Article

Free access

Solving stochastic optimization problems with stochastic constraints: an application in network design

Authors:

Gü Gürkan,

A. Yonca Özge,

Stephen M. RobinsonAuthors Info & Claims

WSC '99: Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1

Pages 471 - 478

https://doi.org/10.1145/324138.324297

Published: 01 December 1999 Publication History

PDF eReader

References

[1]

Ahuja, K.K., Magnati, T.L., and Orlin, J.B. 1993. Network flows: theory, algorithms, and applications (Prentice Hall, Englewood Cliffs, NJ).

Digital Library

Google Scholar

[2]

Ferris, M. C. and Pang, J. S. 1997. Engineering and economic applications of complementarity problems. SIAM Review 39(4): 669-713.

Digital Library

Google Scholar

[3]

Glasserman, P. 1991. Gradient estimation via perturbation analysis (Kluwer, Norwell, MA).

Google Scholar

[4]

Gomory, R.E. and Hu, T.C. 1964. Synthesis of a commu-nication network. SIAM Journal 12(2): 348-369.

Google Scholar

[5]

Grigoriadis, M.D. and Hsu, T. 1979. The Rutgers minimum cost network flow subroutines. SIGMAP Bulletin of the ACM 26: 17-18.

Google Scholar

[6]

G~ urkan, G. and Karaesmen F. 1998. Computation of optimal flow control policies of a manufacturing system with multiple production rates. Working paper.

Google Scholar

[7]

G~ urkan, G., ~ Ozge, A. Y. and Robinson, S. M. 1996. Sample-path solution of stochastic variational inequalities, with applications to option pricing. In Proceedings of the 1996 Winter Simulation Conference, ed. J. M. Charnes, D. M. Morrice, D. T. Brunner and J. J. Swain, 337-344.

Digital Library

Google Scholar

[8]

G~ urkan, G., ~ Ozge, A. Y. and Robinson, S. M. 1998. Sample-path solutions for simulation optimization problems and stochastic variational inequalities. In: Interfaces in Computer Science and Operations Research, ed. D. L. Woodruff (Kluwer, Boston), 169-188.

Digital Library

Google Scholar

[9]

G~ urkan, G., ~ Ozge, A. Y. and Robinson, S. M. 1999. Sample-path solution of stochastic variational inequal-ities. Mathematical Programming 84: 313-333.

Crossref

Google Scholar

[10]

Harker, P. T. and Pang, J.-S. 1990. Finite-dimensional variational inequality and nonlinear complementarity problems: A survey of theory, algorithms and applica-tions. Mathematical Programming 48: 161-220.

Digital Library

Google Scholar

[11]

Ho, Y.-C. and Cao, X.-R. 1991. Perturbation analysis of discrete event dynamical systems (Kluwer, Norwell, MA).

Google Scholar

[12]

Kall, P. 1986. Approximation to optimization problems: an elementary review. Mathematics of Operations Re-search 11: 9-18.

Digital Library

Google Scholar

[13]

~ Ozge, A. Y. 1997. Sample-path solution of stochastic vari-ational inequalities and simulation optimization prob-lems. Ph.D. Dissertation, Department of Industrial En-gineering, University of Wisconsin-Madison, Madison, Wisconsin, USA.

Google Scholar

[14]

Plambeck, E. L., B.-R. Fu, S. M. Robinson, and R. Suri. 1993. Throughput optimization in tandem production lines via nonsmooth programming. In Proceedings of 1993 Summer Computer Simulation Conference, ed. J. Schoen, 70-75. San Diego, CA: Society for Computer Simulation.

Google Scholar

[15]

Plambeck, E. L., B.-R. Fu, S. M. Robinson, and R. Suri. 1996. Sample-path optimization of convex stochastic performance functions. Mathematical Programming 75: 137-176.

Digital Library

Google Scholar

[16]

Robinson, S. M. 1980. Strongly regular generalized equa-tions. Mathematics of Operations Research 5: 43-62.

Digital Library

Google Scholar

[17]

Robinson, S. M. 1996. Analysis of sample-path optimiza-tion. Mathematics of Operations Research 21: 513- 528.

Digital Library

Google Scholar

[18]

Rockafellar, R. T. and Wets, R. J-B. 1998. Variational analysis (Springer-Verlag, Berlin).

Google Scholar

[19]

Rubinstein, R. Y. and Shapiro, A. 1993. Discrete event systems: sensitivity analysis and stochastic optimization by the score function method (Wiley, Chichester and New York).

Google Scholar

[20]

Schramm, H., and Zowe, J. 1990. A version of the bundle idea for minimizing a nonsmooth function: Conceptual idea, convergence analysis, numerical results. Techni-cal Report, 209. Mathematisches Institut, Universit~ at Bayreuth, Bayreuth, Germany.

Google Scholar

[21]

Shapiro, A. 1993. Asymptotic behavior of optimal solutions in stochastic programming. Mathematics of Operations Research 18: 829-845.

