.Muscettola, N.: On the Utility of Bottleneck Reasoning for Scheduling, Proc. of 12th National Conference on Artificial Intelligence, (1994) 1105–1110.Google Scholar
- 10.Sadeh N., Sycara K., and Xiong Y.: Backtracking techniques for the job shop scheduling constraint satisfaction problem, Artificial Intelligence, 76, (1995) 455–480Google Scholar
- 11.Sadeh N. and Fox M.: Variable and value ordering for the job shop constraint satisfaction problem, Artificial Intelligence, 86, (1996) 1–41.Google Scholar
- 12.Smith S. and Cheng C.: Slack-Based Heuristics for onstraint Satisfaction Scheduling, Proc. of 11th National Conference on Artificial Intelligence, (1993) 139–144.Google Scholar
- 13.Walsh T.: Depth-bounded Discrepancy Search, Proc. of 15th Int. Joint Conference on Artificial Intelligence, (1997) 1388–1393.Google Scholar