IDEAS home Printed from https://ideas.repec.org/r/spr/annopr/v142y2006i1p215-24110.1007-s10479-006-6169-8.html
   My bibliography  Save this item

The empirical behavior of sampling methods for stochastic programming

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Xin Chen & Jiawei Zhang, 2009. "A Stochastic Programming Duality Approach to Inventory Centralization Games," Operations Research, INFORMS, vol. 57(4), pages 840-851, August.
  2. Blanchot, Xavier & Clautiaux, François & Detienne, Boris & Froger, Aurélien & Ruiz, Manuel, 2023. "The Benders by batch algorithm: Design and stabilization of an enhanced algorithm to solve multicut Benders reformulation of two-stage stochastic programs," European Journal of Operational Research, Elsevier, vol. 309(1), pages 202-216.
  3. Chunming Tang & Bo He & Zhenzhen Wang, 2020. "Modified Accelerated Bundle-Level Methods and Their Application in Two-Stage Stochastic Programming," Mathematics, MDPI, vol. 8(2), pages 1-26, February.
  4. Unai Aldasoro & Laureano Escudero & María Merino & Juan Monge & Gloria Pérez, 2015. "On parallelization of a stochastic dynamic programming algorithm for solving large-scale mixed 0–1 problems under uncertainty," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 703-742, October.
  5. Aydin, Nezir & Murat, Alper, 2013. "A swarm intelligence based sample average approximation algorithm for the capacitated reliable facility location problem," International Journal of Production Economics, Elsevier, vol. 145(1), pages 173-183.
  6. Chen-Ritzo, Ching-Hua & Ervolina, Tom & Harrison, Terry P. & Gupta, Barun, 2010. "Sales and operations planning in systems with order configuration uncertainty," European Journal of Operational Research, Elsevier, vol. 205(3), pages 604-614, September.
  7. Salazar, Juan M. & Diwekar, Urmila & Constantinescu, Emil & Zavala, Victor M., 2013. "Stochastic optimization approach to water management in cooling-constrained power plants," Applied Energy, Elsevier, vol. 112(C), pages 12-22.
  8. Martin Biel & Mikael Johansson, 2022. "Efficient Stochastic Programming in Julia," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 1885-1902, July.
  9. Michael Freimer & Jeffrey Linderoth & Douglas Thomas, 2012. "The impact of sampling methods on bias and variance in stochastic linear programs," Computational Optimization and Applications, Springer, vol. 51(1), pages 51-75, January.
  10. Baker, Erin & Solak, Senay, 2011. "Climate change and optimal energy technology R&D policy," European Journal of Operational Research, Elsevier, vol. 213(2), pages 442-454, September.
  11. Chen-Ritzo, Ching-Hua & Ervolina, Tom & Harrison, Terry P. & Gupta, Barun, 2011. "Component rationing for available-to-promise scheduling in configure-to-order systems," European Journal of Operational Research, Elsevier, vol. 211(1), pages 57-65, May.
  12. Zhang, Hanxiao & Sun, Muxia & Li, Yan-Fu, 2022. "Reliability–redundancy allocation problem in multi-state flow network: Minimal cut-based approximation scheme," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
  13. Kanokporn Kungwalsong & Chen-Yang Cheng & Chumpol Yuangyai & Udom Janjarassuk, 2021. "Two-Stage Stochastic Program for Supply Chain Network Design under Facility Disruptions," Sustainability, MDPI, vol. 13(5), pages 1-19, March.
  14. Soham Ghosh & Sujay Mukhoti, 2023. "Non-parametric generalised newsvendor model," Annals of Operations Research, Springer, vol. 321(1), pages 241-266, February.
  15. Site Wang & Harsha Gangammanavar & Sandra Ekşioğlu & Scott J. Mason, 2020. "Statistical estimation of operating reserve requirements using rolling horizon stochastic optimization," Annals of Operations Research, Springer, vol. 292(1), pages 371-397, September.
