Finding reliable solutions: Event-driven probabilistic constraint programming

Annals of Operations Research

Volumn 171, Issue 1, 2009, Pages 77-99

  Tarim, S Armagan ^a   Hnich, Brahim ^b   Prestwich, Steven ^c   Rossi, Roberto ^c,d  

^a HACETTEPE UNIVERSITY   (Turkey)

^b IZMIR UNIVERSITY OF ECONOMICS   (Turkey)

^c UNIVERSITY COLLEGE CORK   (Ireland)

^d Centre for Telecommunication Value Chain Research Dublin   (Ireland)

Author keywords

Constraint programming; Event driven; Probabilistic; Uncertainty

Indexed keywords

EID: 70350302454     PISSN: 02545330     EISSN: 15729338     Source Type: Journal    
DOI: 10.1007/s10479-008-0382-6     Document Type: Article

References (28)

1. Beck, J. C., & Wilson, N. (2007). Proactive algorithms for job shop scheduling with probabilistic durations. Journal of Artificial Intelligence Research, 28, 183-232.
2. Bidot, J. (2005). A general framework integrating techniques for scheduling under uncertainty. Ph.D. thesis, Ecole Nationale d'Ingèieurs de Tarbes.
3. Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. New York: Springer.
4. Bistarelli, S., Montanari, U., & Rossi, F. (2002). Soft constraint logic programming and generalized shortest path problems. Journal of Heuristics, 8(1), 25-41.
5. Charnes, A., & Cooper, W. W. (1959). Chance-constrainted programming. Management Science, 6(1), 73-79.
6. Davenport, A., & Beck, J. C. (2000). A survey of techiniques for scheduling with uncertainty (Technical Report). Available at: http://www.tidel.mie.utoronto.ca/publications.php.
7. de Kok, A. G., & Graves, S. C. (2003). Handbooks in operations research and management science: supply chain management: design, coordination and operation (Vol. 11). Amsterdam: Elsevier.
8. Fargier, H., Martin-Clouaire, R., Lang, J., & Schiex, T. (1995). A constraint satisfaction framework for decision under uncertainty. In Proceedings of the eleventh international conference on uncertainty in artificial intelligence, Montreal, Canada.
9. Freuder, E. C., & Richard, J. W. (1992). Partial constraint satisfaction. Artificial Intelligence.
10. Herroelen, W., & Leus, R. (2005). Project scheduling under uncertainty: Survey and research potentials. European Journal of Operational Research, 165, 289-306.
11. Hooker, J. N., Ottosson, G., Thorsteinsson, E. S., & Kim, H. J. (1999). On integrating constraint propagation and linear programming for combinatorial optimization. In Proceedings of the sixteenth national conference on artificial intelligence (pp. 136-141). Menlo Park/Cambridge: AAAI Press/MIT Press.
12. Jain, V., & Grossmann, I. E. (2001). Algorithms for hybrid MILP/CP models for a class of optimization problems. INFORMS Journal on Computing, 13, 258-276.
13. Jeffreys, H. (1961). Theory of probability. Oxford: Clarendon.
14. Kall, P., & Wallace, S. W. (1994). Stochastic programming. New York: Wiley.
15. Kingsman, B. G. (1985). Raw materials purchasing: an operational research approach. Elmsford: Pergamon.
16. Liu, B. (1995a). Dependent-chance goal programming: a class of stochastic programming (Technical Report). Institute of System Science, Chinese Academy of Sciences.
17. Liu, B. (1995b). Dependent-chance goal programming and its genetic algorithm approach (Technical Report). Institute of System Science, Chinese Academy of Sciences.
18. Liu, B. (1997). Dependent-chance programming: A class of stochastic optimization. Computers & Mathematics with Applications, 34, 89-104.
19. Liu, B. (1999). Uncertain programming. New York: Wiley.
20. Liu, B., & Iwamura, K. (1997). Modelling stochastic decision systems using dependent-chance programming. European Journal of Operational Research, 101, 193-203.
21. Liu, B., & Ku, C. (1993). Dependent-chance goal programming and an application. Journal of Systems Engineering & Electronics, 4, 40-47.
22. McKay, M. D., Beckman, R. J., & Conover, W. J. (1979). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21, 239-245.
23. Porteus, E. L. (2002). Foundations of stochastic inventory theory. Stanford: Stanford University Press.
24. Sengupta, J. K. (1972). Stochastic programming: methods and applications. Amsterdam: North-Holland.
25. Tarim, S. A., Manandhar, S., & Walsh, T. (2003). Scenario-based stochastic constraint programming. In Proceedings of the eighteenth international joint conference on artificial intelligence, Acapulco, Mexico (pp. 257-262).
26. Tarim, S. A., Manandhar, S., & Walsh, T. (2006). Stochastic constraint programming: A scenario-based approach. Constraints, 11, 53-80.
27. Vajda, S. (1972). Probabilistic programming. San Diego: Academic Press.
28. Wu, Y., Zhou, J., & Yang, J. (2005). Dependent-chance programming model for stochastic network bottleneck capacity expansion based on neural network and genetic algorithm. In Lecture notes in computer science: Vol. 3612. Advances in Natural Computation (pp. 120-128). Berlin: Springer.