Digital Library

Google Scholar

Cited By

View all

Hu LAndradottir S(2020)Constrained Search via Penalization for Continuous Simulation OptimizationIEEE Transactions on Automatic Control10.1109/TAC.2020.296854865:11(4741-4752)Online publication date: Nov-2020
https://doi.org/10.1109/TAC.2020.2968548
Chagwiza GJones BHove-Musekwa SMtisi S(2015)A generalised likelihood uncertainty estimation mixed-integer programming model: Application to a water resource distribution networkCogent Mathematics10.1080/23311835.2015.10480762:1(1048076)Online publication date: 3-Jun-2015
https://doi.org/10.1080/23311835.2015.1048076
Falsaperla PRaciti FScrimali L(2011)A variational inequality model of the spatial price network problem with uncertain dataOptimization and Engineering10.1007/s11081-011-9158-y13:3(417-434)Online publication date: 1-Jul-2011
https://doi.org/10.1007/s11081-011-9158-y
Show More Cited By

Index Terms

Recommendations

Stochastic Dynamic Optimization with Discounted Stochastic Dominance Constraints

We introduce a stochastic dynamic optimization problem, where risk aversion is expressed by a stochastic ordering constraint. The constraint requires that a random reward sequence depending on our decisions dominates a given benchmark random sequence. ...
Stochastic Optimization Methods: Applications in Engineering and Operations Research
Pathwise Stochastic Control Problems and Stochastic HJB Equations

In this paper we study a class of pathwise stochastic control problems in which the optimality is allowed to depend on the paths of exogenous noise (or information). Such a phenomenon can be illustrated by considering a particular investor who wants to ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

WSC '99: Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1

December 1999

925 pages

ISBN:0780357809

DOI:10.1145/324138

Editors:
Phillip A. Farrington
Univ. of Alabama in Huntsville, Huntsville
,
Harriet Black Nembhard
Univ. of Wisconsin—Madison, Madison
,
David T. Sturrock
Systems Modeling, Sewickley, PA
,
Gerald W. Evans
Univ. of Louisville, Louisville, KY

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 1999

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

WSC99

Sponsor:

WSC99: Winter Simulation Conference 1999

December 5 - 8, 1999

Arizona, Phoenix, USA

Acceptance Rates

WSC '99 Paper Acceptance Rate 139 of 206 submissions, 67%;

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
497
Total Downloads

Downloads (Last 12 months)61
Downloads (Last 6 weeks)16

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Hu LAndradottir S(2020)Constrained Search via Penalization for Continuous Simulation OptimizationIEEE Transactions on Automatic Control10.1109/TAC.2020.296854865:11(4741-4752)Online publication date: Nov-2020
https://doi.org/10.1109/TAC.2020.2968548
Chagwiza GJones BHove-Musekwa SMtisi S(2015)A generalised likelihood uncertainty estimation mixed-integer programming model: Application to a water resource distribution networkCogent Mathematics10.1080/23311835.2015.10480762:1(1048076)Online publication date: 3-Jun-2015
https://doi.org/10.1080/23311835.2015.1048076
Falsaperla PRaciti FScrimali L(2011)A variational inequality model of the spatial price network problem with uncertain dataOptimization and Engineering10.1007/s11081-011-9158-y13:3(417-434)Online publication date: 1-Jul-2011
https://doi.org/10.1007/s11081-011-9158-y
Deng GFerris M(2009)Variable-Number Sample-Path OptimizationMathematical Programming: Series A and B10.5555/3113600.3113770117:1-2(81-109)Online publication date: 1-Mar-2009
https://dl.acm.org/doi/10.5555/3113600.3113770
Gürkan GKaraesmen FÖzdemir Ö(2007)Optimal Threshold Levels in Stochastic Fluid Models via Simulation-based OptimizationDiscrete Event Dynamic Systems10.1007/s10626-006-0002-z17:1(53-97)Online publication date: 1-Mar-2007
https://dl.acm.org/doi/10.1007/s10626-006-0002-z
Deng GFerris M(2007)Variable-Number Sample-Path OptimizationMathematical Programming10.1007/s10107-007-0164-y117:1-2(81-109)Online publication date: 18-Jul-2007
https://doi.org/10.1007/s10107-007-0164-y
Birbil ŞGürkan GListeş O(2006)Solving Stochastic Mathematical Programs with Complementarity Constraints Using SimulationMathematics of Operations Research10.1287/moor.1060.021531:4(739-760)Online publication date: 1-Nov-2006
https://dl.acm.org/doi/10.1287/moor.1060.0215
Bastin FCirillo CToint P(2006)Convergence theory for nonconvex stochastic programming with an application to mixed logitMathematical Programming: Series A and B10.1007/s10107-006-0708-6108:2(207-234)Online publication date: 1-Sep-2006
https://dl.acm.org/doi/10.1007/s10107-006-0708-6
Birbil ŞGürkan GListeş O(2004)Solving stochastic mathematical programs with complementarity constraints using simulationProceedings of the 36th conference on Winter simulation10.5555/1161734.1161838(550-558)Online publication date: 5-Dec-2004
https://dl.acm.org/doi/10.5555/1161734.1161838
Birbil SGurkan GListes O(2004)Solving Stochastic Mathematical Programs with Complementarity Constraints Using SimulationProceedings of the 2004 Winter Simulation Conference, 2004.10.1109/WSC.2004.1371361(538-546)Online publication date: 2004
https://doi.org/10.1109/WSC.2004.1371361

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Stochastic Dynamic Optimization with Discounted Stochastic Dominance Constraints

Stochastic Optimization Methods: Applications in Engineering and Operations Research

Pathwise Stochastic Control Problems and Stochastic HJB Equations