  16. Masato Wada & Felipe Delgado & Bernardo K. Pagnoncelli, 2017. "A risk averse approach to the capacity allocation problem in the airline cargo industry," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 643-651, June.
  17. Emelogu, Adindu & Chowdhury, Sudipta & Marufuzzaman, Mohammad & Bian, Linkan & Eksioglu, Burak, 2016. "An enhanced sample average approximation method for stochastic optimization," International Journal of Production Economics, Elsevier, vol. 182(C), pages 230-252.
  18. Walter Rei & Michel Gendreau & Patrick Soriano, 2010. "A Hybrid Monte Carlo Local Branching Algorithm for the Single Vehicle Routing Problem with Stochastic Demands," Transportation Science, INFORMS, vol. 44(1), pages 136-146, February.
  19. Sebastián Arpón & Tito Homem-de-Mello & Bernardo K. Pagnoncelli, 2020. "An ADMM algorithm for two-stage stochastic programming problems," Annals of Operations Research, Springer, vol. 286(1), pages 559-582, March.
  20. Noorizadegan, Mahdi & Chen, Bo, 2018. "Vehicle routing with probabilistic capacity constraints," European Journal of Operational Research, Elsevier, vol. 270(2), pages 544-555.
  21. Michal Kaut & Stein Wallace, 2011. "Shape-based scenario generation using copulas," Computational Management Science, Springer, vol. 8(1), pages 181-199, April.
  22. Daniel Espinoza & Eduardo Moreno, 2014. "A primal-dual aggregation algorithm for minimizing conditional value-at-risk in linear programs," Computational Optimization and Applications, Springer, vol. 59(3), pages 617-638, December.
  23. Yunxiao Deng & Suvrajeet Sen, 2022. "Predictive stochastic programming," Computational Management Science, Springer, vol. 19(1), pages 65-98, January.
  24. Si Chen & Bruce Golden & Richard Wong & Hongsheng Zhong, 2009. "Arc-Routing Models for Small-Package Local Routing," Transportation Science, INFORMS, vol. 43(1), pages 43-55, February.
  25. Zéphyr, Luckny & Lang, Pascal & Lamond, Bernard F. & Côté, Pascal, 2017. "Approximate stochastic dynamic programming for hydroelectric production planning," European Journal of Operational Research, Elsevier, vol. 262(2), pages 586-601.
  26. Alexandre Forel & Martin Grunow, 2023. "Dynamic stochastic lot sizing with forecast evolution in rolling‐horizon planning," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 449-468, February.
  27. Miao Bai & Bjorn Berg & Esra Sisikoglu Sir & Mustafa Y. Sir, 2023. "Partially partitioned templating strategies for outpatient specialty practices," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 301-318, January.
  28. Ankur Kulkarni & Uday Shanbhag, 2012. "Recourse-based stochastic nonlinear programming: properties and Benders-SQP algorithms," Computational Optimization and Applications, Springer, vol. 51(1), pages 77-123, January.
  29. Jangho Park & Rebecca Stockbridge & Güzin Bayraksan, 2021. "Variance reduction for sequential sampling in stochastic programming," Annals of Operations Research, Springer, vol. 300(1), pages 171-204, May.
  30. Wolf, Christian & Koberstein, Achim, 2013. "Dynamic sequencing and cut consolidation for the parallel hybrid-cut nested L-shaped method," European Journal of Operational Research, Elsevier, vol. 230(1), pages 143-156.
  31. Pedro Borges, 2022. "Cut-sharing across trees and efficient sequential sampling for SDDP with uncertainty in the RHS," Computational Optimization and Applications, Springer, vol. 82(3), pages 617-647, July.
  32. Miao Bai & Robert H. Storer & Gregory L. Tonkay, 2022. "Surgery Sequencing Coordination with Recovery Resource Constraints," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 1207-1223, March.
