IDEAS home Printed from https://ideas.repec.org/r/spr/annopr/v82y1998i0p83-10610.1023-a1018943626786.html
   My bibliography  Save this item

A class of stochastic programs withdecision dependent random elements

Citations

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


Cited by:

  1. Miloš Kopa & Tomáš Rusý, 2021. "A decision-dependent randomness stochastic program for asset–liability management model with a pricing decision," Annals of Operations Research, Springer, vol. 299(1), pages 241-271, April.
  2. Giovanni Pantuso, 2021. "A node formulation for multistage stochastic programs with endogenous uncertainty," Computational Management Science, Springer, vol. 18(3), pages 325-354, July.
  3. Kaut, Michal & Vaagen, Hajnalka & Wallace, Stein W., 2021. "The combined impact of stochastic and correlated activity durations and design uncertainty on project plans," International Journal of Production Economics, Elsevier, vol. 233(C).
  4. Nilay Noyan & Gábor Rudolf & Miguel Lejeune, 2022. "Distributionally Robust Optimization Under a Decision-Dependent Ambiguity Set with Applications to Machine Scheduling and Humanitarian Logistics," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 729-751, March.
  5. Maier, Sebastian & Pflug, Georg C. & Polak, John W., 2020. "Valuing portfolios of interdependent real options under exogenous and endogenous uncertainties," European Journal of Operational Research, Elsevier, vol. 285(1), pages 133-147.
  6. Bora Tarhan & Ignacio Grossmann & Vikas Goel, 2013. "Computational strategies for non-convex multistage MINLP models with decision-dependent uncertainty and gradual uncertainty resolution," Annals of Operations Research, Springer, vol. 203(1), pages 141-166, March.
  7. Wiesemann, Wolfram & Kuhn, Daniel & Rustem, Berç, 2010. "Maximizing the net present value of a project under uncertainty," European Journal of Operational Research, Elsevier, vol. 202(2), pages 356-367, April.
  8. Doan, Xuan Vinh, 2022. "Distributionally robust optimization under endogenous uncertainty with an application in retrofitting planning," European Journal of Operational Research, Elsevier, vol. 300(1), pages 73-84.
  9. Lars Hellemo & Paul I. Barton & Asgeir Tomasgard, 2018. "Decision-dependent probabilities in stochastic programs with recourse," Computational Management Science, Springer, vol. 15(3), pages 369-395, October.
  10. Wolfram Wiesemann & Daniel Kuhn & Berç Rustem, 2012. "Multi-resource allocation in stochastic project scheduling," Annals of Operations Research, Springer, vol. 193(1), pages 193-220, March.
  11. Kim, Nayeon & Montreuil, Benoit & Klibi, Walid, 2022. "Inventory availability commitment under uncertainty in a dropshipping supply chain," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1155-1174.
  12. Tito Homem-de-Mello & Qingxia Kong & Rodrigo Godoy-Barba, 2022. "A Simulation Optimization Approach for the Appointment Scheduling Problem with Decision-Dependent Uncertainties," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2845-2865, September.
  13. Eyyüb Y. Kıbış & İ. Esra Büyüktahtakın & Robert G. Haight & Najmaddin Akhundov & Kathleen Knight & Charles E. Flower, 2021. "A Multistage Stochastic Programming Approach to the Optimal Surveillance and Control of the Emerald Ash Borer in Cities," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 808-834, May.
  14. Tahir Ekin & Nicholas G. Polson & Refik Soyer, 2014. "Augmented Markov Chain Monte Carlo Simulation for Two-Stage Stochastic Programs with Recourse," Decision Analysis, INFORMS, vol. 11(4), pages 250-264, December.
  15. Anchugina, Nina & Ryan, Matthew & Slinko, Arkadii, 2019. "Mixing discount functions: Implications for collective time preferences," Mathematical Social Sciences, Elsevier, vol. 102(C), pages 1-14.
  16. Flam, S. D. & Jourani, A., 2003. "Strategic behavior and partial cost sharing," Games and Economic Behavior, Elsevier, vol. 43(1), pages 44-56, April.
  17. Tahir Ekin & Nicholas G. Polson & Refik Soyer, 2017. "Augmented nested sampling for stochastic programs with recourse and endogenous uncertainty," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(8), pages 613-627, December.
  18. Kavitha G. Menon & Ricardo Fukasawa & Luis A. Ricardez-Sandoval, 2021. "A novel stochastic programming approach for scheduling of batch processes with decision dependent time of uncertainty realization," Annals of Operations Research, Springer, vol. 305(1), pages 163-190, October.
  19. Mizera, Ivan & Volauf, Milos, 2002. "Continuity of Halfspace Depth Contours and Maximum Depth Estimators: Diagnostics of Depth-Related Methods," Journal of Multivariate Analysis, Elsevier, vol. 83(2), pages 365-388, November.
  20. 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.
  21. Fragnière, Emmanuel & Gondzio, Jacek & Yang, Xi, 2010. "Operations risk management by optimally planning the qualified workforce capacity," European Journal of Operational Research, Elsevier, vol. 202(2), pages 518-527, April.
  22. Marco Silva & João Pedro Pedroso, 2022. "Deep Reinforcement Learning for Crowdshipping Last-Mile Delivery with Endogenous Uncertainty," Mathematics, MDPI, vol. 10(20), pages 1-23, October.
  23. Feng, Wei & Feng, Yiping & Zhang, Qi, 2021. "Multistage robust mixed-integer optimization under endogenous uncertainty," European Journal of Operational Research, Elsevier, vol. 294(2), pages 460-475.
  24. Ekin, Tahir, 2018. "Integrated maintenance and production planning with endogenous uncertain yield," Reliability Engineering and System Safety, Elsevier, vol. 179(C), pages 52-61.
  25. F. Hooshmand & S. A. MirHassani, 2018. "Reduction of nonanticipativity constraints in multistage stochastic programming problems with endogenous and exogenous uncertainty," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 87(1), pages 1-18, February.
  26. Kopa, Miloš & Rusý, Tomáš, 2023. "Robustness of stochastic programs with endogenous randomness via contamination," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1259-1272.
  27. Colvin, Matthew & Maravelias, Christos T., 2010. "Modeling methods and a branch and cut algorithm for pharmaceutical clinical trial planning using stochastic programming," European Journal of Operational Research, Elsevier, vol. 203(1), pages 205-215, May.
  28. Solak, Senay & Clarke, John-Paul B. & Johnson, Ellis L. & Barnes, Earl R., 2010. "Optimization of R&D project portfolios under endogenous uncertainty," European Journal of Operational Research, Elsevier, vol. 207(1), pages 420-433, November.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.