A reusable iterative optimization software library to solve combinatorial problems with approximate reasoning

International Journal of Approximate Reasoning

Volumn 19, Issue 1-2, 1998, Pages 161-191

  Raggl, Andreas ^a   Slany, Wolfgang ^b  

^a ERSTE GROUP BANK AG   (Austria)

^b VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Combinatorial optimization; Iterative improvement; Knowledge acquisition; Knowledge base consistency; Multiple criteria decision making; Scheduling under uncertainty; Shift scheduling; Steel making

Indexed keywords

EID: 0013449535     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0888-613X(98)10002-6     Document Type: Article

References (21)

1. B. Abramson, M.M. Yung, Construction through decomposition: A linear time algorithm for the n-queens problem, Technical Report CUCS-129-84, Department of Computer Science. Columbia University, 1984; Divide and conquer under global constraints: A solution to the N-queens problem, Journal of Parallel and Distributed Computing 6 (1989) 649-662.
2. B. Abramson, M.M. Yung, Construction through decomposition: A linear time algorithm for the n-queens problem, Technical Report CUCS-129-84, Department of Computer Science. Columbia University, 1984; Divide and conquer under global constraints: A solution to the N-queens problem, Journal of Parallel and Distributed Computing 6 (1989) 649-662.
3. M. Bonner, S. Mayer, A. Raggl, W. Slany, FLIP++: A fuzzy logic inference processor library. in: Workshop Notes of the IJCAI'95 Workshop on Fuzzy Logic in Artificial Intelligence. 1995, Montreal, Canada, pp. 83-92.
4. G. Brewka, H.W. Guesgen, J. Hertzberg, Constraint relaxation and nonmonotonic reasoning, Technical Report TR-92-002, International Computer Science Institute (ICSI), Berkeley, CA, 1992.
5. R. Dechter, A. Dechter, Belief maintenance in dynamic constraint networks, in: Proceedings of AAAI-88 Seventh National Conference on Artificial Intelligence, St. Paul, Minnesota, USA. American Association of Artificial Intelligence, 1988, pp. 37-42.
6. J. Dorn, M. Girsch, G. Skele, W. Slany, Comparison of iterative improvement techniques for schedule optimization, European Journal of Operational Research 94 (1996) 349-361.
7. J. Dorn, R. Shams, An expert system for scheduling in a steel-making plant, in: Proceedings of the First World Congress on Expert Systems, Orlando, FL, Pergamon Press, Oxford, 1991, pp. 395-404.
8. E.C. Freuder, R.J. Wallace, Partial constraint satisfaction. Artificial Intelligence 58 (1992) 21-70.
9. J. Gärtner, S. Wahl, Computer-aided design of shift schedules - Running in a Foggy Swamp, Technical Report, Vienna University of Technology, 1995.
10. W. Hendrysiak, A. Raggl, W. Slany, DomFLIP++, in: D. Ruan, P. D'hondt, P. Govaerts, E.E. Kerre (Eds.), Intelligent Systems and Soft Computing for Nuclear Science and Industry, Proceedings of the Second International FLINS Workshop 1996, Belgium, World Scientific, Singapore, pp. 123-129.
11. S.M. Huard, E.C. Freuder, A debugging assistant for incompletely specified constraint network knowledge bases, International Journal of Expert Systems: Research and Applications 6 (4) (1993) 419-446.
12. G.M. Kuper, Aggregation in constraint databases ECRC, Technical Report ECRC 93-13, 1993.
13. E.L. Lawler, J.K.Lenstra, A.H.G. Rinnooy Kan, D.B. Shmoys, Sequencing and scheduling: Algorithms and complexity logistics of production and inventory, in: S.C. Graves, A.H.G. Rinnooy Kan, P.H. Zipkin (Eds.), Handbooks in OR and MS. vol. 4, North-Holland, Amsterdam, 1993, ch. 9, pp. 445-522.
14. S. McIlraith, R. Reiter, On tests for hypothetical reasoning, in: W. Hamscher, L. Console, J. de Kleer (Eds.), Readings in Model-Based Diagnosis, Morgan Kaufmann, Los Altos, CA, 1992, pp. 89-96.
15. S. Minton, M.D. Johnston, A.B. Philips, P. Laird, Minimizing conflicts: A heuristic repair method for constraint satisfaction and scheduling problems, Artificial Intelligence 58 (1992) 161-205.
16. C. Reichel, W. Slany (Eds.), StarFLIP++ Version 1.0: A reusable iterative optimization library for combinatorial problems with fuzzy constraints: Reference manual, Number DBAI-TR-97-11, Institute of Information Systems (E 184-2), Vienna University of Technology, 1997, http:// www.dbai.tuwien.ac.at/proj/StarFLIP/papers/starflip.ps.
17. C. Reichel, W. Slany (Eds.), StarFLIP++ Version 1.0 (CD-Rom Version): A reusable iterative optimization library for combinatorial problems with fuzzy constraints. Institute of Information Systems (E184-2), Vienna University of Technology, 1997. http://www.dbai.tuwien.ac.at/ proj/StarFLIP/.
18. W. Slany, Scheduling as a fuzzy multiple criteria optimization problem. Fuzzy Sets and Systems 78 (1996) 197-222.
19. K. Stohl, W. Snopek, T. Weigert, T. Moritz, Development of a scheduling expert system for a steel plant, in: Proceedings of the IFAC Workshop on Experts Systems in Mineral and Metal Processing, Espoo, Finland, Pergamon Press, Oxford, 1991.
20. H.S. Stone, J.M. Stone, Efficient search techniques - An empirical study of the n-queens problem, IBM Journal of Research and Development 31 (1987) 464-474.
21. M. Zweben, M. Fox (Eds.), Intelligent Scheduling, Morgan Kaufmann, San Francisco, CA, 1994.