  33. Philpott, A.B. & de Matos, V.L., 2012. "Dynamic sampling algorithms for multi-stage stochastic programs with risk aversion," European Journal of Operational Research, Elsevier, vol. 218(2), pages 470-483.
  34. Rebecca Stockbridge & Güzin Bayraksan, 2016. "Variance reduction in Monte Carlo sampling-based optimality gap estimators for two-stage stochastic linear programming," Computational Optimization and Applications, Springer, vol. 64(2), pages 407-431, June.
  35. Chen, Lijian, 2020. "Determine the cost of denying boarding to passengers: An optimization-based approach," International Journal of Production Economics, Elsevier, vol. 220(C).
  36. Panos Parpas & Berk Ustun & Mort Webster & Quang Kha Tran, 2015. "Importance Sampling in Stochastic Programming: A Markov Chain Monte Carlo Approach," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 358-377, May.
  37. Bernardo K. Pagnoncelli & Felipe del Canto & Arturo Cifuentes, 2021. "The effect of regularization in portfolio selection problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 156-176, April.
  38. Babak Saleck Pay & Yongjia Song, 2020. "Partition-based decomposition algorithms for two-stage Stochastic integer programs with continuous recourse," Annals of Operations Research, Springer, vol. 284(2), pages 583-604, January.
  39. Sanjay Mehrotra & M. Gokhan Ozevin, 2009. "Decomposition Based Interior Point Methods for Two-Stage Stochastic Convex Quadratic Programs with Recourse," Operations Research, INFORMS, vol. 57(4), pages 964-974, August.
  40. Zhanwen Shi & Erbao Cao, 2020. "Contract farming problems and games under yield uncertainty," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 64(4), pages 1210-1238, October.
  41. Shane S. Drew & Tito Homem-de-Mello, 2012. "Some Large Deviations Results for Latin Hypercube Sampling," Methodology and Computing in Applied Probability, Springer, vol. 14(2), pages 203-232, June.
  42. Sina Faridimehr & Saravanan Venkatachalam & Ratna Babu Chinnam, 2021. "Managing access to primary care clinics using scheduling templates," Health Care Management Science, Springer, vol. 24(3), pages 482-498, September.
  43. Jonathan Turner & Kibaek Kim & Sanjay Mehrotra & Debra DaRosa & Mark Daskin & Heron Rodriguez, 2013. "Using optimization models to demonstrate the need for structural changes in training programs for surgical medical residents," Health Care Management Science, Springer, vol. 16(3), pages 217-227, September.
  44. Daniel Ralph & Huifu Xu, 2011. "Convergence of Stationary Points of Sample Average Two-Stage Stochastic Programs: A Generalized Equation Approach," Mathematics of Operations Research, INFORMS, vol. 36(3), pages 568-592, August.
  45. Yu Nie & Xing Wu & Tito Homem-de-Mello, 2012. "Optimal Path Problems with Second-Order Stochastic Dominance Constraints," Networks and Spatial Economics, Springer, vol. 12(4), pages 561-587, December.
  46. Halit Üster & Sung Ook Hwang, 2017. "Closed-Loop Supply Chain Network Design Under Demand and Return Uncertainty," Transportation Science, INFORMS, vol. 51(4), pages 1063-1085, November.
  47. Evren Güney, 2019. "On the optimal solution of budgeted influence maximization problem in social networks," Operational Research, Springer, vol. 19(3), pages 817-831, September.
  48. Guo, Penghui & Zhu, Jianjun, 2023. "Capacity reservation for humanitarian relief: A logic-based Benders decomposition method with subgradient cut," European Journal of Operational Research, Elsevier, vol. 311(3), pages 942-970.
  49. Hu, Junjie & Morais, Hugo & Sousa, Tiago & Lind, Morten, 2016. "Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1207-1226.
  50. Harsha Gangammanavar & Yifan Liu & Suvrajeet Sen, 2021. "Stochastic Decomposition for Two-Stage Stochastic Linear Programs with Random Cost Coefficients," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 51-71, January.
