Approximation algorithms for 2-stage stochastic scheduling problems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4513 LNCS, Issue , 2007, Pages 145-157

  Shmoys, David B ^a   Sozio, Mauro ^b  

^a Cornell University   (United States)

^b SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

POLYNOMIALS; PROBLEM SOLVING; RANDOM PROCESSES; SCHEDULING;

POLYNOMIAL SCENARIO MODEL; PROBABILISTIC COMPONENTS; STOCHASTIC GENERALIZATIONS; STOCHASTIC OPTIMIZATION PROBLEM;

APPROXIMATION ALGORITHMS;

EID: 38049016852     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-72792-7_12     Document Type: Conference Paper

References (11)

1. A. Bar-Noy, R. Bar-Yehuda, A. Freund, J. Naor, and B. Schieber. A unified approach to approximating resource allocation and scheduling. Journal of the ACM, 48:1069-1090, 2001.
2. M. Charikar, C. Chekuri, and M. Pal. Sampling bounds for stochastic optimization. In Proceedings of APPROX-RANDOM 2005, pages 257-269, 2005.
3. S. Dye, L. Stougie, and A. Tomasgard. The stochastic single resource service-provision problem. Naval Research Logistics, 50:869-887, 2003.
4. R. L. Graham, E. L. Lawler, J. K. Lenstra, and A. H. G. Rinnooy Kan. Optimization and approximation in deterministic sequencing and scheduling: A survey. Ann. Discrete Math., 5:287-326, 1979.
5. A. Gupta, M. Pál, R. Ravi, and A. Sinha. Boosted sampling: approximation algorithms for stochastic optimization. In Proceedings of the 36th Annual ACM Symposium on Theory of Computing, pages 265-274, 2004.
6. 1-ε. Acta Mathematica, 182:105-142, 1999.
7. N. Immorlica, D. Karger, M. Minkoff, and V. S. Mirrokni. On the costs and benefits of procrastination: approximation algorithms for stochastic combinatorial optimization problems. In Proceedings of the 16th ACM-SIAM Symposium on Discrete Algorithms, pages 691-700, 2004.
8. N. Kong and A. J. Schaefer. A factor 1/2 approximation algorithm for two-stage stochastic matching problems. European Journal of Operational Research, 172:740-746, 2006.
9. R. Ravi and A. Sinha. Hedging uncertainty: Approximation algorithms for stochastic optimization problems. In D. Bienstock and G. Nemhauser, editors, Integer Programming and Combinatorial Optimization: 10th International IPCO Conference, number 3064 in Lecture Notes in Computer Science, pages 101-115. Springer-Verlag, 2004.
10. D. B. Shmoys and C. Swamy. Stochastic optimization is (almost) as easy as deterministic optimization. In Proceedings of the 45th Annual Symposium on Foundations of Computer Science, pages 228-237, 2004.
11. C. Swamy and D. B. Shmoys. The sampling-based approximation algorithms for multi-stage stochastic optimization. In Proceedings of the 46th Annual Symposium on Foundations of Computer Science, pages 357-366, 2005.