  51. Fei, Xin & Gülpınar, Nalân & Branke, Jürgen, 2019. "Efficient solution selection for two-stage stochastic programs," European Journal of Operational Research, Elsevier, vol. 277(3), pages 918-929.
  52. Cerisola, Santiago & Latorre, Jesus M. & Ramos, Andres, 2012. "Stochastic dual dynamic programming applied to nonconvex hydrothermal models," European Journal of Operational Research, Elsevier, vol. 218(3), pages 687-697.
  53. Löhndorf, Nils, 2016. "An empirical analysis of scenario generation methods for stochastic optimization," European Journal of Operational Research, Elsevier, vol. 255(1), pages 121-132.
  54. Zahra Azadi & Harsha Gangammanavar & Sandra Eksioglu, 2020. "Developing childhood vaccine administration and inventory replenishment policies that minimize open vial wastage," Annals of Operations Research, Springer, vol. 292(1), pages 215-247, September.
  55. Shi, Qingxin & Li, Fangxing & Dong, Jin & Olama, Mohammed & Wang, Xiaofei & Winstead, Chris & Kuruganti, Teja, 2022. "Co-optimization of repairs and dynamic network reconfiguration for improved distribution system resilience," Applied Energy, Elsevier, vol. 318(C).
  56. Arnab Bhattacharya & Jeffrey P. Kharoufeh & Bo Zeng, 2023. "A Nonconvex Regularization Scheme for the Stochastic Dual Dynamic Programming Algorithm," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1161-1178, September.
  57. Suvrajeet Sen & Yifan Liu, 2016. "Mitigating Uncertainty via Compromise Decisions in Two-Stage Stochastic Linear Programming: Variance Reduction," Operations Research, INFORMS, vol. 64(6), pages 1422-1437, December.
  58. Huifu Xu & Dali Zhang, 2013. "Stochastic Nash equilibrium problems: sample average approximation and applications," Computational Optimization and Applications, Springer, vol. 55(3), pages 597-645, July.
  59. Jonathan Turner & Soonhui Lee & Mark Daskin & Tito Homem-de-Mello & Karen Smilowitz, 2012. "Dynamic fleet scheduling with uncertain demand and customer flexibility," Computational Management Science, Springer, vol. 9(4), pages 459-481, November.
  60. Güzin Bayraksan & David P. Morton, 2011. "A Sequential Sampling Procedure for Stochastic Programming," Operations Research, INFORMS, vol. 59(4), pages 898-913, August.
  61. Fabian J. Sting & Arnd Huchzermeier, 2012. "Dual sourcing: Responsive hedging against correlated supply and demand uncertainty," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(1), pages 69-89, February.
  62. Banu Gemici-Ozkan & S. David Wu & Jeffrey T. Linderoth & Jeffry E. Moore, 2010. "OR PRACTICE---R&D Project Portfolio Analysis for the Semiconductor Industry," Operations Research, INFORMS, vol. 58(6), pages 1548-1563, December.
  63. Nesbitt, Peter & Blake, Lewis R. & Lamas, Patricio & Goycoolea, Marcos & Pagnoncelli, Bernardo K. & Newman, Alexandra & Brickey, Andrea, 2021. "Underground mine scheduling under uncertainty," European Journal of Operational Research, Elsevier, vol. 294(1), pages 340-352.
  64. Senay Solak & Gustaf Solveling & John-Paul B. Clarke & Ellis L. Johnson, 2018. "Stochastic Runway Scheduling," Transportation Science, INFORMS, vol. 52(4), pages 917-940, August.
  65. Weskamp, Christoph & Koberstein, Achim & Schwartz, Frank & Suhl, Leena & Voß, Stefan, 2019. "A two-stage stochastic programming approach for identifying optimal postponement strategies in supply chains with uncertain demand," Omega, Elsevier, vol. 83(C), pages 123-138.
  66. Johannes Royset, 2013. "On sample size control in sample average approximations for solving smooth stochastic programs," Computational Optimization and Applications, Springer, vol. 55(2), pages 265-309, June.
  67. Osmani, Atif & Zhang, Jun, 2014. "Optimal grid design and logistic planning for wind and biomass based renewable electricity supply chains under uncertainties," Energy, Elsevier, vol. 70(C), pages 514-528.
  68. Douglas S. Altner & Anthony C. Rojas & Leslie D. Servi, 2018. "A two-stage stochastic program for multi-shift, multi-analyst, workforce optimization with multiple on-call options," Journal of Scheduling, Springer, vol. 21(5), pages 517-531, October.
  69. Halit Üster & Gökhan Memişoğlu, 2018. "Biomass Logistics Network Design Under Price-Based Supply and Yield Uncertainty," Transportation Science, INFORMS, vol. 52(2), pages 474-492, March.
  70. Xiaotie Chen & David L. Woodruff, 2023. "Software for Data-Based Stochastic Programming Using Bootstrap Estimation," INFORMS Journal on Computing, INFORMS, vol. 35(6), pages 1218-1224, November.
  71. Wim van Ackooij & Welington de Oliveira & Yongjia Song, 2018. "Adaptive Partition-Based Level Decomposition Methods for Solving Two-Stage Stochastic Programs with Fixed Recourse," INFORMS Journal on Computing, INFORMS, vol. 30(1), pages 57-70, February.
  72. Matteo Fischetti & Domenico Salvagnin & Arrigo Zanette, 2009. "Fast Approaches to Improve the Robustness of a Railway Timetable," Transportation Science, INFORMS, vol. 43(3), pages 321-335, August.
  73. Michael R. Bussieck & Michael C. Ferris & Alexander Meeraus, 2009. "Grid-Enabled Optimization with GAMS," INFORMS Journal on Computing, INFORMS, vol. 21(3), pages 349-362, August.
  74. Karmel S. Shehadeh & Amy E. M. Cohn & Ruiwei Jiang, 2021. "Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 89-111, February.
  75. Aldasoro, Unai & Escudero, Laureano F. & Merino, María & Pérez, Gloria, 2017. "A parallel Branch-and-Fix Coordination based matheuristic algorithm for solving large sized multistage stochastic mixed 0–1 problems," European Journal of Operational Research, Elsevier, vol. 258(2), pages 590-606.
  76. Fengqi You & Ignacio Grossmann, 2013. "Multicut Benders decomposition algorithm for process supply chain planning under uncertainty," Annals of Operations Research, Springer, vol. 210(1), pages 191-211, November.
  77. Shuang Chen & Li-Ping Pang & Xue-Fei Ma & Dan Li, 2016. "SAA method based on modified Newton method for stochastic variational inequality with second-order cone constraints and application in portfolio optimization," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 84(1), pages 129-154, August.
  78. Wolf, Christian & Fábián, Csaba I. & Koberstein, Achim & Suhl, Leena, 2014. "Applying oracles of on-demand accuracy in two-stage stochastic programming – A computational study," European Journal of Operational Research, Elsevier, vol. 239(2), pages 437-448.
  79. Julia Higle & Lei Zhao, 2012. "Adaptive and nonadaptive approaches to statistically based methods for solving stochastic linear programs: a computational investigation," Computational Optimization and Applications, Springer, vol. 51(2), pages 509-532, March.
  80. Yankai Cao & Carl D. Laird & Victor M. Zavala, 2016. "Clustering-based preconditioning for stochastic programs," Computational Optimization and Applications, Springer, vol. 64(2), pages 379-406, June.
  81. Johannes O. Royset & Roberto Szechtman, 2013. "Optimal Budget Allocation for Sample Average Approximation," Operations Research, INFORMS, vol. 61(3), pages 762-776, June.
  82. Jörn Dunkel & Stefan Weber, 2010. "Stochastic Root Finding and Efficient Estimation of Convex Risk Measures," Operations Research, INFORMS, vol. 58(5), pages 1505-1521, October.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.